Key statistical features and common formation mechanisms of mesoscale vortices over Tibetan Plateau: A 42-warm-season analysis based on ERA5 data

Key statistical features and common formation mechanisms of mesoscale vortices over Tibetan Plateau: A 42-warm-season analysis based on ERA5 data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Key statistical features and common formation mechanisms of mesoscale vortices over Tibetan Plateau: A 42-warm-season analysis based on ERA5 data Huan Tang, Shen-Ming Fu, Chaoying Yang, Jianhua Sun, Wanli Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6436848/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract During warm seasons, the Tibetan Plateau serves as a key source region for mesoscale vortices, which significantly influence weather patterns over and around the plateau. Despite extensive research on Tibetan Plateau mesoscale vortices (TMVs), no studies have examined statistical characteristics of shorter-lived TMVs (< 6 hr), and no studies have ever shown the TMVs’ vertical-extent features, common formation mechanisms, and hourly diurnal variations. To address these knowledge gaps, we conduct a statistical analysis on the TMVs over 42 warm seasons (from 1979 to 2020) by using the hourly ERA5 data. The findings reveal that TMV formation is mainly terrain-dependent, with higher frequencies occurring in regions of greater altitude. Over 68% of TMVs exhibit a lifespan of < 6 hr, and more than 75% of TMVs have a vertical extent of ≤ 50 hPa. Precipitation associated with TMVs accounts for ~ 13.8% of the total accumulated precipitation over the plateau, contributing up to ~ 36% in certain regions of the northwestern and central plateau. TMVs exhibit a diurnal peak in occurrence frequency around 22:00 local solar time. TMV formation is mainly driven by vertical stretching due to low-level convergence. TMVs are more likely to form under environmental conditions characterized by warmer surface temperatures, higher convective available potential energy, heavier precipitation, stronger upper-tropospheric divergence, and greater mid-tropospheric cyclonic vorticity. A strong steering flow, an intense vortex intensity, a large vertical extent, and a rapid enhancement in the upper-level cyclonic-vorticity are crucial for the TMVs’ moving out from the Tibetan Plateau. Tibetan Plateau Mesoscale vortices Statistical features Composite features Vorticity budget Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 1 Introduction According to American Meteorological Society ( https://glossary.ametsoc.org/wiki/Vortex ), a vortex usually refers to a compact flow that circulates around an axis, characterized by a local extremum in vorticity (only cyclonic vorticity is considered in this study). Vortices are common weather systems and can occur globally (Neu et al. 2013; Fu et al. 2020). Vortices with a horizontal scale of 2–2000 km are typically classified as mesoscale vortices (Orlanski 1975; Fu et al. 2020). For decades, mesoscale vortices have been a key focus of research owing to their close association with various types of hazardous weather. These include heavy rainfall (Bartels and Maddo 1991; Trier and Davis 2002), hail (Tessendorf et al. 2005), lightning (Bovalo et al. 2014; Fierro and Mansell 2018), strong winds (Evans et al. 2014; Grunzke et al. 2017), dust storms (Qian et al. 2002; Huang et al. 2016), and blizzards (Zhang et al. 2012). The Tibetan Plateau, often referred to as the “Third Pole” and the “Asian Water Tower” (Zhao et al. 2018), is the highest plateau in the world and significantly influences regional weather and climate (Yeh and Gao 1979; Yanai et al. 1992; Duan and Wu 2005; Duan et al. 2018; Liu et al. 2020; Wu et al. 2020). During warm seasons, the Tibetan Plateau serves as a large heat source (Yeh and Gao 1979; Li et al. 2014a; Jiang et al.2016; Mai et al. 2021), which creates favorable conditions for the formation of mesoscale vortices (Li et al. 2014b; Fu et al. 2019, 2021a). Notably, the Tibetan Plateau mesoscale vortices (TMVs) are particularly significant, with the Tibetan Plateau vortices being the most recognized (Feng et al. 2014). Tibetan Plateau vortices typically have a lifespan of ≥ 6 hr (Li et al. 2014c; Curio et al. 2019; Fu et al. 2019; Lin et al. 2020) and significantly contribute to the total precipitation over the plateau (Fu et al. 2021b; Lin et al. 2021). Previous studies (Tao and Ding 1981; Curio et al. 2019; Li et al. 2014a, b; Chen et al. 2019; Lin et al. 2020; Li et al.2020b) have shown that the effects of Tibetan Plateau vortices extend beyond the plateau. Under favorable conditions, some of these vortices can exit the plateau and trigger heavy rainfall in downstream regions. Notably, the catastrophic flood events in the Yangtze River Basin in 1998 and the Huang-Huai River Basin in 2003 were closely associated with Tibetan Plateau vortices (Tan et al. 2013; Fu et al. 2021b). For decades, numerous studies have investigated TMVs, with a significant focus on Tibetan Plateau vortices. These studies have mainly explored four key aspects. First, Lu et al. (1984) examined the structural features of Tibetan Plateau vortices and found that some vortices exhibited a symmetric warm core, with a cold core near the surface. Qiao et al. (1994) analyzed satellite cloud imagery and found that Tibetan Plateau vortices displayed a spiral cloud pattern similar to that of tropical cyclones. Additionally, Li et al. (2019) and Fu et al. (2019) demonstrated that Tibetan Plateau vortices exhibited upper-level divergence and lower-level convergence. Second, Li et al. (2011), Li et al. (2014a), and Lin et al. (2023, 2024) investigated the formation and development mechanisms of Tibetan Plateau vortices. The findings revealed that convergence, wind shear, latent heat, and sensible heat were crucial factors in the formation of Tibetan Plateau vortices. Tang et al. (2023a) further highlighted that in warmer and wetter environments, latent heat often plays a more dominant role in the formation of these vortices. Qian et al. (1984), Shen et al. (1986), Dell'Osso and Chen (1986), Wang (1987), Li et al. (2014a), Zhang et al. (2019a), Ma et al. (2022), and Wu et al. (2022) and Dong et al. (2024) compared the relative importance of latent heat and sensible heat in Tibetan Plateau vortex formation. The findings indicated that the significance of these factors varies based on the specific scenario. Third, Yu et al. (2007), Yu et al. (2009), Li et al. (2011), Li et al. (2014a), Fu et al. (2019), and Ma et al. (2022) assessed the mechanisms driving the eastward displacement of vortices. The results indicated that the favorable conditions for the eastward movement of Tibetan Plateau vortices included a strong upper-level jet, an eastward-extended strong South Asian High, a westward-extended strong western Pacific subtropical high, and abundant moisture transport. Fourth, Lin (2015), Huang et al. (2018), Curio et al. (2019), and Lin et al. (2020) evaluated the statistical characteristics of Tibetan Plateau vortices and found that their annual mean occurrence frequency ranged from 53 to 269. Wang et al. (2009) observed significant interdecadal, interannual, and seasonal variations in the frequency of these vortices. Yu and Gao (2006) indicated that Tibetan Plateau vortices forming east of 92°E had a higher probability of exiting the plateau. Previous studies have elucidated various key features of TMVs. However, several significant limitations persist: (i) No statistical studies have examined the shorter-lived (< 6 hr) TMVs; (ii) the vertical extent and formation mechanisms of different TMV types remain underexplored; (iii) the peak in the hourly diurnal variation of TMV occurrence frequency has not been identified, and the underlying mechanisms remain unclear. Addressing these knowledge gaps will provide comprehensive insights into precipitation and mesoscale weather systems over and around the Tibetan Plateau. To date, no studies have utilized hourly ERA5 reanalysis data (Hersbach et al. 2020) to investigate the long-term statistical characteristics of TMVs. Nevertheless, ERA5 provides the highest spatiotemporal resolution (i.e., hourly/0.25°) and effectively describes the atmosphere over and around the Tibetan Plateau (Zhang et al. 2019b; Huang et al. 2021; Xin et al. 2022; Lin et al. 2023). Iin view of this, this study aims to address these knowledge gaps by using hourly ERA5 data. The paper is structured as follows: Section 2 describes the data and methodology. Section 3 examines the basic climatological features of TMVs. Section 4 explores the vertical extents, tracks, and precipitation associated with TMVs. Section 5 highlights the composite features. Section 6 concludes the study and discusses its findings. 2 Data and methods 2.1 Data This study utilizes hourly ERA5 reanalysis data with a spatial resolution of 0.25° × 0.25° (Hersbach et al. 2020) for 42 warm seasons (May–September; 1979–2020) to identify and track TMVs. The data are also used to analyze the background circulations, three-dimensional structures, and formation mechanisms of TMVs. The spatial resolution of the dataset used to identify mesoscale vortices is crucial. High-resolution datasets provide a significant advantage over low-resolution datasets, as they can effectively capture the vortex structure (cf. Figure 1 a and 1 b). This study utilizes a total of 10 variables: zonal wind, meridional wind, vertical velocity, geopotential height, temperature, specific humidity, vorticity, divergence at 37 pressure levels, total precipitation, and 2-m surface temperature. The IMERG final precipitation L3 product, generated by the latest version of the Global Precipitation Measurement (GPM) Program (Huffman et al. 2019), provides a half-hourly resolution of 0.1° × 0.1°. This product is used to analyze precipitation associated with TMVs. The GPM data are aggregated within each hour to generate hourly precipitation, ensuring consistency with the temporal resolution of the ERA5 data. Because the GPM data do not cover the entire study period, precipitation analysis is based on data from 20 warm seasons (2001 to 2020). Although GPM may underestimate light rain amounts and the intensity of extremely heavy rainfall, it accurately captures key precipitation features over the Tibetan Plateau, with a false alarm ratio of ~ 14% and a missing data ratio of ~ 13% (Ma et al. 2016; Xu et al. 2017; Zhang et al. 2018b). Moreover, a comparison of the data from the GPM program and Tropical Rainfall Measuring Mission (3B42 V7) indicates that GPM data provide more accurate precipitation patterns over the Tibetan Plateau (Ma et al. 2016; Zhang et al. 2018b). 2.2 Detection and tracking algorithm We detect TMVs using the following procedure: (i) Calculate the restricted vorticity[1] (Fu et al.2020) and regard those ≥ 10 − 5 s -1 as candidate vortex centers; (ii) The quadrant-averaged wind is calculated using a 100 km radius based on these candidate vortex centers. Only the vortex centers with a cyclonic quadrant-averaged wind are retained as valid vortex structures. To track TMVs horizontally, we use the neighborhood searching method (Blender et al. 1997). If the distance between the centers of the valid vortex structures at times t and t + 1 (where “1” denotes 1 hr) is ≤ 300 km, they are considered part of the same TMV. Otherwise, the tracking is terminated. Higher temporal resolution data are more effective in capturing the continuous evolution of a vortex. For example, Vortex I dissipates at t + ∆ t (where ∆ t denotes the temporal resolution), and Vortex II forms near Vortex I (Fig. 1 d). Higher temporal resolution data can detect the dissipation of Vortex I, while lower temporal resolution data cannot (Fig. 1 c). This misjudgment increases the likelihood of identifying Vortex II for Vortex I, leading to an incorrect extension of Vortex I lifespan. The vertical extent of TMVs is determined using the following procedure: (i) At 500 hPa, we examine both higher and lower pressure levels in increments of 50 hPa. If the horizontal distance between the vortex centers at two adjacent levels is ≤ 300 km, they are considered part of the same TMV; (ii) We analyze all continuous vertical levels with vortex structures related to the same TMV to determine the vertical extent of TMVs (Fu et al. 2016, 2022). Tang et al. (2023b) detected and tracked TMVs and then manually adjusted each TMV to improve result accuracy. After these corrections, a total of 15,644 TMVs were identified across the 42 warm seasons. Approximately 14% of TMVs identified and tracked by the algorithm did not match those manually identified. Further details on the algorithm are provided in Tang et al. (2023b). 2.3 Parameters and classifications of vortices In this study, we mainly use the following vortex parameters: (i) Formation/dissipation indicates the first/last time a TMV is detected; (ii) lifespan signifies the period between the formation and dissipation of TMVs; (iii) occurrence frequency denotes the total number of TMVs; (iv) influence frequency indicates the total number of instances in which TMVs are detectable; (v) location represents the center of TMVs; (vi) moving distance denotes the distance between the formation and dissipation locations of TMVs; (vii) top/bottom levels represent the minimum/maximum pressure levels within the TMV vertical extent; (viii) thickness indicates the difference between the top and bottom levels of TMVs; (ix) central region is defined as a 2° × 2° box centered on the TMV location; (x) intensity is evaluated as the maximum vorticity within the TMV central region over its lifespan; (xi) TMV-related precipitation is defined as precipitation (≥ 0.1 mm hr⁻ 1 ) within a 300 km radius of the vortex center (Fu et al. 2021a). All TMVs analyzed in this study have a minimum lifespan of 1 hr and are detectable from at least two consecutive hourly ERA5 data points. This approach differs from those used in previous studies for detecting/tracking Tibetan Plateau vortices (Table 1 ). By utilizing hourly ERA5 data and setting a minimum lifespan threshold of 1 hr, we can identify TMVs with lifespans shorter than 6 hr, which would be undetectable using reanalysis datasets with a temporal interval of ≥ 6 hr. Therefore, TMVs are classified into two groups based on their lifespans: shorter-lived (lifespan < 6 hr) and longer-lived TMVs (lifespan ≥ 6 hr). Out of the total 15,644 TMVs, 10,691 are classified as shorter-lived, while 4,953 are classified as longer-lived (Fig. 2 ). Among the longer-lived TMVs, 1,898 are categorized as moving type, as the distance between their formation and dissipation locations is ≥ 200 km. The remaining TMVs are classified as quasi-stationary. The moving type TMVs are further classified into vacating type (54 TMVs) and non-vacating type (1,844 TMVs), based on whether the vortex dissipates outside the Tibetan Plateau. Table 1 Parameters for detecting and tracking Tibetan Plateau vortices in different studies. “Auto” denotes the use of identification algorithms. \(\:\zeta\:\) , Z , and W represent relative vorticity, geopotential height, and horizontal wind at 500 hPa, respectively. “/” indicates that no parameter is used. Reference Method Data (Variable) Temporal (spatial) resolution Minimum lifespan (Radius) Study period Annual mean Proportion of vacating Trend Wang et al. (2009) Manual Weather charts ( Z ; W ) 12 hr (/) 12 hr (/) 1980–2004 (May-Sep) 68 10% Decrease Li (2012) Manual Weather charts ( Z ; W ) 12 hr (/) 12 hr (/) 1980–2000 (except1982) (May-Sep) 77 / Decrease Tang et al. (2014) Manual Yearbooks ( Z ; W ) 12 hr (/) / (/) 1998–2011 (Jan-Dec) 40 23.5% Increase Li et al. (2014c) Manual CFSR ( Z ; W ) 6 hr (2.5°) 6 hr (/) 1981–2010 (Jun-Aug) 32 / Increase Feng et al. (2014) Auto CFSR ( \(\:\zeta\:\) ) 6 hr (0.5°) 3 hr (100 km) 2000–2009 (April-Oct) 103 8.5% / Lin (2015) Auto ERA-interim ( Z ) 6 hr (1°) 18 hr (200 km) 1979–2013 (Jan-Dec) 53 12.0% Decrease (2/10a) Huang et al. (2018) Auto ERA-interim ( Z ; W ) 6 hr (0.5°) 18 hr (200 km) 1979–2016 (Jan-Dec) 56 / Increase (3/10a) Zhang et al. (2018a) Auto CFSR ( Z ; W ) 6 hr r (0.5°) 12 hr (200 km) 1981–2010 (Jan-Dec) 60 / Decrease (4/10a) Guan and Li (2019) Auto CFSR ( Z ; W ) 6 hr (0.5°) 12 hr (200 km) 1979–2016 (Jan-Dec) 71 / Increase (3/10a) Curio et al. (2019) Auto ERA-interim ( \(\:\zeta\:\) ) 6 hr (1°) 24 hr (200 km) 1979–2015 (Jan-Dec) 154 20.0% / CFSR ( \(\:\zeta\:\) ) 6 hr (0.5°) 269 20.0% / Li et al. (2020a) Auto FNL ( Z ; W ) 6 hr (1°) / (/) 2000–2015 / / / / ERA-interim ( Z ; W ) 6 hr (0.7°) Lin et al. (2020) Auto ERA-interim ( Z ) 6 hr (1°) 18 hr (145 km) 1979–2017 (Jan-Dec) 63.5 12.9% / ERA40 ( Z ) 6 hr (1°) 18 hr (140 km) 1958–2001 (Jan-Dec) 63.5 11.5% / JRA55 ( Z ) 6 hr (1.25°) 18 hr (60 km) 1958–2017 (Jan-Dec) 63.5 12.6% / CFSR ( Z ) 6 hr (0.5°) 18 hr (170 km) 1979–2017 (Jan-Dec) 63.5 13.9% / MERRA2 ( Z ) 6 hr (0.5°) 18 hr (155 km) 1980–2017 (Jan-Dec) 63.5 14.3% / This study Auto ERA5 ( \(\:\zeta\:\) ) 1 hr (0.25°) 1 hr (100 km) 1979–2020 (Jan-Dec) 118 (≥ 6 hr) 0.35% Increase (10/10a) 2.4 Composite methods This study utilizes two composite methods: Eulerian and Lagrangian composites. The Eulerian composite is used to compute the arithmetic mean of meteorological fields in their original coordinates and analyze the larger-scale background circulations of TMVs. The Lagrangian composite is used to calculate the arithmetic mean of meteorological fields relative to the TMV centers. In this method, the centers of all TMVs are overlapped and serve as the origin of the coordinate system. The Lagrangian composite is mainly used to investigate the three-dimensional structures and common formation mechanisms of TMVs. 2.5 Vorticity budget The vorticity budget in the pressure coordinate system (Kirk 2003; Fu et al. 2017) is used to investigate the formation mechanisms of different types of TMVs. The expression is as follows: \(\:\frac{\partial\:\zeta\:}{\partial\:t}=-{\mathbf{V}}_{\text{h}}\cdot\:{\nabla\:}_{\text{h}}\zeta\:-\beta\:v-\omega\:\frac{\partial\:\zeta\:}{\partial\:p}+\mathbf{k}\cdot\:\left(\frac{\partial\:{\mathbf{V}}_{\text{h}}}{\partial\:p}\times\:{\nabla\:}_{\text{h}}\omega\:\right)-\left(\zeta\:+f\right){\nabla\:}_{\text{h}}\cdot\:{\mathbf{V}}_{\text{h}}+\) residual effect (1), where \(\:\zeta\:\) denotes the relative vorticity (hereinafter referred to as vorticity); t signifies time; ( i , j , k ) represent the unit vectors in the zonal, meridional, and vertical directions, respectively; \(\:{\mathbf{V}}_{\text{h}}=u\mathbf{i}+v\mathbf{j}\) indicates the horizontal wind vector, with the subscript “h” representing the horizontal component; \(\:{\nabla\:}_{\text{h}}=\frac{\partial\:}{\partial\:x}\mathbf{i}+\frac{\partial\:}{\partial\:y}\mathbf{j}\) ; \(\:\beta\:=\frac{\partial\:f}{\partial\:y}\) , where f denotes the Coriolis parameter; ω denotes vertical velocity in the pressure coordinate system; p represents pressure. The term \(\:\frac{\partial\:\zeta\:}{\partial\:t}\) represents the local temporal derivative of vorticity; \(\:-{\mathbf{V}}_{\text{h}}\cdot\:{\nabla\:}_{\text{h}}\zeta\:\) denotes the horizontal advection of vorticity; \(\:-\beta\:v\) indicates the advection of planetary vorticity, which is typically one order of magnitude smaller than the other terms (Fu et al. 2013); \(\:-\omega\:\frac{\partial\:\zeta\:}{\partial\:p}\) represents the vertical advection of vorticity; \(\:\mathbf{k}\cdot\:\left(\frac{\partial\:{\mathbf{V}}_{\text{h}}}{\partial\:p}\times\:{\nabla\:}_{\text{h}}\omega\:\right)\) corresponds to the tilting effect, which converts horizontal vorticity into vertical vorticity; \(\:-\left(\zeta\:+f\right){\nabla\:}_{\text{h}}\cdot\:{\mathbf{V}}_{\text{h}}\) indicates the stretching effect. Finally, the residual effect represents the combined effects of friction, subgrid processes, and calculation errors. We define the overall effect as the sum of all terms on the right-hand side of Eq. (1), excluding the residual effect. 3 Basic climatological characteristics 3.1 Spatial distribution A total of 15,644 TMVs are identified over the 42 warm seasons, with an average occurrence frequency of 372 per warm season. The influence frequency of TMVs reaches 99,090, accounting for nearly half of the total duration of the warm season on average. TMVs can form in nearly any region of the Tibetan Plateau, with over 80% occurring west of 95°E (Fig. 3 b). The highest occurrence frequency is observed between the Kunlun and Gangdise Mountains (Fig. 3 b). Additionally, the western section of the Qaidam Basin and the areas north of the Tanggula Mountains exhibit high occurrence frequencies. To quantify the spatial distribution of TMVs, we calculate the zonally and meridionally accumulated occurrence frequencies at 1° intervals. A peak in the zonally accumulated occurrence frequency occurs within the band of 33–34°N, mainly owing to TMVs over the western section of the Tibetan Plateau (Fig. 3 b and 3 c). For the meridionally accumulated occurrence frequency, a peak is observed around 89°E, with three secondary peaks around 81°E, 84°E, and 98°E. The correlation between the zonally/meridionally accumulated occurrence frequency and the zonally/meridionally averaged terrain height is ~ 0.84/0.53 (exceeding the 99% confidence level), indicating that TMV formation is highly terrain-dependent (Fig. 3 c and 3 d). A comparison between Fig. 3 b and 3 e indicates that the overall distribution of influence frequency is similar to that of occurrence frequency, as the moving type accounts for only ~ 12.1% of all TMVs. Because some TMVs can exit the Tibetan Plateau, surrounding areas outside the plateau—particularly the northeastern, eastern, and southeastern sections—are affected by TMVs (Fig. 3 e). 3.2 Temporal variations 3.2.1 Annual features Occurrence frequencies of all TMVs, longer-lived TMVs, quasi-stationary type, and moving types exhibit significant annual variations (Fig. 4 ), with a 42-year mean occurrence frequency of ~ 372, ~118, ~ 73, and ~ 45, respectively. All TMVs exhibit the highest occurrence frequency in 1987 and 1999 (422), while the longer-lived TMVs exhibits maximum occurrence frequency in 2018 (174). The quasi-stationary TMVs achieves peak occurrence frequency in 2018 (118). The moving TMVs exhibits peak occurrence in 1999, 2002, 2008, and 2016 (61). All TMVs, longer-lived TMVs, quasi-stationary type, and moving type exhibit the lowest occurrence frequencies in 1984 (290), 1983 (73), 1983 (39), and 1981 (29), respectively. The Mann–Kendall trend test (Wei, 2007) reveals significantly increasing trends for longer-lived, quasi-stationary, and moving TMVs (exceeding the 99% confidence level). However, no significant linear trend is observed for all TMVs, as the shorter-lived TMVs do not exhibit a significant trend (not shown). The longer-lived TMVs exhibit the highest increase in occurrence frequency (~ 1 TMV per warm season, Fig. 4 a), while the moving TMVs exhibit the slowest increase in occurrence frequency (~ 0.3 TMV per warm season, Fig. 4 b). The occurrence frequency of the quasi-stationary and moving TMVs exhibits a significant positive correlation with the annual mean surface temperature of the Tibetan Plateau (exceeding the 95% confidence level). This indicates that the warming of the plateau (~ 0.4°C per decade, Fig. 4 a) is closely related to the increase in these TMVs. 3.2.2 Monthly features The occurrence frequency of TMVs exhibits significant monthly variations (Fig. 5 a). For all TMVs, including the quasi-stationary and moving types, the occurrence frequency increases from May to June, peaks in July, and then decreases from July to September, with the lowest frequency occurring in September. These monthly variations in TMV occurrence are closely related to changes in the stream field over the Tibetan Plateau. In June, July, and August, convergence lines form and persist in the western section of the Tibetan Plateau (Fig. 5 c–e), which promotes TMV formation through convergence-related cyclonic vorticity production (Fu et al. 2019). Consequently, more TMVs form near these convergence lines than in May and September. In contrast, the stream fields over the eastern section of the Tibetan Plateau and the mean formation locations of TMVs do not exhibit significant monthly variations (Fig. 5 b–f). 3.2.3 Diurnal variations We use hourly ERA5 data to investigate the hourly diurnal variation features of TMVs, which have rarely been addressed in previous studies. For longer-lived TMVs, both quasi-stationary and moving types, vortices form between 14:00 LST of the previous day and 03:00 LST (i.e., from afternoon to early morning), which account for ≥ 60% of occurrences in each category (Fig. 6 a). Owing to the significantly higher occurrence frequency of the quasi-stationary type compared with the moving type, the diurnal variation features of the longer-lived TMVs are similar to those of the quasi-stationary type. The maximum occurrence frequencies for longer-lived TMVs and the quasi-stationary type occur around 21:30–22:30 LST, while the peak occurrence for the moving TMVs occurs around 18:30–19:30 LST. To explore the potential mechanisms driving these diurnal variations, we investigate factors such as 2-m temperature, total precipitation (related to latent heating), surface sensible heat, surface latent heat, convective available potential energy (CAPE), vertical velocity, and 200-hPa divergence. These factors can influence TMV formation through thermodynamic and/or dynamical forcings (Fu et al. 2019, 2021b). The diurnal variations of 2-m temperature and total precipitation, calculated at the formation times of the quasi-stationary and moving TMVs, exhibit similar patterns to those observed throughout the entire study period (Fig. 6 b and 6 d) but with greater intensity. This indicates that TMVs tend to form in environments characterized by warmer surface temperatures and higher precipitation. Generally, the 2-m temperature reaches its peak between 14:00 and 16:00 LST (Fig. 6 b), which destabilizes the boundary layer. Moreover, CAPE reaches its peak between 12:30 and 15:30 LST (Fig. 6 c). The combination of warmer surface temperatures, an unstable boundary layer, and higher CAPE promotes convection and rainfall. Consequently, total precipitation reaches its peak between 14:00 and 18:00 LST (Fig. 6 d), consistent with the maximum upward motions (Fig. 6 c). These motions are mainly driven by latent heating from precipitation. Stronger upward motions are associated with more intense low-level convergence owing to fluid continuity. This convergence generates cyclonic vorticity through vertical stretching, which causes the peak occurrence frequencies of the moving and quasi-stationary TMVs to occur 4–7 hr later (Fig. 6 a). The diurnal variations of 200-hPa divergence for the moving and quasi-stationary TMVs exhibit similar patterns to those over the Tibetan Plateau but with significantly higher intensity (Fig. 6 c). This suggests that TMVs typically form in environments with strong upper-tropospheric divergence, which aids in sustaining or enhancing upward motions (Fu et al. 2021b). The 200-hPa divergence reaches its peak between 16:30 and 17:30 LST, ~ 1 hr after the peak of total precipitation and 2–5 hr before the occurrence frequency peaks for the moving and quasi-stationary TMVs. The diurnal variation of 500-hPa vorticity (Fig. 6 a) for the moving and quasi-stationary TMVs exhibits similar patterns but significantly differs from the climate mean. Key points are as follows: (i) Both the moving and quasi-stationary TMVs exhibit two peaks (22:30–23:30 LST for the former and 00:30–01:30 LST for the latter). However, the climate mean displays only one peak (20:30–21:30 LST); (ii) the cyclonic vorticity for the moving and quasi-stationary TMVs is significantly larger than the climate mean. Overall, the peak times of occurrence frequencies for the moving and quasi-stationary TMVs are consistent with the peak time of the climate mean 500-hPa vorticity (Fig. 6 a). This suggests that the diurnal variation of dynamical features over the Tibetan Plateau is crucial for TMV formation. 3.3 Features related to lifespans Among the 15,644 TMVs detected over the 42 warm seasons, ~ 90% have a lifespan of less than 12 hr (Fig. 7 a), with only ~ 2% having a lifespan of 24 hr or more. Shorter-lived TMVs, which are difficult to detect using reanalysis data with a 6-hr or longer temporal interval, comprise up to 68.2% of all TMVs. This contributes to the higher occurrence frequency of TMVs detected in this study compared with previous studies (Table 1 ). Overall, TMVs occurrence frequencies decrease rapidly as their lifespans increase, with the mean and median lifespans of ~ 5.3 and 3.1 hr, respectively (left column of Fig. 7 b). The lifespans of TMVs exhibit a strong positive correlation with their moving distances, with a correlation coefficient of ~ 0.8, which exceeds the 99.9% confidence level (Fig. 7 c). This indicates that TMVs with longer lifespans tend to travel greater distances. Most TMVs exhibit quasi-stationary behavior, with an average moving distance of ~ 80 km, and over 75% of TMVs travel less than 100 km. Although TMVs with longer lifespans or greater moving distances comprise only a small proportion of all TMVs, they are often associated with more severe disasters compared with typical TMVs (Fu et al. 2019, 2021b). Similarly, the intensities of TMVs exhibit a significant positive correlation with their lifespans, with a correlation coefficient of ~ 0.4, which exceeds the 99.9% confidence level (Fig. 7 d). This indicates that TMVs with longer lifespans exhibit greater intensities. TMVs feature mean and median intensities of ~ 2.1 × 10 − 4 and ~ 1.9 × 10 − 4 s − 1 , respectively (Fig. 7 b). The lifespans of the three TMV types vary significantly (Fig. 7 e). On average, the vacating TMVs exhibit the longest lifespan (~ 26.7 hr), followed by the non-vacating TMVs (~ 15.4 hr). However, the quasi-stationary TMVs have the shortest lifespan (~ 8.7 hr). The longest lifespan observed among the non-vacating TMVs is ~ 108 hr, which is the maximum recorded across all TMV types. Nearly all quasi-stationary TMVs (~ 99.5%) and most non-vacating TMVs (~ 85.5%) have lifespans of less than 24 hr. In contrast, ~ 47.0% of vacating TMVs have a longer lifespan of 24 hr or more (Fig. 7 e). 4 Characteristics of vertical extents, tracks, and precipitation 4.1 Vertical extent features The vertical extent is a crucial characteristic of TMVs but has been rarely investigated in previous studies. For all TMVs, over 60% of them have a highest top-level (i.e., the highest top-level during a TMV’s entire lifespan) around 500 hPa (Fig. 8 a), whereas, those TMVs with a highest top-level higher than 400 hPa account for < 5%. Overall, the number of TMVs significantly decreases as their highest top levels become higher. A comparison between shorter-lived TMVs and other types reveals that the former have a larger proportion for the vortices with a highest top-level around 500 hPa (Fig. 8 a). This indicates that TMVs with longer lifespans are more likely to have higher top levels. Given that the Tibetan Plateau has a mean altitude of ~ 4000 m, the surface pressure typically falls within the 500–550 hPa range. Because most TMVs remain confined to the Tibetan Plateau (Fig. 2 ), the lowest bottom-levels (i.e., the lowest bottom-level during a TMV’s entire lifespan) of > 99% of the TMVs are also within this range (Fig. 8 b). Only the vacating TMVs exhibit bottom levels extending downward to 600 hPa or lower. Comparisons between shorter-lived TMVs and other types reveal that shorter-lived TMVs contain fewer vortices with a lowest bottom -level around 550 hPa (Fig. 8 b). Among all TMVs, over 40% exhibit a maximum thickness of ~ 50 hPa, making this the most common category (Fig. 8 c), while only 32.7% feature a maximum thickness of less than 50 hPa. This indicates most TMVs belong to the shallow (in vertical extent) type of mesoscale vortex. TMVs with a thickness of 100 hPa or more comprise ~ 23.7% of all TMVs, while TMVs with a thickness of 200 hPa or greater account for only ~ 3.6%. A comparison between shorter-lived TMVs and other types indicates that the shorter-lived TMVs contain fewer vortices with thicknesses exceeding 50 hPa. This suggests that TMVs with longer lifespans tend to have greater vertical thickness, as their top altitudes are higher and bottom altitudes are lower. 4.2 Track features The impacts of the quasi-stationary and moving TMVs are mainly concentrated over the Tibetan Plateau (Fig. 9 a–b). For the quasi-stationary TMVs, regions with high influence frequency are mainly located in the western, central, and eastern sections of the plateau (Fig. 9 a). In contrast, the moving TMVs exhibit high influence frequencies within a zonally oriented band between 32° and 36°N (Fig. 9 b). Compared with previous studies, this study identifies a significantly lower proportion of vortices exiting the Tibetan Plateau (Table 1 ). This difference is mainly attributed to the use of hourly ERA5 data, which provides significantly higher temporal resolution than the datasets used in previous research. The higher temporal resolution increases the occurrence frequency of vortices, enabling the detection of shorter-lived vortices. Additionally, the higher resolution leads to a lower number of vortices exiting the plateau, as datasets with lower temporal resolution are more likely to overestimate the lifespans of vortices, which may increase the number of the vacating type erroneously. As mentioned above, by using the hourly ERA5 data, the numerator for calculating the vacating type’s proportion decreases but the denominator increases, and thus, the vacating-type’s proportion is smaller in this study. Unlike the quasi-stationary and moving TMVs, ~ 91% of the vacating TMV tracks originate from the eastern and northeastern sections of the Tibetan Plateau (Fig. 9 c). After exiting the plateau, vacating TMVs mainly affects regions near the plateau, with only a small proportion directly influencing on areas in Mongolia, India, and even the East China Sea. The movement directions of the vacating TMVs are closely associated with the steering flow at the 500-hPa level. For the vacating TMVs, ~ 56% of the vortices move eastward (Fig. 9 e), and ~ 30% move northeastward. The remaining vortices mainly move northward or southwestward, each accounting for ~ 6%. Notably, no vortices move westward or southeastward. In contrast, the non-vacating TMVs exhibit significantly different movement patterns: (i) the eastward and northeastward moving types account for ~ 63% and 10%, respectively; (ii) the westward and southeastward moving types comprise around 9% (ranked third) and 8% (ranked fourth), respectively; (iii) the southward/northward moving types comprises ~ 1%, representing the smallest proportion within the non-vacating TMVs. 4.3 Precipitation features Over the Tibetan Plateau, the temporally averaged accumulated precipitation typically decreases from southeast to northwest (Fig. 10 a), with two peak centers located in the southeastern (≥ 1200 mm) and eastern (≥ 800 mm) sections of the plateau. In contrast, the TMV-related precipitation exhibits a different spatial distribution, with its maximum centers mainly located in the central band of the Tibetan Plateau (Fig. 10 b). This region displays the highest TMV influence frequency (Fig. 3 e). The spatial mean of the temporally averaged TMV-related precipitation over the Tibetan Plateau is ~ 37.8 mm per warm season, which is about an order of magnitude smaller than the warm-season accumulated precipitation (~ 322.3 mm). The contribution of TMVs to the total accumulated precipitation over the Tibetan Plateau differs in distribution compared with TMV-related precipitation (cf. Figure 10 b and 10 c). The maximum center of TMV contributions is mainly located in the northwestern section of the plateau, with high TMV influence frequency (Fig. 3 e). However, the warm-season accumulated precipitation in this area is relatively low (Fig. 10 a). In ~ 21.8% of the Tibetan Plateau area, TMV-related precipitation accounts for over 20% of the total warm-season accumulated precipitation. On average, TMV-related precipitation comprises ~ 13.8% of the total warm-season accumulated precipitation over the Tibetan Plateau, which is significantly lower than the proportions observed in the northwestern Tibetan Plateau (≥ 36%) (Fig. 10 c). Precipitation associated with shorter-lived TMVs, the quasi-stationary type, and the moving type exhibits distinct spatial distributions (Fig. 10 d–f). Notably, the moving TMVs generate the highest accumulated precipitation, with the rainfall centers mainly located around the junction of Qinghai, Xizang, and Sichuan (Fig. 10 f). In contrast, the quasi-stationary TMVs have the lowest accumulated precipitation, with rainfall centers mainly located in the southwestern section of the Tibetan Plateau (Fig. 10 e). Regarding rainfall contributions from shorter-lived TMVs, the quasi-stationary type, and the moving type, their maximum centers are mainly located in the northwestern section of the plateau (Fig. 10 g–i). Among these, the moving TMVs have the highest contribution to rainfall, followed by the quasi-stationary TMVs and the shorter-lived TMVs. However, shorter-lived TMVs should not be overlooked, as in some regions of the plateau, their contribution to local rainfall exceeds 12% (Fig. 10 g). To elucidate TMV contributions to different precipitation intensities, we categorize rainfall intensity into three groups based on the percentile distributions of historical hourly precipitation over the Tibetan Plateau: (a) weak precipitation (0.1 mm hr⁻ 1 ≤ hourly precipitation < 1 mm hr⁻ 1 ), (b) moderate precipitation (1 mm hr⁻ 1 ≤ hourly precipitation < 3 mm hr⁻ 1 ), and (c) heavy precipitation (hourly precipitation ≥ 3 mm hr⁻ 1 ). For all three groups, the contributions of TMVs to the warm-season accumulated precipitation reach their peak in the northwestern section of the Tibetan Plateau (Fig. 10 j–l). With increasing rainfall intensity increases, the maximum contributions of TMV-related precipitation also increase from ~ 36% in the weak group to ~ 70% in the heavy group (the northwestern and eastern sections of the plateau). Overall, TMV-related precipitation accounts for ~ 12.8% in the weak group, ~ 14.2% in the moderate group, and ~ 13.4% in the heavy group across the Tibetan Plateau. 5 Composite features During the study period, the quasi-stationary, non-vacating, and vacating TMVs exhibit occurrence frequencies of 3055, 1844, and 54, respectively. Eulerian and Lagrangian composites are used to analyze the common features of these three types of TMVs. 5.1 Composite background circulations The Eulerian composite is used to illustrate the common background circulations for the different types of TMVs. In the upper troposphere, the South Asian High maintains strong intensity at lower latitudes for all three types of TMVs (Fig. 11 a–c), with an upper-level jet present in the middle latitudes. Notably, the South Asian High exhibits the largest coverage for the quasi-stationary TMVs and the smallest coverage for the vacating TMVs. The upper-level jets for the quasi-stationary and non-vacating TMVs are mainly located north of the Tibetan Plateau (Fig. 11 a–b), while the jet for the vacating TMVs is mainly positioned to the northeast of the plateau (Fig. 11 c). Over 50% of the vacating TMVs form in regions beneath the upper-level jet, while less than 20% of both quasi-stationary and non-vacating TMVs form in these regions. This indicates that the upper-level steering flow is stronger for the vacating TMVs, which facilitates their movement away from the plateau. In the middle troposphere, the western Pacific subtropical high is mainly located in the southeast of China for all three types of TMVs (Fig. 11 d–f). West of the subtropical high, a shortwave trough appears over the southwestern section of the Tibetan Plateau. The southwesterly winds ahead of this trough facilitate moisture transport to the Tibetan Plateau, resulting in a relatively moist band over the plateau. Overall, the background circulations and mean formation locations are similar for the quasi-stationary and non-vacating TMVs (Fig. 11 d–e) but differ from those of the vacating TMVs. The key difference among the three types of TMVs is the presence of a shortwave trough over the eastern flank of the Tibetan Plateau for the vacating TMV, which is absent in the quasi-stationary and non-vacating TMVs. This shortwave trough creates favorable conditions for the formation of the vacating TMVs, with their average formation location occurring within the trough (Fig. 11 f). 5.2 Composite three-dimensional structures Lagrangian composites are used to illustrate the three-dimensional structures of TMVs at the time of their formation. To examine the structures of the vortices, assessing their vertical stretching is crucial. The quasi-stationary, non-vacating, and vacating TMVs exhibit average top and bottom levels of ~ 479/~514, ~ 480/~518, and ~ 466/~531 hPa, respectively. Consequently, the average thickness of the quasi-stationary TMVs (~ 35 hPa) is similar to that of the non-vacating TMVs (~ 38 hPa) but significantly thinner than the average thickness of the vacating TMVs (~ 65 hPa). All TMVs form over the Tibetan Plateau, which features significant altitude variations (ranging from 2600 to 5500 m). Therefore, to analyze the Lagrangian composite, it is crucial to account for the height of the Tibetan Plateau. Over 75% of the quasi-stationary and non-vacating TMVs form at terrain elevations higher than 4600 m, while over 75% of the vacating TMVs form at elevations above 2900 m (Fig. 11 i). Therefore, only results at elevations above these reference heights (4600 m for the quasi-stationary and non-vacating TMVs and 2900 m for the vacating TMVs (as indicated by gray shading in Fig. 12 ) are considered valid for analysis. All three types of TMVs exhibit a cyclonic vorticity center and a negative geopotential height deviation center within their central regions (indicated by the purple dashed boxes in Fig. 12 a–c). Notably, the vacating TMVs exhibit the strongest cyclonic vorticity and geopotential height deviation. However, the quasi-stationary TMVs feature the weakest cyclonic vorticity. This indicates that the vacating TMVs exhibit the highest intensity, and the quasi-stationary TMVs feature the lowest intensity. This is further supported by a comparison of the 500-hPa cyclonic vorticity and geopotential height within their central regions (Fig. 13 d–f). Notably, the vacating TMVs feature the thickest air columns with cyclonic vorticity and negative geopotential height deviation (Fig. 12 a–c), reflecting their greatest vertical extent. For all three TMV types, negative geopotential height deviations below 400 hPa are mainly associated with cyclonic vorticity. However, the three types display significant differences in the type of vorticity above 400 hPa. For the quasi-stationary and non-vacating TMVs, the negative geopotential height deviations are mainly related to anticyclonic vorticity (Fig. 12 a–b), as their central regions are dominated by a high-pressure ridge (Fig. 13 a–b). For the vacating TMVs, the negative geopotential height deviations are mainly associated with cyclonic vorticity (Fig. 12 c), as their central regions are dominated by a low-pressure trough (Fig. 13 c). For all three TMV types, strong convergence and divergence dominate the lower and upper levels within their central regions (Fig. 12 d–f), respectively. These features enhance and maintain upward motions through mass continuity (Fu et al. 2019, 2022). Notably, the non-vacating TMVs exhibit the strongest lower-level convergence, while the vacating TMVs feature the weakest low-level convergence. For all three TMV types, strong ascent centers are mainly located above the strong low-level convergence centers and are closely associated with positive temperature deviations (Fig. 12 g–i). This indicates the key role of precipitation-related latent heating in generating these positive temperature deviations. Notably, the vacating TMVs exhibit the strongest positive temperature deviations and the highest rainfall intensity than the other two types of TMVs (not shown). Additionally, only the vacating TMVs exhibit a negative temperature-deviation center (indicating a cold pool) below the central region (Fig. 12 g–i). The absence of negative temperature-deviation centers in the quasi-stationary and non-vacating TMVs may be attributed to the strong surface heating from the Tibetan Plateau, as their referenced height is much higher than that of the vacating type (Fig. 11 i). The air column with relative humidity ≥ 72% exhibits the greatest vertical extent for the vacating type (Fig. 12 j–l), indicating that the moisture conditions are most favorable for precipitation in this type. For all three TMV types, westerly winds dominate above 500 hPa (Fig. 12 d-f) and are present in the southern section of the central region (Fig. 13 d–f). Compared with the quasi-stationary TMVs, the non-vacating and vacating TMVs exhibit a stronger westerly wind, which facilitates their movement (Fig. 13 b–c and 13 e–f). Additionally, the vacating TMVs feature the strongest intensities of both northerly and southerly winds, further suggesting that this type exhibits the highest vortex intensity among all three TMV types. 5.3 Formation mechanisms To elucidate the formation mechanisms of TMVs, we conduct a vorticity budget analysis on the central-region averaged vorticity within the mean vertical extent of the vortices, from t-6 (i.e., 6 hr before TMV formation) to t (i.e., the time of TMV formation). Before conducting a detailed analysis, we first assess the balance of the budget equation. A comparison between Fig. 14 a–c and Fig. 14 d–f indicates that for all TMV types, the local temporal derivative exhibits a pattern similar to the overall-effect’s behavior (Section 2 e) in both vertical structures and magnitudes. Moreover, from t -6 to t , within the respective mean vertical extent of all TMV types, the ratios of local temporal derivatives to overall effects have a mean value greater than 72%, and the residual effect is significantly smaller than both the local temporal derivative and overall effect (Fig. 14 ). This indicates that the vorticity budget equation is well balanced, making it suitable for elucidating the formation mechanisms of the vortices. Within the mean vertical extent of the three TMV types, the central-region averaged vorticity significantly increases from t -6 to t , corresponding to vortex formation (Fig. 15 a–c). Notably, the vacating TMVs exhibit the strongest cyclonic vorticity, with the slowest rate of increase. The non-vacating TMVs feature the second strongest cyclonic vorticity, with the highest rate of increase. The differences in the rates of increase across the three TMV types are further supported by the terms local temporal derivative and overall effect (Fig. 14 a–f). Although the vorticity budget distributions vary for each TMV type (Fig. 16 ), significant similarities emerge: (i) The stretching term is the main driver of the increase in cyclonic vorticity within the central regions of the vortices (Fig. 16 a–c). This indicates that vertical stretching, driven by strong low-level convergence (Fig. 15 d–f), is the main factor in generating the cyclonic vorticity associated with TMVs. This finding is consistent with results from previous studies (Fu et al. 2019, 2021b). (ii) Vertical advection is the second most dominant factor (Fig. 16 j-l), as ascending motions in the central region of TMVs (Fig. 15 g–i) transport cyclonic vorticity (Fig. 15 a–c) upward. (iii) Horizontal advection is the most detrimental factor, which decelerates the increase in cyclonic vorticity (Fig. 16 g–i), as it causes a net export of cyclonic vorticity from the central regions of TMVs. Moreover, the tilting term contributes to the reduction in cyclonic vorticity within the central regions of TMVs (Fig. 16 d–f), by generating anticyclonic vorticity. For the vacating TMVs, the upper levels are dominated by cyclonic vorticity (Fig. 15 c), which is associated with the upper-level low-pressure trough (Fig. 13 c). In contrast, the upper levels of quasi-stationary and non-vacating TMVs are dominated by anticyclonic vorticity (Fig. 15 a–b). Additionally, the vacating TMVs exhibit the strongest horizontal advection (Fig. 16 g–i), mainly driven by the eastward transport of cyclonic vorticity and the westerly winds associated with the upper-level low-pressure trough (Fig. 13 c). Consequently, the upper-level cyclonic vorticity of vacating TMVs rapidly increases over time (Fig. 15 c), which is confirmed by the strong overall effect at upper levels (Fig. 14 f). In contrast, the upper-level anticyclonic vorticity of quasi-stationary and non-vacating TMVs weakens over time (Fig. 14 d-e). Overall, more favorable dynamical conditions are crucial for the vacating process of TMVs. 6 Conclusion and discussion During warm seasons, the Tibetan Plateau serves as a crucial source region for mesoscale vortices, which significantly influence rainfall over and around the plateau. Despite numerous studies on these vortices, none have investigated the shorter-lived TMVs, and no studies have ever investigated TMVs’ vertical-extent features, common formation mechanisms, and hourly diurnal variations. In this study, we attempt to address these remaining scientific questions, which is helpful to reach a more comprehensive understanding of the precipitation and mesoscale weather systems over and around Tibetan Plateau. Over 42 warm seasons, a total of 15,644 TMVs are identified (~ 372 TMVs per warm season), which account for ~ 50.2% of the total duration of a warm season on average. TMVs exhibit a significantly higher occurrence frequency than Tibetan Plateau vortices (Curio et al. 2019; Lin et al. 2020), as TMVs include vortices with a lifespan of ≤ 6 hr, which cannot be detected by datasets with a temporal resolution coarser than 6 hr. These vortices can form in nearly any region over the Tibetan Plateau, with over 80% of TMVs originating west of 95°E. The formation of TMVs is highly terrain-dependent, with higher altitude regions tend to promote the formation of more TMVs. TMVs account for ~ 13.8% of the total accumulated precipitation over the Tibetan Plateau, which is lower than those reported by Curio et al. (2019) and Lin et al. (2021). This difference is partly due to their use of precipitation datasets with coarser temporal resolutions. As precipitation intensity increases, TMV contribution to rainfall also increases. Over 68% of TMVs have a lifespan of less than 6 hr (with ~ 2% lasting 24 hr or more. Additionally, over 75% of TMVs have a vertical thickness of ≤ 50 hPa. Overall, as the lifespan of TMVs increases, they tend to exhibit stronger intensities, greater vertical thicknesses, and more significant displacement. From 1979 to 2020, our results reveal that TMVs do not increase/decrease significantly in their total occurrence frequency. In contrast, longer-lived TMVs have increased at a rate of ~ 1 TMV per warm season. However, as these results are ERA5-based, they may be different from the actual situation. TMVs exhibit significant monthly variations, with the occurrence frequency peaking in July. This is closely related to shifts in the location of the 500-hPa convergence line over the western part of the Tibetan Plateau. Regarding diurnal variation, longer-lived TMVs exhibit a prominent peak from the afternoon to early morning (this finding is consistent with the results documented in Li et al. (2014b)), with the highest frequency around 22:00 LST. Overall, TMVs tend to form in a background environment characterized by warmer surface temperatures, higher CAPE, heavier precipitation, stronger upper-tropospheric divergence, and greater middle-tropospheric cyclonic vorticity. Among the different TMV types, the vacating type exhibits the highest intensity in cyclonic vorticity, geopotential height, wind speed, and precipitation. Conversely, the quasi-stationary type exhibits the lowest intensity. The tracks of both the vacating and quasi-stationary TMVs cover most of the Tibetan Plateau. However, the vacating TMVs mainly originate from the eastern and northeastern sections of the plateau and move toward the downstream regions. Most of both TMVs move eastward, consistent with the findings of Lin et al. (2020). This study analyzes the common formation mechanisms for different TMV types by using a Lagrangian composite of the vorticity budget. For the quasi-stationary, non-vacating, and vacating TMVs, their cyclonic vorticity significantly increases before the formation of TMVs owing to vertical stretching associated with strong low-level convergence. Moreover, the upper levels of the vacating TMVs are dominated by cyclonic vorticity, which is related to an upper-level low-pressure trough. Conversely, the upper levels of the quasi-stationary and non-vacating TMVs are dominated by anticyclonic vorticity. This study indicates that a strong steering flow, intense vortex intensity, large vertical extent, and rapid enhancement of upper-level cyclonic vorticity are crucial for the vacating of TMVs. Similarly, Curio et al. (2019) identified a strong steering flow as a favorable condition. However, Li et al. (2019) proposed that the 500-hPa convergence to the east of the vortices, divergence associated with the 200-hPa upper-level jet, and strong ascending motions were key factors for the vacating of vortices. This study utilizes hourly ERA5 data to explore TMV statistics, which are helpful to reach a more comprehensive understanding of the mesoscale vortices over Tibetan Plateau. However, because ERA5 data may contain non-negligible errors in representing the actual atmospheric conditions over the plateau, the results of this study may differ from the actual conditions. To reduce uncertainties associated with the use of ERA5, we recommend using additional high-spatiotemporal-resolution data (e.g., 0.25° and hourly, or finer) in future TMV investigations. Combining all available research results will lead to more accurate conclusions regarding TMVs. Declarations Data Availability The ERA5 data used in this work (Hersbach et al. 2020 for data on pressure and single levels) are freely available on the Copernicus Data Store (CDS) at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset&text=ERA5. The GPM IMERG data was downloaded from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets?keywords=GPM&page=1). Funding This research was supported by the National Natural Science Foundation of China (grant numbers 42075002, 42475008 and U2142202), the Open Research Fund Project of Sichuan Provincial Key Laboratory of Plateau and Basin Rainstorm and Flood Disaster (SKZT202203, SZKT202402), the Youth Research Project of the China Meteorological Administration Training Center (2023CMATCQN03), and the Fengyun Satellite Application Advance Plan (FY-APP-2022.0102). Competing Interests The authors declare that there are no conflicts of interest or competing interests regarding the publication of this paper. References Bartels DL, Maddox RA (1991) Midlevel cyclonic vortices generated by mesoscale convective systems. Mon Weather Rev 119:104-117. https://doi.org/10.1175/1520-0493(1991)1192.0.CO;2 Blender R, Fraedrich K, Lunkeit F (1997) Identification of cyclone-track regimes in the North Atlantic. 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Science Press, Beijing, pp 195-205 Ma Y, Tang GQ, Long D, Yong B (2016) Similarity and error intercomparison of the GPM and its predecessor-TRMM multisatellite precipitation analysis using the best available hourly gauge network over the Tibetan Plateau. Remote Sens 8(7):569. https://doi.org/10.3390/rs8070569 Ma TT, Wu GX, Liu YM, Mao JY (2022) Abnormal warm sea-surface temperature in the Indian Ocean, active potential vorticity over the Tibetan Plateau, and severe flooding along the Yangtze River in summer 2020. Q J R Meteorol Soc 148:1001-1019. https://doi.org/10.1002/qj.4243 Mai Z, Fu SM, Sun JH et al (2021) Key statistical characteristics of the mesoscale convective systems generated over the Tibetan Plateau and their relationship to precipitation and southwest vortices. Int J Climatol 41(S1):E875-E896. https://doi.org/10.1002/joc.6735 Neu U, Akperov MG, Bellenbaum N et al (2013) A community effort to intercompare extratropical cyclone detection and tracking algorithms. Bull Am Meteorol Soc 94:529-547. https://doi.org/10.1175/BAMS-D-11-00154.1 Orlanski I (1975) A rational subdivision of scales for atmospheric processes. Bull Am Meteorol Soc 56:527-530 Qian W, Quan L, Shi S (2002) Variations of the dust storm in China and its climatic control. J Climate 15:1216-1229. https://doi.org/10.1175/1520-0442(2002)0152.0.CO;2 Qian ZA, Tang MC, Li DL et al (1984) The discuss on climate factors and statistical analysis of the Tibetan Plateau vortex in 1979 summer. In: Edit Group of Tibetan Plateau Meteorological Experiment Corpus (ed) The Tibetan Plateau Meteorological Experiment Corpus II. Science Press, Beijing, pp 182-194 Qiao QM, Zhang YG (1994) Tibetan Plateau climatology. Meteorology Press, Beijing, pp 120-155 Shen R, Reiter ER, Bresch JF (1986) Some aspects of the effects of sensible heating on the development of summer weather systems over the Tibetan Plateau. J Atmos Sci 43(20):2241-2260 Tan ZM, Chen LS, Li Y et al (2013) South China β research on structure and mechanism of scale strong convection system. Meteorology Press, Beijing Tang XY, Zhou CY, Wang G (2014) Statistical analysis on the plateau low vortex activity characteristics. Plateau Mountain Meteorol Res 34(3):41-44 Tang YQ, Wu GX, He B et al (2023a) Two types of Tibetan Plateau vortex genesis in June and the associated mechanisms. Clim Dyn 61:4343-4357. https://doi.org/10.1007/s00382-023-06806-7 Tang H, Fu SM, Sun JH, Zhou XX (2023b) A three-dimensional objective identification of the Tibetan Plateau vortex based on wind field. Chin J Atmos Sci 47(3):698-712. http://www.iapjournals.ac.cn/dqkx/article/doi/10.3878/j.issn.1006-9895.2112.21127 Tao SY, Ding YH (1981) Observational evidence of the influence of the Qinghai-Xizang (Tibet) Plateau on the occurrence of heavy rain and severe convective storms in China. Bull Am Meteorol Soc 62(1):23-30. https://doi.org/10.1175/1520-0477(1981)0622.0.CO;2 Tessendorf SA, Miller LJ, Wiens KC, Rutledge SA (2005) The 29 June 2000 supercell observed during STEPS. Part I: kinematics and microphysics. J Atmos Sci 62(12):4127-4150. https://doi.org/10.1175/JAS3585.1 Trier SB, Davis CA (2002) Influence of balanced motion on heavy precipitation within a long-lived convectively generated vortex. Mon Weather Rev 130:877-899. https://doi.org/10.1175/1520-0493(2002)1302.0.CO;2 Wang B (1987) The development mechanism for Tibetan Plateau warm vortices. J Atmos Sci 44:2978-2994. https://doi.org/10.1175/1520-0469(1987)0442.0.CO;2 Wang X, Li YQ, Yu SH, Jiang XW (2009) Statistical study on the plateau low vortex activities. Plateau Meteorol 28(1):64-71 Wei FY (2007) Modern climate statistical diagnosis and prediction technology, 2nd edn. Meteorological Press, Beijing Wu GX, Ma TT, Liu YM, Jiang ZH (2020) PV-Q perspective of cyclogenesis and vertical velocity development downstream of the Tibetan Plateau. J Geophys Res Atmos 125:e2019JD030912. https://doi.org/10.1029/2019JD030912 Wu GX, Tang YQ, He B et al (2022) Potential vorticity perspective of the genesis of a Tibetan Plateau vortex in June 2016. Clim Dyn 58:3351-3367. https://doi.org/10.1007/s00382-021-06102-2 Xin YF, Liu JB, Liu XW et al (2022) Reduction of uncertainties in surface heat flux over the Tibetan Plateau from ERA-Interim to ERA5. Int J Climatol 42:6277-6292. https://doi.org/10.1002/joc.7589 Xu R, Tian FQ, Yang L et al (2017) Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network. J Geophys Res Atmos 122(2):910-924. https://doi.org/10.1002/2016JD025418 Yanai M, Li CF, Song ZS (1992) Seasonal heating of the Tibetan Plateau and its effects on the evolution of the Asian summer monsoon. J Meteorol Soc Jpn 70(1B):319-350. https://doi.org/10.2151/jmsj1965.70.1B_319 Yeh TC, Gao YX (1979) Meteorology of the Qinghai-Xizang (Tibet) Plateau. Science Press, Beijing Yu SH, Gao WL (2006) Observational analysis on the movement of vortices before/after moving out the Tibetan Plateau. Plateau Meteorol 25:392-399 Yu SH, Xiao YH, Gao WL (2007) Cold air influence on the Tibetan Plateau vortex moving out of the Plateau. J Appl Meteorol Sci 18:737-747 Yu SH, Gao WL, Xiao YH (2009) Diagnosis of effect of southwesterlies on Tibetan vortex moving east. Plateau Mountain Meteorol Res 29:1-8 Zhang B, Li GP, Duan L et al (2018a) Climatic characteristics of Tibetan Plateau vortex based on the objective identification in the recent 30 years. J Lanzhou Univ Nat Sci 54(1) Zhang SJ, Wang DH, Qin ZK et al (2018b) Assessment of the GPM and TRMM precipitation products using the rain gauge network over the Tibetan Plateau. J Meteorol Res 32(2):324-336. https://doi.org/10.1007/s13351-018-7067-0 Zhang FM, Wang CH, Pu ZH (2019a) Genesis of Tibetan Plateau vortex: Roles of surface diabatic and atmospheric condensational latent heating. J Appl Meteorol Clim 58(12):2633-2651. https://doi.org/10.1175/JAMC-D-19-0103.1 Zhang W, Zhang H, Liang H et al (2019b) On the suitability of ERA5 in hourly GPS precipitable water vapor. Footnotes To calculate relative vorticity using the central difference scheme, we apply two additional conditions to ensure accurate results (Fu et al. 2020): (i) The four points used in the calculation should exhibit a consistent counterclockwise circulation (in the northern hemisphere); (ii) The magnitudes of |∂ v /∂x| and |∂ u /∂y| (with u and v representing zonal and meridional winds, respectively) should be of similar magnitude. If both conditions are met, the calculation result is retained. Otherwise, the result is set to zero. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6436848","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450108547,"identity":"2973a572-3ec7-42d1-a079-b855aa1b8713","order_by":0,"name":"Huan Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYJACZoYKUpTzgLWcIVkLYxspWuzZzx5+XTjPLpq/gf3iYx4GuzzCtvDkpVnP3JacO+MAT7ExD0NyMREOyzEz5t12ILfhAE+a5AyGA4kNBLXwvwFqmXMgdz7xWiRyjB/zNhzI3XCA/ZjEB6K03HhjxsxzLDl342EeZoMPBsmEtbD35xh/5qmxy513vP3hg4QKO8JagIBNAkwx8xgwMBgQoR6k9gPUwgfEqR8Fo2AUjIIRBwCylTk2OBfLBwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-1130-7730","institution":"China Meteorological Administration","correspondingAuthor":true,"prefix":"","firstName":"Huan","middleName":"","lastName":"Tang","suffix":""},{"id":450108548,"identity":"36604a2b-ff67-4838-9a0e-4b38b496d8ef","order_by":1,"name":"Shen-Ming Fu","email":"","orcid":"https://orcid.org/0000-0001-9670-0607","institution":"Institute of Atmospheric Physics Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shen-Ming","middleName":"","lastName":"Fu","suffix":""},{"id":450108549,"identity":"9a4071c0-0985-47a1-b9fa-e9531f4d3a39","order_by":2,"name":"Chaoying Yang","email":"","orcid":"","institution":"Electric Power Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Chaoying","middleName":"","lastName":"Yang","suffix":""},{"id":450108550,"identity":"f0282e0a-5973-481d-8344-fda5e7e6a1f0","order_by":3,"name":"Jianhua Sun","email":"","orcid":"","institution":"IAP CAS: Institute of Atmospheric Physics Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianhua","middleName":"","lastName":"Sun","suffix":""},{"id":450108551,"identity":"098bae55-c214-424e-9316-2729b353822f","order_by":4,"name":"Wanli Li","email":"","orcid":"","institution":"China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Wanli","middleName":"","lastName":"Li","suffix":""},{"id":450108552,"identity":"9b73894b-b07c-4367-9fc5-e6e892fb387d","order_by":5,"name":"Xingwen Jiang","email":"","orcid":"","institution":"Chengdu Institute of Plateau Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Xingwen","middleName":"","lastName":"Jiang","suffix":""},{"id":450108553,"identity":"39257a58-db4b-4914-92db-6d6ebe7154ff","order_by":6,"name":"Xiuming Wang","email":"","orcid":"","institution":"China Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Xiuming","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-04-13 01:58:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6436848/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6436848/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81958354,"identity":"6ebff3ab-7c42-4c59-9403-6b0efb9ab417","added_by":"auto","created_at":"2025-05-05 10:17:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":495483,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) Schematic of detecting a mesoscale vortex (abbreviated as vortex) using high-resolution grid data. The blue-shaded circle with a red boundary represents the vortex, and the large red dot indicates its center. The large purple arrows show the cyclonic wind within the vortex grids, while the small green boxes represent grids outside the vortex. The grid interval, denoted as d\u003cem\u003ex\u003c/em\u003e, signifies spatial resolution. Panel (b) is similar to (a) but shows low-resolution grid data. Panel (c) illustrates the use of low-temporal resolution data to track a vortex. Here, the blue-shaded circle with a red boundary represents Vortex I, the red dot indicates its center, \u003cem\u003et\u003c/em\u003e represents time, ∆\u003cem\u003et \u003c/em\u003edenotes the temporal interval (i.e., temporal resolution), and the open arrow shows the vortex tracking result. Panel (d) is the same as (c) but displays high-temporal resolution data. Here, the small blue-shaded circle with a red curve line denotes the dissipation of Vortex I, and the blue-shaded circle with a green boundary represents the newly formed Vortex II.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/2b48c31bd5e70a49983fcbbb.png"},{"id":81959309,"identity":"c34bd612-7ce4-4fae-ba8b-8139cc60ac86","added_by":"auto","created_at":"2025-05-05 10:25:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":495856,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of various types of TMVs. The green number inside each small box indicates the occurrence frequency of TMVs of a specific type.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/c26e348e0f29263e8288b7ca.png"},{"id":81959310,"identity":"fe9b7d9d-0556-4053-9ac4-d9363d14e130","added_by":"auto","created_at":"2025-05-05 10:25:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":761912,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the terrain height of the Tibetan Plateau (shading in meters), with the red box indicating the region used for detecting TMV formation. Panel (b) illustrates the spatial distribution (on a 0.25° × 0.25° grid) of all TMV occurrence frequencies based on the formation locations (shading). Panel (c) displays the zonally accumulated occurrence frequency of TMVs over the Tibetan Plateau at 1° intervals (red line; e.g., the value at 34°N is calculated through the summation of the occurrence frequencies of TMVs within the 33.5°N–34.5°N band across the entire plateau) and the zonally averaged terrain height (blue line in meters). Panel (d) is similar to (c) but presents the meridionally accumulated occurrence frequency. Panel (e) is the same as (b) but displays the frequency of influence. The thick black lines denote the main body of the Tibetan Plateau, while the black dashed lines show 89°E and 33°N.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/e791c31f7dc0285495f732c6.png"},{"id":81959311,"identity":"192f3873-fd30-48de-9965-d11179e1d0f1","added_by":"auto","created_at":"2025-05-05 10:25:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11845907,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the annual occurrence frequencies of all TMVs (green bars) and longer-lived TMVs (red bars). The green and red dashed lines represent the linear trends of these two types of TMVs from 1979 to 2020. The blue solid line represents the station-observed annual mean temperature, and the blue dashed line indicates its trend. Panel (b) is the same as (a) but displays the quasi-stationary and moving TMVs.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/9c5583b88b39427191a74736.png"},{"id":81959687,"identity":"c141dbee-385f-4f32-ad1a-044186b73283","added_by":"auto","created_at":"2025-05-05 10:33:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":16743604,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the monthly occurrence frequencies of all TMVs, quasi-stationary type, and moving type during the 42 warm seasons from 1979 to 2020, with the frequency values represented by the columns. Panels (b–f) display the monthly averaged 500-hPa streamline field (from 1979 to 2020) and the monthly occurrence frequency of TMVs (shading). The thick black solid lines denote the main body of the Tibetan Plateau, the purple pentagrams indicate the mean locations of all TMVs formed each month, and the purple dashed lines represent the convergence lines.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/78876991e7d9c74411723dc4.png"},{"id":81958375,"identity":"51b24511-1507-415d-9b78-d095c798767c","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":8718743,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the diurnal variations in occurrence frequencies (pink lines) for longer-lived TMVs, moving, and quasi-stationary types, along with the 500-hPa vorticity (purple lines), averaged over the Tibetan Plateau at the formation times of TMVs and for the entire study period (denoted as “Climate”). Panel (b) displays the same data as (a), but with the diurnal cycles of surface sensible heat flux (orange lines), surface latent heat flux (green lines), and 2-m temperature (red lines). Panel (c) presents the same as (b) but with CAPE (blue lines) and 400-hPa vertical velocity (purple lines). Panel (d) shows the same data as (b), but with 200-hPa divergence (blue lines) and total precipitation (green lines). LST denotes local standard time, calculated as: LST = UTC + longitude/15.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/816a8b1756f15139ceab2e7b.png"},{"id":81958360,"identity":"4f073fcc-3071-45b4-9bb0-4cd8385337d5","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":654325,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the occurrence frequencies (blue bars) and percentages of TMVs with different lifespans. Panel (b) presents boxplots of lifespans (h), moving distances (°), and intensities (10⁻\u003csup\u003e5\u003c/sup\u003e s⁻\u003csup\u003e1\u003c/sup\u003e). The boxes represent the 25th–75th percentiles, the lines within the boxes indicate the median values, and the small triangles denote the mean values. Panel (c) shows the scatterplot of TMV lifespans versus their moving distances, with the red line representing the linear fit result. Panel (d) is the same as (c) but displays TMV intensities. Panel (e) shows the percentages of different lifespans for quasi-stationary, non-vacating, and vacating types.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/aca4a509f96381d3465fd878.png"},{"id":81958370,"identity":"ec0e7c65-f6dd-414f-94b3-c9ba2c4b1cf8","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":5157300,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the highest top levels of TMVs observed during 42 warm seasons, based on the maximum altitude reached throughout each TMV lifespan. Panel (b) presents the lowest bottom levels of TMVs over the same period, based on the minimum altitude reached during each TMV lifespan. Panel (c) shows the maximum vertical thicknesses of TMVs during the 42 warm seasons, based on the highest thickness observed throughout each TMV lifespan.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/fb5ec5f970d83bea2ed2ad72.png"},{"id":81958373,"identity":"913bc543-9d28-441a-81d6-61436fc84883","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":8769345,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the influence frequency of the quasi-stationary TMVs. Panel (b) presents the influence frequency for the moving TMVs. Panel (c) displays the tracks (red lines) of the vacating type, with blue and green dots representing the formation and dissipation locations of TMVs, respectively. The gray shading indicates the terrain, and the bold black line denotes the boundary of the Tibetan Plateau. Panel (d) shows the eight moving directions of TMVs at 45° intervals. The moving direction is defined by a vector that starts from the mean location at the TMV formation time, extends 1 hr later, and ends at the mean location at the TMV’s dissipation time and 1 hour earlier. Panel (e) displays the proportions of different moving directions for the vacating (inner circle) and non-vacating TMVs (outer circle).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/9ca7bd36fa1dac06bcb24fcb.png"},{"id":81958368,"identity":"64af5e4e-22cd-4265-9867-1b53b1865a9d","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1681297,"visible":true,"origin":"","legend":"\u003cp\u003ePanel (a) shows the temporally averaged warm-season accumulated precipitation (shading; mm) (2001 to 2020). Panel (b) is the same as (a) but displays all TMV-related precipitation. Panel (c) illustrates the contribution of all TMV-related precipitation to the total precipitation over the Tibetan Plateau during the 20 warm seasons (shading; %). Panels (d–f) are the same as (b) but forshorter-lived TMVs, quasi-stationary type, and moving type. Panels (g–i) are the same as (c) but present data for shorter-lived TMVs, quasi-stationary type, and moving type. Panels (j–l) are the same as (c) but display weak, moderate, and strong precipitation intensities.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/fd45302fe9326b672d6cff4d.png"},{"id":81958361,"identity":"01618c23-2d0d-42fe-a100-127a1627d81f","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1161458,"visible":true,"origin":"","legend":"\u003cp\u003eComposite background circulations at 200 hPa at the formation time of TMVs for the (a) quasi-stationary, (b) non-vacating, and (c) vacating types. Shading represents wind speeds exceeding 25 m s\u003csup\u003e−1\u003c/sup\u003e, the black solid line indicates geopotential height (units: gpm), the red solid line represents temperature (units: °C), the bold black line outlines the boundary of the Tibetan Plateau, small blue dots indicate TMV formation locations, and large red dots represent the mean formation locations. Panels (d–f) are the same as (a–c) but display data at 500 hPa. Shading represents specific humidity (units: gkg\u003csup\u003e−1\u003c/sup\u003e), blue wind barbs denote the wind field (a full barb = 4 m s\u003csup\u003e−1\u003c/sup\u003e), the blue solid line represents the isohypse of 5880 gpm, and the thick dashed green lines represent trough lines. Panels (g–i) are box-and-whisker plots showing the latitudes, longitudes, and terrain elevations at the formation time for the three types of TMVs. The boxes represent the 25th–75th percentiles, the lines within the boxes indicate the median values, and the stars represent the mean values. SAH = South Asia High; WPSH = western Pacific subtropical high.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/895c38be718c11b7dc2609c3.png"},{"id":81958363,"identity":"4e0ffa6b-9494-4d6b-b3c9-30491937acb7","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3587587,"visible":true,"origin":"","legend":"\u003cp\u003eLagrangian composites of cross-sectional averages of the meridional mean (from −1° to 1°) at the formation time for (a) quasi-stationary, (b) non-vacating, and (c) vacating TMVs. Shading represents vorticity (10⁻\u003csup\u003e5\u003c/sup\u003e s⁻\u003csup\u003e1\u003c/sup\u003e), black contours denote geopotential height deviation (gpm), purple dashed boxes indicate the central region and averaged vertical extent of TMVs, and gray shading indicates the reference heights for the quasi-stationary and non-vacating TMVs. Panels (d–f) are the same as (a–c) but display divergence (shading; 10⁻\u003csup\u003e5\u003c/sup\u003e s⁻\u003csup\u003e1\u003c/sup\u003e) and zonal wind (black contours; m s⁻\u003csup\u003e1\u003c/sup\u003e). Panels (g–i) are the same as (a–c) but present vertical motions (shading; Pa s⁻\u003csup\u003e1\u003c/sup\u003e) and temperature deviation (black contours; K). Panels (j–l) are the same as (a–c) but display relative humidity (shading; %) and meridional wind (black contours; m s⁻\u003csup\u003e1\u003c/sup\u003e). The deviation is calculated from 6°W to 6°E.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/d20110e93e5d126b25a8c9da.png"},{"id":81960384,"identity":"81ac9bf1-f7a4-457e-b0b3-b6bdfee919ae","added_by":"auto","created_at":"2025-05-05 10:41:44","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2765834,"visible":true,"origin":"","legend":"\u003cp\u003eLagrangian composite of geopotential height (black contours; gpm), horizontal wind speed (blue contours; m s⁻\u003csup\u003e1\u003c/sup\u003e), and vorticity (shading; 10⁻\u003csup\u003e5\u003c/sup\u003e s⁻\u003csup\u003e1\u003c/sup\u003e) at 250 hPa for the quasi-stationary (a), non-vacating (b), and vacating TMVs (c). Purple boxes denote the central regions of TMVs. Panels (d–f) are the same variables as (a–c) but at 500 hPa. The thick orange dashed line in (c) represents the trough line.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/ec05be0b50b6e61d0e2fe581.png"},{"id":81959686,"identity":"765633e8-ad4f-49e2-b42c-01001c3a97b5","added_by":"auto","created_at":"2025-05-05 10:33:44","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":3895925,"visible":true,"origin":"","legend":"\u003cp\u003eLagrangian composite of the horizontally averaged (within a 2°\u0026nbsp;×\u0026nbsp;2° box centered on the composite centers of TMVs) vorticity budget terms (10⁻\u003csup\u003e10\u003c/sup\u003e s⁻\u003csup\u003e2\u003c/sup\u003e), from \u003cem\u003et\u003c/em\u003e-6 to \u003cem\u003et\u003c/em\u003e. The green lines represent the averaged top levels of TMVs, the purple lines indicate the averaged bottom levels, and the gray shading indicates the referenced heights for the quasi-stationary and non-vacating TMVs.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/f3ee5c3b0fe2b7a2fdf0fdb2.png"},{"id":81959325,"identity":"27b6fdcc-0e40-4e4d-aad0-55ceb8c8512b","added_by":"auto","created_at":"2025-05-05 10:25:44","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":4083306,"visible":true,"origin":"","legend":"\u003cp\u003eLagrangian composite of the horizontally averaged (within a 2°×2° box centered on TMV composite centers) vorticity for quasi-stationary (a), non-vacating (b), and vacating (c) types from \u003cem\u003et\u003c/em\u003e-6 to \u003cem\u003et\u003c/em\u003e, with \u003cem\u003et\u003c/em\u003e representing the TMV formation time. Panels (d–f) show the same data as (a–c) but for divergence (10⁻\u003csup\u003e5\u003c/sup\u003e s⁻\u003csup\u003e1\u003c/sup\u003e). Panels (g–i) show the same as (a–c) but for vertical motion (Pa s⁻\u003csup\u003e1\u003c/sup\u003e). The green lines represent the averaged top levels of TMVs, the purple lines indicate the averaged bottom levels of TMVs, and the gray shading indicates the referenced heights for the quasi-stationary and non-vacating TMVs.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/b49d7b63691ff8b8e9aaed4a.png"},{"id":81958376,"identity":"33b7f0d6-0a41-47bd-a524-b1cf59b44042","added_by":"auto","created_at":"2025-05-05 10:17:44","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":3383552,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 15 but displays the terms for stretching, tilting, horizontal advection, and vertical advection (10⁻\u003csup\u003e10\u003c/sup\u003e s⁻\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/d10c4d29513e6183a0679916.png"},{"id":87842809,"identity":"2bf18d33-58aa-4c7b-b820-763b8b594ad3","added_by":"auto","created_at":"2025-07-29 14:25:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":75325605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6436848/v1/bee63584-104c-47e0-854c-1c0940bbaa68.pdf"}],"financialInterests":"","formattedTitle":"Key statistical features and common formation mechanisms of mesoscale vortices over Tibetan Plateau: A 42-warm-season analysis based on ERA5 data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAccording to American Meteorological Society (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://glossary.ametsoc.org/wiki/Vortex\u003c/span\u003e\u003cspan address=\"https://glossary.ametsoc.org/wiki/Vortex\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a vortex usually refers to a compact flow that circulates around an axis, characterized by a local extremum in vorticity (only cyclonic vorticity is considered in this study). Vortices are common weather systems and can occur globally (Neu et al. 2013; Fu et al. 2020). Vortices with a horizontal scale of 2\u0026ndash;2000 km are typically classified as mesoscale vortices (Orlanski 1975; Fu et al. 2020). For decades, mesoscale vortices have been a key focus of research owing to their close association with various types of hazardous weather. These include heavy rainfall (Bartels and Maddo 1991; Trier and Davis 2002), hail (Tessendorf et al. 2005), lightning (Bovalo et al. 2014; Fierro and Mansell 2018), strong winds (Evans et al. 2014; Grunzke et al. 2017), dust storms (Qian et al. 2002; Huang et al. 2016), and blizzards (Zhang et al. 2012).\u003c/p\u003e \u003cp\u003eThe Tibetan Plateau, often referred to as the \u0026ldquo;Third Pole\u0026rdquo; and the \u0026ldquo;Asian Water Tower\u0026rdquo; (Zhao et al. 2018), is the highest plateau in the world and significantly influences regional weather and climate (Yeh and Gao 1979; Yanai et al. 1992; Duan and Wu 2005; Duan et al. 2018; Liu et al. 2020; Wu et al. 2020). During warm seasons, the Tibetan Plateau serves as a large heat source (Yeh and Gao 1979; Li et al. 2014a; Jiang et al.2016; Mai et al. 2021), which creates favorable conditions for the formation of mesoscale vortices (Li et al. 2014b; Fu et al. 2019, 2021a). Notably, the Tibetan Plateau mesoscale vortices (TMVs) are particularly significant, with the Tibetan Plateau vortices being the most recognized (Feng et al. 2014). Tibetan Plateau vortices typically have a lifespan of \u0026ge;\u0026thinsp;6 hr (Li et al. 2014c; Curio et al. 2019; Fu et al. 2019; Lin et al. 2020) and significantly contribute to the total precipitation over the plateau (Fu et al. 2021b; Lin et al. 2021). Previous studies (Tao and Ding 1981; Curio et al. 2019; Li et al. 2014a, b; Chen et al. 2019; Lin et al. 2020; Li et al.2020b) have shown that the effects of Tibetan Plateau vortices extend beyond the plateau. Under favorable conditions, some of these vortices can exit the plateau and trigger heavy rainfall in downstream regions. Notably, the catastrophic flood events in the Yangtze River Basin in 1998 and the Huang-Huai River Basin in 2003 were closely associated with Tibetan Plateau vortices (Tan et al. 2013; Fu et al. 2021b).\u003c/p\u003e \u003cp\u003eFor decades, numerous studies have investigated TMVs, with a significant focus on Tibetan Plateau vortices. These studies have mainly explored four key aspects. First, Lu et al. (1984) examined the structural features of Tibetan Plateau vortices and found that some vortices exhibited a symmetric warm core, with a cold core near the surface. Qiao et al. (1994) analyzed satellite cloud imagery and found that Tibetan Plateau vortices displayed a spiral cloud pattern similar to that of tropical cyclones. Additionally, Li et al. (2019) and Fu et al. (2019) demonstrated that Tibetan Plateau vortices exhibited upper-level divergence and lower-level convergence. Second, Li et al. (2011), Li et al. (2014a), and Lin et al. (2023, 2024) investigated the formation and development mechanisms of Tibetan Plateau vortices. The findings revealed that convergence, wind shear, latent heat, and sensible heat were crucial factors in the formation of Tibetan Plateau vortices. Tang et al. (2023a) further highlighted that in warmer and wetter environments, latent heat often plays a more dominant role in the formation of these vortices. Qian et al. (1984), Shen et al. (1986), Dell'Osso and Chen (1986), Wang (1987), Li et al. (2014a), Zhang et al. (2019a), Ma et al. (2022), and Wu et al. (2022) and Dong et al. (2024) compared the relative importance of latent heat and sensible heat in Tibetan Plateau vortex formation. The findings indicated that the significance of these factors varies based on the specific scenario. Third, Yu et al. (2007), Yu et al. (2009), Li et al. (2011), Li et al. (2014a), Fu et al. (2019), and Ma et al. (2022) assessed the mechanisms driving the eastward displacement of vortices. The results indicated that the favorable conditions for the eastward movement of Tibetan Plateau vortices included a strong upper-level jet, an eastward-extended strong South Asian High, a westward-extended strong western Pacific subtropical high, and abundant moisture transport. Fourth, Lin (2015), Huang et al. (2018), Curio et al. (2019), and Lin et al. (2020) evaluated the statistical characteristics of Tibetan Plateau vortices and found that their annual mean occurrence frequency ranged from 53 to 269. Wang et al. (2009) observed significant interdecadal, interannual, and seasonal variations in the frequency of these vortices. Yu and Gao (2006) indicated that Tibetan Plateau vortices forming east of 92\u0026deg;E had a higher probability of exiting the plateau.\u003c/p\u003e \u003cp\u003ePrevious studies have elucidated various key features of TMVs. However, several significant limitations persist: (i) No statistical studies have examined the shorter-lived (\u0026lt;\u0026thinsp;6 hr) TMVs; (ii) the vertical extent and formation mechanisms of different TMV types remain underexplored; (iii) the peak in the hourly diurnal variation of TMV occurrence frequency has not been identified, and the underlying mechanisms remain unclear. Addressing these knowledge gaps will provide comprehensive insights into precipitation and mesoscale weather systems over and around the Tibetan Plateau. To date, no studies have utilized hourly ERA5 reanalysis data (Hersbach et al. 2020) to investigate the long-term statistical characteristics of TMVs. Nevertheless, ERA5 provides the highest spatiotemporal resolution (i.e., hourly/0.25\u0026deg;) and effectively describes the atmosphere over and around the Tibetan Plateau (Zhang et al. 2019b; Huang et al. 2021; Xin et al. 2022; Lin et al. 2023). Iin view of this, this study aims to address these knowledge gaps by using hourly ERA5 data. The paper is structured as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the data and methodology. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e examines the basic climatological features of TMVs. Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e4\u003c/span\u003e explores the vertical extents, tracks, and precipitation associated with TMVs. Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e5\u003c/span\u003e highlights the composite features. Section \u003cspan refid=\"Sec23\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes the study and discusses its findings.\u003c/p\u003e"},{"header":"2 Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis study utilizes hourly ERA5 reanalysis data with a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; (Hersbach et al. 2020) for 42 warm seasons (May\u0026ndash;September; 1979\u0026ndash;2020) to identify and track TMVs. The data are also used to analyze the background circulations, three-dimensional structures, and formation mechanisms of TMVs. The spatial resolution of the dataset used to identify mesoscale vortices is crucial. High-resolution datasets provide a significant advantage over low-resolution datasets, as they can effectively capture the vortex structure (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This study utilizes a total of 10 variables: zonal wind, meridional wind, vertical velocity, geopotential height, temperature, specific humidity, vorticity, divergence at 37 pressure levels, total precipitation, and 2-m surface temperature.\u003c/p\u003e \u003cp\u003eThe IMERG final precipitation L3 product, generated by the latest version of the Global Precipitation Measurement (GPM) Program (Huffman et al. 2019), provides a half-hourly resolution of 0.1\u0026deg; \u0026times; 0.1\u0026deg;. This product is used to analyze precipitation associated with TMVs. The GPM data are aggregated within each hour to generate hourly precipitation, ensuring consistency with the temporal resolution of the ERA5 data. Because the GPM data do not cover the entire study period, precipitation analysis is based on data from 20 warm seasons (2001 to 2020). Although GPM may underestimate light rain amounts and the intensity of extremely heavy rainfall, it accurately captures key precipitation features over the Tibetan Plateau, with a false alarm ratio of ~\u0026thinsp;14% and a missing data ratio of ~\u0026thinsp;13% (Ma et al. 2016; Xu et al. 2017; Zhang et al. 2018b). Moreover, a comparison of the data from the GPM program and Tropical Rainfall Measuring Mission (3B42 V7) indicates that GPM data provide more accurate precipitation patterns over the Tibetan Plateau (Ma et al. 2016; Zhang et al. 2018b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Detection and tracking algorithm\u003c/h2\u003e \u003cp\u003eWe detect TMVs using the following procedure: (i) Calculate the restricted vorticity[1]\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e (Fu et al.2020) and regard those\u0026thinsp;\u0026ge;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e as candidate vortex centers; (ii) The quadrant-averaged wind is calculated using a 100 km radius based on these candidate vortex centers. Only the vortex centers with a cyclonic quadrant-averaged wind are retained as valid vortex structures. To track TMVs horizontally, we use the neighborhood searching method (Blender et al. 1997). If the distance between the centers of the valid vortex structures at times \u003cem\u003et\u003c/em\u003e and \u003cem\u003et\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1 (where \u0026ldquo;1\u0026rdquo; denotes 1 hr) is \u0026le;\u0026thinsp;300 km, they are considered part of the same TMV. Otherwise, the tracking is terminated. Higher temporal resolution data are more effective in capturing the continuous evolution of a vortex. For example, Vortex I dissipates at \u003cem\u003et\u003c/em\u003e + ∆\u003cem\u003et\u003c/em\u003e (where ∆\u003cem\u003et\u003c/em\u003e denotes the temporal resolution), and Vortex II forms near Vortex I (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). Higher temporal resolution data can detect the dissipation of Vortex I, while lower temporal resolution data cannot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This misjudgment increases the likelihood of identifying Vortex II for Vortex I, leading to an incorrect extension of Vortex I lifespan. The vertical extent of TMVs is determined using the following procedure: (i) At 500 hPa, we examine both higher and lower pressure levels in increments of 50 hPa. If the horizontal distance between the vortex centers at two adjacent levels is \u0026le;\u0026thinsp;300 km, they are considered part of the same TMV; (ii) We analyze all continuous vertical levels with vortex structures related to the same TMV to determine the vertical extent of TMVs (Fu et al. 2016, 2022).\u003c/p\u003e \u003cp\u003eTang et al. (2023b) detected and tracked TMVs and then manually adjusted each TMV to improve result accuracy. After these corrections, a total of 15,644 TMVs were identified across the 42 warm seasons. Approximately 14% of TMVs identified and tracked by the algorithm did not match those manually identified. Further details on the algorithm are provided in Tang et al. (2023b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Parameters and classifications of vortices\u003c/h2\u003e \u003cp\u003eIn this study, we mainly use the following vortex parameters: (i) Formation/dissipation indicates the first/last time a TMV is detected; (ii) lifespan signifies the period between the formation and dissipation of TMVs; (iii) occurrence frequency denotes the total number of TMVs; (iv) influence frequency indicates the total number of instances in which TMVs are detectable; (v) location represents the center of TMVs; (vi) moving distance denotes the distance between the formation and dissipation locations of TMVs; (vii) top/bottom levels represent the minimum/maximum pressure levels within the TMV vertical extent; (viii) thickness indicates the difference between the top and bottom levels of TMVs; (ix) central region is defined as a 2\u0026deg; \u0026times; 2\u0026deg; box centered on the TMV location; (x) intensity is evaluated as the maximum vorticity within the TMV central region over its lifespan; (xi) TMV-related precipitation is defined as precipitation (\u0026ge;\u0026thinsp;0.1 mm hr⁻\u003csup\u003e1\u003c/sup\u003e) within a 300 km radius of the vortex center (Fu et al. 2021a).\u003c/p\u003e \u003cp\u003eAll TMVs analyzed in this study have a minimum lifespan of 1 hr and are detectable from at least two consecutive hourly ERA5 data points. This approach differs from those used in previous studies for detecting/tracking Tibetan Plateau vortices (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By utilizing hourly ERA5 data and setting a minimum lifespan threshold of 1 hr, we can identify TMVs with lifespans shorter than 6 hr, which would be undetectable using reanalysis datasets with a temporal interval of \u0026ge;\u0026thinsp;6 hr. Therefore, TMVs are classified into two groups based on their lifespans: shorter-lived (lifespan\u0026thinsp;\u0026lt;\u0026thinsp;6 hr) and longer-lived TMVs (lifespan\u0026thinsp;\u0026ge;\u0026thinsp;6 hr). Out of the total 15,644 TMVs, 10,691 are classified as shorter-lived, while 4,953 are classified as longer-lived (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among the longer-lived TMVs, 1,898 are categorized as moving type, as the distance between their formation and dissipation locations is \u0026ge;\u0026thinsp;200 km. The remaining TMVs are classified as quasi-stationary. The moving type TMVs are further classified into vacating type (54 TMVs) and non-vacating type (1,844 TMVs), based on whether the vortex dissipates outside the Tibetan Plateau.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters for detecting and tracking Tibetan Plateau vortices in different studies. \u0026ldquo;Auto\u0026rdquo; denotes the use of identification algorithms. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003eZ\u003c/em\u003e, and \u003cem\u003eW\u003c/em\u003e represent relative vorticity, geopotential height, and horizontal wind at 500 hPa, respectively. \u0026ldquo;/\u0026rdquo; indicates that no parameter is used.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData (Variable)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTemporal (spatial) resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum lifespan (Radius)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003cp\u003eperiod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnnual mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProportion of vacating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWang et al. (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeather charts (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 hr\u003c/p\u003e \u003cp\u003e(/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 hr (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1980\u0026ndash;2004 (May-Sep)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi (2012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeather charts (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 hr\u003c/p\u003e \u003cp\u003e(/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 hr (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1980\u0026ndash;2000 (except1982) (May-Sep)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDecrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTang et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYearbooks (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 hr\u003c/p\u003e \u003cp\u003e(/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/ (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1998\u0026ndash;2011 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al. (2014c)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(2.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 hr (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1981\u0026ndash;2010 (Jun-Aug)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeng et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 hr (100 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000\u0026ndash;2009 (April-Oct)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin (2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA-interim (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr\u003c/p\u003e \u003cp\u003e(200 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2013 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDecrease (2/10a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuang\u003c/p\u003e \u003cp\u003eet al. (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA-interim (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr\u003c/p\u003e \u003cp\u003e(200 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2016 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease (3/10a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang\u003c/p\u003e \u003cp\u003eet al. (2018a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003er (0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 hr (200 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1981\u0026ndash;2010 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDecrease (4/10a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuan and Li (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 hr (200 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2016 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease (3/10a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurio\u003c/p\u003e \u003cp\u003eet al. (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA-interim (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24 hr (200 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1979\u0026ndash;2015 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLi et al. (2020a)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFNL (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e/ (/)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2000\u0026ndash;2015 /\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA-interim (\u003cem\u003eZ\u003c/em\u003e; \u003cem\u003eW\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.7\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eLin et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA-interim (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr (145 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2017 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA40 (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr (140 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1958\u0026ndash;2001 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJRA55 (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(1.25\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr (60 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1958\u0026ndash;2017 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFSR (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr (170 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2017 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMERRA2 (\u003cem\u003eZ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 hr\u003c/p\u003e \u003cp\u003e(0.5\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 hr (155 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1980\u0026ndash;2017 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERA5 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 hr (0.25\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 hr (100 km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;2020 (Jan-Dec)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e118\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;6 hr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease (10/10a)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Composite methods\u003c/h2\u003e \u003cp\u003eThis study utilizes two composite methods: Eulerian and Lagrangian composites. The Eulerian composite is used to compute the arithmetic mean of meteorological fields in their original coordinates and analyze the larger-scale background circulations of TMVs. The Lagrangian composite is used to calculate the arithmetic mean of meteorological fields relative to the TMV centers. In this method, the centers of all TMVs are overlapped and serve as the origin of the coordinate system. The Lagrangian composite is mainly used to investigate the three-dimensional structures and common formation mechanisms of TMVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Vorticity budget\u003c/h2\u003e \u003cp\u003eThe vorticity budget in the pressure coordinate system (Kirk 2003; Fu et al. 2017) is used to investigate the formation mechanisms of different types of TMVs. The expression is as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:\\zeta\\:}{\\partial\\:t}=-{\\mathbf{V}}_{\\text{h}}\\cdot\\:{\\nabla\\:}_{\\text{h}}\\zeta\\:-\\beta\\:v-\\omega\\:\\frac{\\partial\\:\\zeta\\:}{\\partial\\:p}+\\mathbf{k}\\cdot\\:\\left(\\frac{\\partial\\:{\\mathbf{V}}_{\\text{h}}}{\\partial\\:p}\\times\\:{\\nabla\\:}_{\\text{h}}\\omega\\:\\right)-\\left(\\zeta\\:+f\\right){\\nabla\\:}_{\\text{h}}\\cdot\\:{\\mathbf{V}}_{\\text{h}}+\\)\u003c/span\u003e \u003c/span\u003e residual effect (1),\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e denotes the relative vorticity (hereinafter referred to as vorticity); \u003cem\u003et\u003c/em\u003e signifies time; (\u003cb\u003ei\u003c/b\u003e, \u003cb\u003ej\u003c/b\u003e, \u003cb\u003ek\u003c/b\u003e) represent the unit vectors in the zonal, meridional, and vertical directions, respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathbf{V}}_{\\text{h}}=u\\mathbf{i}+v\\mathbf{j}\\)\u003c/span\u003e\u003c/span\u003e indicates the horizontal wind vector, with the subscript \u0026ldquo;h\u0026rdquo; representing the horizontal component; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\nabla\\:}_{\\text{h}}=\\frac{\\partial\\:}{\\partial\\:x}\\mathbf{i}+\\frac{\\partial\\:}{\\partial\\:y}\\mathbf{j}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:=\\frac{\\partial\\:f}{\\partial\\:y}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003ef\u003c/em\u003e denotes the Coriolis parameter; \u003cem\u003eω\u003c/em\u003e denotes vertical velocity in the pressure coordinate system; \u003cem\u003ep\u003c/em\u003e represents pressure.\u003c/p\u003e \u003cp\u003eThe term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\partial\\:\\zeta\\:}{\\partial\\:t}\\)\u003c/span\u003e\u003c/span\u003e represents the local temporal derivative of vorticity; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-{\\mathbf{V}}_{\\text{h}}\\cdot\\:{\\nabla\\:}_{\\text{h}}\\zeta\\:\\)\u003c/span\u003e\u003c/span\u003e denotes the horizontal advection of vorticity; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\beta\\:v\\)\u003c/span\u003e\u003c/span\u003e indicates the advection of planetary vorticity, which is typically one order of magnitude smaller than the other terms (Fu et al. 2013); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\omega\\:\\frac{\\partial\\:\\zeta\\:}{\\partial\\:p}\\)\u003c/span\u003e\u003c/span\u003e represents the vertical advection of vorticity; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{k}\\cdot\\:\\left(\\frac{\\partial\\:{\\mathbf{V}}_{\\text{h}}}{\\partial\\:p}\\times\\:{\\nabla\\:}_{\\text{h}}\\omega\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e corresponds to the tilting effect, which converts horizontal vorticity into vertical vorticity; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\left(\\zeta\\:+f\\right){\\nabla\\:}_{\\text{h}}\\cdot\\:{\\mathbf{V}}_{\\text{h}}\\)\u003c/span\u003e\u003c/span\u003e indicates the stretching effect. Finally, the residual effect represents the combined effects of friction, subgrid processes, and calculation errors. We define the overall effect as the sum of all terms on the right-hand side of Eq.\u0026nbsp;(1), excluding the residual effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Basic climatological characteristics","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial distribution\u003c/h2\u003e \u003cp\u003eA total of 15,644 TMVs are identified over the 42 warm seasons, with an average occurrence frequency of 372 per warm season. The influence frequency of TMVs reaches 99,090, accounting for nearly half of the total duration of the warm season on average. TMVs can form in nearly any region of the Tibetan Plateau, with over 80% occurring west of 95\u0026deg;E (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The highest occurrence frequency is observed between the Kunlun and Gangdise Mountains (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Additionally, the western section of the Qaidam Basin and the areas north of the Tanggula Mountains exhibit high occurrence frequencies. To quantify the spatial distribution of TMVs, we calculate the zonally and meridionally accumulated occurrence frequencies at 1\u0026deg; intervals. A peak in the zonally accumulated occurrence frequency occurs within the band of 33\u0026ndash;34\u0026deg;N, mainly owing to TMVs over the western section of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). For the meridionally accumulated occurrence frequency, a peak is observed around 89\u0026deg;E, with three secondary peaks around 81\u0026deg;E, 84\u0026deg;E, and 98\u0026deg;E. The correlation between the zonally/meridionally accumulated occurrence frequency and the zonally/meridionally averaged terrain height is ~\u0026thinsp;0.84/0.53 (exceeding the 99% confidence level), indicating that TMV formation is highly terrain-dependent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eA comparison between Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee indicates that the overall distribution of influence frequency is similar to that of occurrence frequency, as the moving type accounts for only\u0026thinsp;~\u0026thinsp;12.1% of all TMVs. Because some TMVs can exit the Tibetan Plateau, surrounding areas outside the plateau\u0026mdash;particularly the northeastern, eastern, and southeastern sections\u0026mdash;are affected by TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal variations\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3.2.1 Annual features\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eOccurrence frequencies of all TMVs, longer-lived TMVs, quasi-stationary type, and moving types exhibit significant annual variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with a 42-year mean occurrence frequency of ~\u0026thinsp;372, ~118, ~\u0026thinsp;73, and ~\u0026thinsp;45, respectively. All TMVs exhibit the highest occurrence frequency in 1987 and 1999 (422), while the longer-lived TMVs exhibits maximum occurrence frequency in 2018 (174). The quasi-stationary TMVs achieves peak occurrence frequency in 2018 (118). The moving TMVs exhibits peak occurrence in 1999, 2002, 2008, and 2016 (61). All TMVs, longer-lived TMVs, quasi-stationary type, and moving type exhibit the lowest occurrence frequencies in 1984 (290), 1983 (73), 1983 (39), and 1981 (29), respectively. The Mann\u0026ndash;Kendall trend test (Wei, 2007) reveals significantly increasing trends for longer-lived, quasi-stationary, and moving TMVs (exceeding the 99% confidence level). However, no significant linear trend is observed for all TMVs, as the shorter-lived TMVs do not exhibit a significant trend (not shown). The longer-lived TMVs exhibit the highest increase in occurrence frequency (~\u0026thinsp;1 TMV per warm season, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), while the moving TMVs exhibit the slowest increase in occurrence frequency (~\u0026thinsp;0.3 TMV per warm season, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The occurrence frequency of the quasi-stationary and moving TMVs exhibits a significant positive correlation with the annual mean surface temperature of the Tibetan Plateau (exceeding the 95% confidence level). This indicates that the warming of the plateau (~\u0026thinsp;0.4\u0026deg;C per decade, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) is closely related to the increase in these TMVs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3.2.2 Monthly features\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eThe occurrence frequency of TMVs exhibits significant monthly variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). For all TMVs, including the quasi-stationary and moving types, the occurrence frequency increases from May to June, peaks in July, and then decreases from July to September, with the lowest frequency occurring in September. These monthly variations in TMV occurrence are closely related to changes in the stream field over the Tibetan Plateau. In June, July, and August, convergence lines form and persist in the western section of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec\u0026ndash;e), which promotes TMV formation through convergence-related cyclonic vorticity production (Fu et al. 2019). Consequently, more TMVs form near these convergence lines than in May and September. In contrast, the stream fields over the eastern section of the Tibetan Plateau and the mean formation locations of TMVs do not exhibit significant monthly variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u0026ndash;f).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e\u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003e3.2.3 Diurnal variations\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eWe use hourly ERA5 data to investigate the hourly diurnal variation features of TMVs, which have rarely been addressed in previous studies. For longer-lived TMVs, both quasi-stationary and moving types, vortices form between 14:00 LST of the previous day and 03:00 LST (i.e., from afternoon to early morning), which account for \u0026ge;\u0026thinsp;60% of occurrences in each category (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Owing to the significantly higher occurrence frequency of the quasi-stationary type compared with the moving type, the diurnal variation features of the longer-lived TMVs are similar to those of the quasi-stationary type. The maximum occurrence frequencies for longer-lived TMVs and the quasi-stationary type occur around 21:30\u0026ndash;22:30 LST, while the peak occurrence for the moving TMVs occurs around 18:30\u0026ndash;19:30 LST. To explore the potential mechanisms driving these diurnal variations, we investigate factors such as 2-m temperature, total precipitation (related to latent heating), surface sensible heat, surface latent heat, convective available potential energy (CAPE), vertical velocity, and 200-hPa divergence. These factors can influence TMV formation through thermodynamic and/or dynamical forcings (Fu et al. 2019, 2021b).\u003c/p\u003e \u003cp\u003eThe diurnal variations of 2-m temperature and total precipitation, calculated at the formation times of the quasi-stationary and moving TMVs, exhibit similar patterns to those observed throughout the entire study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed) but with greater intensity. This indicates that TMVs tend to form in environments characterized by warmer surface temperatures and higher precipitation. Generally, the 2-m temperature reaches its peak between 14:00 and 16:00 LST (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), which destabilizes the boundary layer. Moreover, CAPE reaches its peak between 12:30 and 15:30 LST (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The combination of warmer surface temperatures, an unstable boundary layer, and higher CAPE promotes convection and rainfall. Consequently, total precipitation reaches its peak between 14:00 and 18:00 LST (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), consistent with the maximum upward motions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). These motions are mainly driven by latent heating from precipitation. Stronger upward motions are associated with more intense low-level convergence owing to fluid continuity. This convergence generates cyclonic vorticity through vertical stretching, which causes the peak occurrence frequencies of the moving and quasi-stationary TMVs to occur 4\u0026ndash;7 hr later (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe diurnal variations of 200-hPa divergence for the moving and quasi-stationary TMVs exhibit similar patterns to those over the Tibetan Plateau but with significantly higher intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This suggests that TMVs typically form in environments with strong upper-tropospheric divergence, which aids in sustaining or enhancing upward motions (Fu et al. 2021b). The 200-hPa divergence reaches its peak between 16:30 and 17:30 LST, ~\u0026thinsp;1 hr after the peak of total precipitation and 2\u0026ndash;5 hr before the occurrence frequency peaks for the moving and quasi-stationary TMVs.\u003c/p\u003e \u003cp\u003eThe diurnal variation of 500-hPa vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) for the moving and quasi-stationary TMVs exhibits similar patterns but significantly differs from the climate mean. Key points are as follows: (i) Both the moving and quasi-stationary TMVs exhibit two peaks (22:30\u0026ndash;23:30 LST for the former and 00:30\u0026ndash;01:30 LST for the latter). However, the climate mean displays only one peak (20:30\u0026ndash;21:30 LST); (ii) the cyclonic vorticity for the moving and quasi-stationary TMVs is significantly larger than the climate mean. Overall, the peak times of occurrence frequencies for the moving and quasi-stationary TMVs are consistent with the peak time of the climate mean 500-hPa vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). This suggests that the diurnal variation of dynamical features over the Tibetan Plateau is crucial for TMV formation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Features related to lifespans\u003c/h2\u003e \u003cp\u003eAmong the 15,644 TMVs detected over the 42 warm seasons, ~ 90% have a lifespan of less than 12 hr (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), with only\u0026thinsp;~\u0026thinsp;2% having a lifespan of 24 hr or more. Shorter-lived TMVs, which are difficult to detect using reanalysis data with a 6-hr or longer temporal interval, comprise up to 68.2% of all TMVs. This contributes to the higher occurrence frequency of TMVs detected in this study compared with previous studies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Overall, TMVs occurrence frequencies decrease rapidly as their lifespans increase, with the mean and median lifespans of ~\u0026thinsp;5.3 and 3.1 hr, respectively (left column of Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lifespans of TMVs exhibit a strong positive correlation with their moving distances, with a correlation coefficient of ~\u0026thinsp;0.8, which exceeds the 99.9% confidence level (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). This indicates that TMVs with longer lifespans tend to travel greater distances. Most TMVs exhibit quasi-stationary behavior, with an average moving distance of ~\u0026thinsp;80 km, and over 75% of TMVs travel less than 100 km. Although TMVs with longer lifespans or greater moving distances comprise only a small proportion of all TMVs, they are often associated with more severe disasters compared with typical TMVs (Fu et al. 2019, 2021b). Similarly, the intensities of TMVs exhibit a significant positive correlation with their lifespans, with a correlation coefficient of ~\u0026thinsp;0.4, which exceeds the 99.9% confidence level (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). This indicates that TMVs with longer lifespans exhibit greater intensities. TMVs feature mean and median intensities of ~\u0026thinsp;2.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and ~\u0026thinsp;1.9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe lifespans of the three TMV types vary significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). On average, the vacating TMVs exhibit the longest lifespan (~\u0026thinsp;26.7 hr), followed by the non-vacating TMVs (~\u0026thinsp;15.4 hr). However, the quasi-stationary TMVs have the shortest lifespan (~\u0026thinsp;8.7 hr). The longest lifespan observed among the non-vacating TMVs is ~\u0026thinsp;108 hr, which is the maximum recorded across all TMV types. Nearly all quasi-stationary TMVs (~\u0026thinsp;99.5%) and most non-vacating TMVs (~\u0026thinsp;85.5%) have lifespans of less than 24 hr. In contrast, ~\u0026thinsp;47.0% of vacating TMVs have a longer lifespan of 24 hr or more (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Characteristics of vertical extents, tracks, and precipitation","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Vertical extent features\u003c/h2\u003e \u003cp\u003eThe vertical extent is a crucial characteristic of TMVs but has been rarely investigated in previous studies. For all TMVs, over 60% of them have a highest top-level (i.e., the highest top-level during a TMV\u0026rsquo;s entire lifespan) around 500 hPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea), whereas, those TMVs with a highest top-level higher than 400 hPa account for \u0026lt;\u0026thinsp;5%. Overall, the number of TMVs significantly decreases as their highest top levels become higher. A comparison between shorter-lived TMVs and other types reveals that the former have a larger proportion for the vortices with a highest top-level around 500 hPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). This indicates that TMVs with longer lifespans are more likely to have higher top levels.\u003c/p\u003e \u003cp\u003eGiven that the Tibetan Plateau has a mean altitude of ~\u0026thinsp;4000 m, the surface pressure typically falls within the 500\u0026ndash;550 hPa range. Because most TMVs remain confined to the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the lowest bottom-levels (i.e., the lowest bottom-level during a TMV\u0026rsquo;s entire lifespan) of \u0026gt;\u0026thinsp;99% of the TMVs are also within this range (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Only the vacating TMVs exhibit bottom levels extending downward to 600 hPa or lower. Comparisons between shorter-lived TMVs and other types reveal that shorter-lived TMVs contain fewer vortices with a lowest bottom -level around 550 hPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAmong all TMVs, over 40% exhibit a maximum thickness of ~\u0026thinsp;50 hPa, making this the most common category (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), while only 32.7% feature a maximum thickness of less than 50 hPa. This indicates most TMVs belong to the shallow (in vertical extent) type of mesoscale vortex. TMVs with a thickness of 100 hPa or more comprise\u0026thinsp;~\u0026thinsp;23.7% of all TMVs, while TMVs with a thickness of 200 hPa or greater account for only\u0026thinsp;~\u0026thinsp;3.6%. A comparison between shorter-lived TMVs and other types indicates that the shorter-lived TMVs contain fewer vortices with thicknesses exceeding 50 hPa. This suggests that TMVs with longer lifespans tend to have greater vertical thickness, as their top altitudes are higher and bottom altitudes are lower.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Track features\u003c/h2\u003e \u003cp\u003eThe impacts of the quasi-stationary and moving TMVs are mainly concentrated over the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea\u0026ndash;b). For the quasi-stationary TMVs, regions with high influence frequency are mainly located in the western, central, and eastern sections of the plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea). In contrast, the moving TMVs exhibit high influence frequencies within a zonally oriented band between 32\u0026deg; and 36\u0026deg;N (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). Compared with previous studies, this study identifies a significantly lower proportion of vortices exiting the Tibetan Plateau (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This difference is mainly attributed to the use of hourly ERA5 data, which provides significantly higher temporal resolution than the datasets used in previous research. The higher temporal resolution increases the occurrence frequency of vortices, enabling the detection of shorter-lived vortices. Additionally, the higher resolution leads to a lower number of vortices exiting the plateau, as datasets with lower temporal resolution are more likely to overestimate the lifespans of vortices, which may increase the number of the vacating type erroneously. As mentioned above, by using the hourly ERA5 data, the numerator for calculating the vacating type\u0026rsquo;s proportion decreases but the denominator increases, and thus, the vacating-type\u0026rsquo;s proportion is smaller in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUnlike the quasi-stationary and moving TMVs, ~\u0026thinsp;91% of the vacating TMV tracks originate from the eastern and northeastern sections of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). After exiting the plateau, vacating TMVs mainly affects regions near the plateau, with only a small proportion directly influencing on areas in Mongolia, India, and even the East China Sea. The movement directions of the vacating TMVs are closely associated with the steering flow at the 500-hPa level. For the vacating TMVs, ~\u0026thinsp;56% of the vortices move eastward (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee), and ~\u0026thinsp;30% move northeastward. The remaining vortices mainly move northward or southwestward, each accounting for ~\u0026thinsp;6%. Notably, no vortices move westward or southeastward. In contrast, the non-vacating TMVs exhibit significantly different movement patterns: (i) the eastward and northeastward moving types account for ~\u0026thinsp;63% and 10%, respectively; (ii) the westward and southeastward moving types comprise around 9% (ranked third) and 8% (ranked fourth), respectively; (iii) the southward/northward moving types comprises\u0026thinsp;~\u0026thinsp;1%, representing the smallest proportion within the non-vacating TMVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Precipitation features\u003c/h2\u003e \u003cp\u003eOver the Tibetan Plateau, the temporally averaged accumulated precipitation typically decreases from southeast to northwest (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea), with two peak centers located in the southeastern (\u0026ge;\u0026thinsp;1200 mm) and eastern (\u0026ge;\u0026thinsp;800 mm) sections of the plateau. In contrast, the TMV-related precipitation exhibits a different spatial distribution, with its maximum centers mainly located in the central band of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb). This region displays the highest TMV influence frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). The spatial mean of the temporally averaged TMV-related precipitation over the Tibetan Plateau is ~\u0026thinsp;37.8 mm per warm season, which is about an order of magnitude smaller than the warm-season accumulated precipitation (~\u0026thinsp;322.3 mm).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe contribution of TMVs to the total accumulated precipitation over the Tibetan Plateau differs in distribution compared with TMV-related precipitation (cf. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb and \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec). The maximum center of TMV contributions is mainly located in the northwestern section of the plateau, with high TMV influence frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). However, the warm-season accumulated precipitation in this area is relatively low (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). In ~\u0026thinsp;21.8% of the Tibetan Plateau area, TMV-related precipitation accounts for over 20% of the total warm-season accumulated precipitation. On average, TMV-related precipitation comprises\u0026thinsp;~\u0026thinsp;13.8% of the total warm-season accumulated precipitation over the Tibetan Plateau, which is significantly lower than the proportions observed in the northwestern Tibetan Plateau (\u0026ge;\u0026thinsp;36%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec). Precipitation associated with shorter-lived TMVs, the quasi-stationary type, and the moving type exhibits distinct spatial distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed\u0026ndash;f). Notably, the moving TMVs generate the highest accumulated precipitation, with the rainfall centers mainly located around the junction of Qinghai, Xizang, and Sichuan (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ef). In contrast, the quasi-stationary TMVs have the lowest accumulated precipitation, with rainfall centers mainly located in the southwestern section of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ee). Regarding rainfall contributions from shorter-lived TMVs, the quasi-stationary type, and the moving type, their maximum centers are mainly located in the northwestern section of the plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eg\u0026ndash;i). Among these, the moving TMVs have the highest contribution to rainfall, followed by the quasi-stationary TMVs and the shorter-lived TMVs. However, shorter-lived TMVs should not be overlooked, as in some regions of the plateau, their contribution to local rainfall exceeds 12% (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eg).\u003c/p\u003e \u003cp\u003eTo elucidate TMV contributions to different precipitation intensities, we categorize rainfall intensity into three groups based on the percentile distributions of historical hourly precipitation over the Tibetan Plateau: (a) weak precipitation (0.1 mm hr⁻\u003csup\u003e1\u003c/sup\u003e \u0026le; hourly precipitation\u0026thinsp;\u0026lt;\u0026thinsp;1 mm hr⁻\u003csup\u003e1\u003c/sup\u003e), (b) moderate precipitation (1 mm hr⁻\u003csup\u003e1\u003c/sup\u003e \u0026le; hourly precipitation\u0026thinsp;\u0026lt;\u0026thinsp;3 mm hr⁻\u003csup\u003e1\u003c/sup\u003e), and (c) heavy precipitation (hourly precipitation\u0026thinsp;\u0026ge;\u0026thinsp;3 mm hr⁻\u003csup\u003e1\u003c/sup\u003e). For all three groups, the contributions of TMVs to the warm-season accumulated precipitation reach their peak in the northwestern section of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ej\u0026ndash;l). With increasing rainfall intensity increases, the maximum contributions of TMV-related precipitation also increase from ~\u0026thinsp;36% in the weak group to ~\u0026thinsp;70% in the heavy group (the northwestern and eastern sections of the plateau). Overall, TMV-related precipitation accounts for ~\u0026thinsp;12.8% in the weak group, ~\u0026thinsp;14.2% in the moderate group, and ~\u0026thinsp;13.4% in the heavy group across the Tibetan Plateau.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Composite features","content":"\u003cp\u003eDuring the study period, the quasi-stationary, non-vacating, and vacating TMVs exhibit occurrence frequencies of 3055, 1844, and 54, respectively. Eulerian and Lagrangian composites are used to analyze the common features of these three types of TMVs.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Composite background circulations\u003c/h2\u003e \u003cp\u003eThe Eulerian composite is used to illustrate the common background circulations for the different types of TMVs. In the upper troposphere, the South Asian High maintains strong intensity at lower latitudes for all three types of TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea\u0026ndash;c), with an upper-level jet present in the middle latitudes. Notably, the South Asian High exhibits the largest coverage for the quasi-stationary TMVs and the smallest coverage for the vacating TMVs. The upper-level jets for the quasi-stationary and non-vacating TMVs are mainly located north of the Tibetan Plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea\u0026ndash;b), while the jet for the vacating TMVs is mainly positioned to the northeast of the plateau (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ec). Over 50% of the vacating TMVs form in regions beneath the upper-level jet, while less than 20% of both quasi-stationary and non-vacating TMVs form in these regions. This indicates that the upper-level steering flow is stronger for the vacating TMVs, which facilitates their movement away from the plateau.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the middle troposphere, the western Pacific subtropical high is mainly located in the southeast of China for all three types of TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ed\u0026ndash;f). West of the subtropical high, a shortwave trough appears over the southwestern section of the Tibetan Plateau. The southwesterly winds ahead of this trough facilitate moisture transport to the Tibetan Plateau, resulting in a relatively moist band over the plateau. Overall, the background circulations and mean formation locations are similar for the quasi-stationary and non-vacating TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ed\u0026ndash;e) but differ from those of the vacating TMVs. The key difference among the three types of TMVs is the presence of a shortwave trough over the eastern flank of the Tibetan Plateau for the vacating TMV, which is absent in the quasi-stationary and non-vacating TMVs. This shortwave trough creates favorable conditions for the formation of the vacating TMVs, with their average formation location occurring within the trough (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ef).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Composite three-dimensional structures\u003c/h2\u003e \u003cp\u003eLagrangian composites are used to illustrate the three-dimensional structures of TMVs at the time of their formation. To examine the structures of the vortices, assessing their vertical stretching is crucial. The quasi-stationary, non-vacating, and vacating TMVs exhibit average top and bottom levels of ~\u0026thinsp;479/~514, ~\u0026thinsp;480/~518, and ~\u0026thinsp;466/~531 hPa, respectively. Consequently, the average thickness of the quasi-stationary TMVs (~\u0026thinsp;35 hPa) is similar to that of the non-vacating TMVs (~\u0026thinsp;38 hPa) but significantly thinner than the average thickness of the vacating TMVs (~\u0026thinsp;65 hPa). All TMVs form over the Tibetan Plateau, which features significant altitude variations (ranging from 2600 to 5500 m). Therefore, to analyze the Lagrangian composite, it is crucial to account for the height of the Tibetan Plateau. Over 75% of the quasi-stationary and non-vacating TMVs form at terrain elevations higher than 4600 m, while over 75% of the vacating TMVs form at elevations above 2900 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ei). Therefore, only results at elevations above these reference heights (4600 m for the quasi-stationary and non-vacating TMVs and 2900 m for the vacating TMVs (as indicated by gray shading in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e) are considered valid for analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll three types of TMVs exhibit a cyclonic vorticity center and a negative geopotential height deviation center within their central regions (indicated by the purple dashed boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea\u0026ndash;c). Notably, the vacating TMVs exhibit the strongest cyclonic vorticity and geopotential height deviation. However, the quasi-stationary TMVs feature the weakest cyclonic vorticity. This indicates that the vacating TMVs exhibit the highest intensity, and the quasi-stationary TMVs feature the lowest intensity. This is further supported by a comparison of the 500-hPa cyclonic vorticity and geopotential height within their central regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ed\u0026ndash;f). Notably, the vacating TMVs feature the thickest air columns with cyclonic vorticity and negative geopotential height deviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea\u0026ndash;c), reflecting their greatest vertical extent. For all three TMV types, negative geopotential height deviations below 400 hPa are mainly associated with cyclonic vorticity. However, the three types display significant differences in the type of vorticity above 400 hPa. For the quasi-stationary and non-vacating TMVs, the negative geopotential height deviations are mainly related to anticyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea\u0026ndash;b), as their central regions are dominated by a high-pressure ridge (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ea\u0026ndash;b). For the vacating TMVs, the negative geopotential height deviations are mainly associated with cyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ec), as their central regions are dominated by a low-pressure trough (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eFor all three TMV types, strong convergence and divergence dominate the lower and upper levels within their central regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed\u0026ndash;f), respectively. These features enhance and maintain upward motions through mass continuity (Fu et al. 2019, 2022). Notably, the non-vacating TMVs exhibit the strongest lower-level convergence, while the vacating TMVs feature the weakest low-level convergence. For all three TMV types, strong ascent centers are mainly located above the strong low-level convergence centers and are closely associated with positive temperature deviations (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eg\u0026ndash;i). This indicates the key role of precipitation-related latent heating in generating these positive temperature deviations. Notably, the vacating TMVs exhibit the strongest positive temperature deviations and the highest rainfall intensity than the other two types of TMVs (not shown). Additionally, only the vacating TMVs exhibit a negative temperature-deviation center (indicating a cold pool) below the central region (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eg\u0026ndash;i). The absence of negative temperature-deviation centers in the quasi-stationary and non-vacating TMVs may be attributed to the strong surface heating from the Tibetan Plateau, as their referenced height is much higher than that of the vacating type (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ei).\u003c/p\u003e \u003cp\u003eThe air column with relative humidity\u0026thinsp;\u0026ge;\u0026thinsp;72% exhibits the greatest vertical extent for the vacating type (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ej\u0026ndash;l), indicating that the moisture conditions are most favorable for precipitation in this type. For all three TMV types, westerly winds dominate above 500 hPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ed-f) and are present in the southern section of the central region (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ed\u0026ndash;f). Compared with the quasi-stationary TMVs, the non-vacating and vacating TMVs exhibit a stronger westerly wind, which facilitates their movement (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eb\u0026ndash;c and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ee\u0026ndash;f). Additionally, the vacating TMVs feature the strongest intensities of both northerly and southerly winds, further suggesting that this type exhibits the highest vortex intensity among all three TMV types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Formation mechanisms\u003c/h2\u003e \u003cp\u003eTo elucidate the formation mechanisms of TMVs, we conduct a vorticity budget analysis on the central-region averaged vorticity within the mean vertical extent of the vortices, from t-6 (i.e., 6 hr before TMV formation) to t (i.e., the time of TMV formation). Before conducting a detailed analysis, we first assess the balance of the budget equation. A comparison between Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ea\u0026ndash;c and Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ed\u0026ndash;f indicates that for all TMV types, the local temporal derivative exhibits a pattern similar to the overall-effect\u0026rsquo;s behavior (Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee) in both vertical structures and magnitudes. Moreover, from \u003cem\u003et\u003c/em\u003e-6 to \u003cem\u003et\u003c/em\u003e, within the respective mean vertical extent of all TMV types, the ratios of local temporal derivatives to overall effects have a mean value greater than 72%, and the residual effect is significantly smaller than both the local temporal derivative and overall effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e). This indicates that the vorticity budget equation is well balanced, making it suitable for elucidating the formation mechanisms of the vortices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin the mean vertical extent of the three TMV types, the central-region averaged vorticity significantly increases from \u003cem\u003et\u003c/em\u003e-6 to \u003cem\u003et\u003c/em\u003e, corresponding to vortex formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ea\u0026ndash;c). Notably, the vacating TMVs exhibit the strongest cyclonic vorticity, with the slowest rate of increase. The non-vacating TMVs feature the second strongest cyclonic vorticity, with the highest rate of increase. The differences in the rates of increase across the three TMV types are further supported by the terms local temporal derivative and overall effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ea\u0026ndash;f). Although the vorticity budget distributions vary for each TMV type (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e), significant similarities emerge: (i) The stretching term is the main driver of the increase in cyclonic vorticity within the central regions of the vortices (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003ea\u0026ndash;c). This indicates that vertical stretching, driven by strong low-level convergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ed\u0026ndash;f), is the main factor in generating the cyclonic vorticity associated with TMVs. This finding is consistent with results from previous studies (Fu et al. 2019, 2021b). (ii) Vertical advection is the second most dominant factor (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003ej-l), as ascending motions in the central region of TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003eg\u0026ndash;i) transport cyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ea\u0026ndash;c) upward. (iii) Horizontal advection is the most detrimental factor, which decelerates the increase in cyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003eg\u0026ndash;i), as it causes a net export of cyclonic vorticity from the central regions of TMVs. Moreover, the tilting term contributes to the reduction in cyclonic vorticity within the central regions of TMVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003ed\u0026ndash;f), by generating anticyclonic vorticity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the vacating TMVs, the upper levels are dominated by cyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ec), which is associated with the upper-level low-pressure trough (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec). In contrast, the upper levels of quasi-stationary and non-vacating TMVs are dominated by anticyclonic vorticity (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ea\u0026ndash;b). Additionally, the vacating TMVs exhibit the strongest horizontal advection (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003eg\u0026ndash;i), mainly driven by the eastward transport of cyclonic vorticity and the westerly winds associated with the upper-level low-pressure trough (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003ec). Consequently, the upper-level cyclonic vorticity of vacating TMVs rapidly increases over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003ec), which is confirmed by the strong overall effect at upper levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ef). In contrast, the upper-level anticyclonic vorticity of quasi-stationary and non-vacating TMVs weakens over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003ed-e). Overall, more favorable dynamical conditions are crucial for the vacating process of TMVs.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion and discussion","content":"\u003cp\u003eDuring warm seasons, the Tibetan Plateau serves as a crucial source region for mesoscale vortices, which significantly influence rainfall over and around the plateau. Despite numerous studies on these vortices, none have investigated the shorter-lived TMVs, and no studies have ever investigated TMVs\u0026rsquo; vertical-extent features, common formation mechanisms, and hourly diurnal variations. In this study, we attempt to address these remaining scientific questions, which is helpful to reach a more comprehensive understanding of the precipitation and mesoscale weather systems over and around Tibetan Plateau.\u003c/p\u003e \u003cp\u003eOver 42 warm seasons, a total of 15,644 TMVs are identified (~\u0026thinsp;372 TMVs per warm season), which account for ~\u0026thinsp;50.2% of the total duration of a warm season on average. TMVs exhibit a significantly higher occurrence frequency than Tibetan Plateau vortices (Curio et al. 2019; Lin et al. 2020), as TMVs include vortices with a lifespan of \u0026le;\u0026thinsp;6 hr, which cannot be detected by datasets with a temporal resolution coarser than 6 hr. These vortices can form in nearly any region over the Tibetan Plateau, with over 80% of TMVs originating west of 95\u0026deg;E. The formation of TMVs is highly terrain-dependent, with higher altitude regions tend to promote the formation of more TMVs. TMVs account for ~\u0026thinsp;13.8% of the total accumulated precipitation over the Tibetan Plateau, which is lower than those reported by Curio et al. (2019) and Lin et al. (2021). This difference is partly due to their use of precipitation datasets with coarser temporal resolutions. As precipitation intensity increases, TMV contribution to rainfall also increases. Over 68% of TMVs have a lifespan of less than 6 hr (with ~\u0026thinsp;2% lasting 24 hr or more. Additionally, over 75% of TMVs have a vertical thickness of \u0026le;\u0026thinsp;50 hPa. Overall, as the lifespan of TMVs increases, they tend to exhibit stronger intensities, greater vertical thicknesses, and more significant displacement.\u003c/p\u003e \u003cp\u003eFrom 1979 to 2020, our results reveal that TMVs do not increase/decrease significantly in their total occurrence frequency. In contrast, longer-lived TMVs have increased at a rate of ~\u0026thinsp;1 TMV per warm season. However, as these results are ERA5-based, they may be different from the actual situation. TMVs exhibit significant monthly variations, with the occurrence frequency peaking in July. This is closely related to shifts in the location of the 500-hPa convergence line over the western part of the Tibetan Plateau. Regarding diurnal variation, longer-lived TMVs exhibit a prominent peak from the afternoon to early morning (this finding is consistent with the results documented in Li et al. (2014b)), with the highest frequency around 22:00 LST. Overall, TMVs tend to form in a background environment characterized by warmer surface temperatures, higher CAPE, heavier precipitation, stronger upper-tropospheric divergence, and greater middle-tropospheric cyclonic vorticity.\u003c/p\u003e \u003cp\u003eAmong the different TMV types, the vacating type exhibits the highest intensity in cyclonic vorticity, geopotential height, wind speed, and precipitation. Conversely, the quasi-stationary type exhibits the lowest intensity. The tracks of both the vacating and quasi-stationary TMVs cover most of the Tibetan Plateau. However, the vacating TMVs mainly originate from the eastern and northeastern sections of the plateau and move toward the downstream regions. Most of both TMVs move eastward, consistent with the findings of Lin et al. (2020). This study analyzes the common formation mechanisms for different TMV types by using a Lagrangian composite of the vorticity budget. For the quasi-stationary, non-vacating, and vacating TMVs, their cyclonic vorticity significantly increases before the formation of TMVs owing to vertical stretching associated with strong low-level convergence. Moreover, the upper levels of the vacating TMVs are dominated by cyclonic vorticity, which is related to an upper-level low-pressure trough. Conversely, the upper levels of the quasi-stationary and non-vacating TMVs are dominated by anticyclonic vorticity. This study indicates that a strong steering flow, intense vortex intensity, large vertical extent, and rapid enhancement of upper-level cyclonic vorticity are crucial for the vacating of TMVs. Similarly, Curio et al. (2019) identified a strong steering flow as a favorable condition. However, Li et al. (2019) proposed that the 500-hPa convergence to the east of the vortices, divergence associated with the 200-hPa upper-level jet, and strong ascending motions were key factors for the vacating of vortices.\u003c/p\u003e \u003cp\u003eThis study utilizes hourly ERA5 data to explore TMV statistics, which are helpful to reach a more comprehensive understanding of the mesoscale vortices over Tibetan Plateau. However, because ERA5 data may contain non-negligible errors in representing the actual atmospheric conditions over the plateau, the results of this study may differ from the actual conditions. To reduce uncertainties associated with the use of ERA5, we recommend using additional high-spatiotemporal-resolution data (e.g., 0.25\u0026deg; and hourly, or finer) in future TMV investigations. Combining all available research results will lead to more accurate conclusions regarding TMVs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe ERA5 data used in this work (Hersbach et al. 2020 for data on pressure and single levels) are freely available on the Copernicus Data Store (CDS) at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset\u0026amp;text=ERA5. The GPM IMERG data was downloaded from the Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets?keywords=GPM\u0026amp;page=1).\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (grant numbers 42075002, 42475008 and U2142202), the Open Research Fund Project of Sichuan Provincial Key Laboratory of Plateau and Basin Rainstorm and Flood Disaster (SKZT202203, SZKT202402), the Youth Research Project of the China Meteorological Administration Training Center (2023CMATCQN03), and the Fengyun Satellite Application Advance Plan (FY-APP-2022.0102).\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest or competing interests regarding the publication of this paper.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBartels DL, Maddox RA (1991) Midlevel cyclonic vortices generated by mesoscale convective systems. 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Otherwise, the result is set to zero.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tibetan Plateau, Mesoscale vortices, Statistical features, Composite features, Vorticity budget","lastPublishedDoi":"10.21203/rs.3.rs-6436848/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6436848/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDuring warm seasons, the Tibetan Plateau serves as a key source region for mesoscale vortices, which significantly influence weather patterns over and around the plateau. Despite extensive research on Tibetan Plateau mesoscale vortices (TMVs), no studies have examined statistical characteristics of shorter-lived TMVs (\u0026lt;\u0026thinsp;6 hr), and no studies have ever shown the TMVs\u0026rsquo; vertical-extent features, common formation mechanisms, and hourly diurnal variations. To address these knowledge gaps, we conduct a statistical analysis on the TMVs over 42 warm seasons (from 1979 to 2020) by using the hourly ERA5 data. The findings reveal that TMV formation is mainly terrain-dependent, with higher frequencies occurring in regions of greater altitude. Over 68% of TMVs exhibit a lifespan of \u0026lt;\u0026thinsp;6 hr, and more than 75% of TMVs have a vertical extent of \u0026le;\u0026thinsp;50 hPa. Precipitation associated with TMVs accounts for ~\u0026thinsp;13.8% of the total accumulated precipitation over the plateau, contributing up to ~\u0026thinsp;36% in certain regions of the northwestern and central plateau. TMVs exhibit a diurnal peak in occurrence frequency around 22:00 local solar time. TMV formation is mainly driven by vertical stretching due to low-level convergence. TMVs are more likely to form under environmental conditions characterized by warmer surface temperatures, higher convective available potential energy, heavier precipitation, stronger upper-tropospheric divergence, and greater mid-tropospheric cyclonic vorticity. A strong steering flow, an intense vortex intensity, a large vertical extent, and a rapid enhancement in the upper-level cyclonic-vorticity are crucial for the TMVs\u0026rsquo; moving out from the Tibetan Plateau.\u003c/p\u003e","manuscriptTitle":"Key statistical features and common formation mechanisms of mesoscale vortices over Tibetan Plateau: A 42-warm-season analysis based on ERA5 data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 10:17:39","doi":"10.21203/rs.3.rs-6436848/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d203439-c410-4239-9542-8cee1ae0f9ee","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-29T14:16:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 10:17:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6436848","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6436848","identity":"rs-6436848","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}