Orographic Effects on Precipitation Microphysics and Vertical Structure over the Sichuan Basin and Its Surrounding Regions Using GPM-DPR Data (2015–2022)

Orographic Effects on Precipitation Microphysics and Vertical Structure over the Sichuan Basin and Its Surrounding Regions Using GPM-DPR Data (2015–2022) | 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 Orographic Effects on Precipitation Microphysics and Vertical Structure over the Sichuan Basin and Its Surrounding Regions Using GPM-DPR Data (2015–2022) Min Yuan, Meilin Yan, Yongren Chen, Delong Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8252478/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted 9 You are reading this latest preprint version Abstract Using GPM-DPR observations from 2015 to 2022, this study investigates how two key topographic factors—elevation and slope—shape the vertical structures and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) over the Sichuan Basin and its surrounding regions. The main findings are as follows. (1) The freezing height (FzH) of CP is higher than that of SP and increases with elevation. The storm top height (STH) of CP exhibits a broader range, with shallow convection occurring primarily over low elevations and gentle slopes. For both precipitation types, STH increases with elevation and slope. (2) SP shows a pronounced bright band near the FzH, whereas CP exhibits stronger Ze throughout the precipitation column; under rainfall rates ≥ 8 mm h⁻¹, near-surface Z e commonly reaches ≥ 40 dBZ over low and mid-elevation areas. The mass-weighted mean diameter (D m ) increases with rainfall rate (CP > SP) and shows a non-monotonic vertical pattern at mid elevations. The particle concentration (N w ) increases with rainfall rate (SP > CP), is higher at low and mid elevations than at high elevations, and tends to be higher on steeper slopes where D m is correspondingly smaller. (3) When rainfall rate RR ≥ 4 mm h⁻¹, particle growth in low-elevation regions is dominated by collision–coalescence, but transitions toward a coalescence–breakup equilibrium with increasing elevation. Steeper slopes further suppress coalescence. These findings advance the understanding of how complex topography modulates precipitation structures and microphysical processes over mountainous regions. Sichuan Basin orographic precipitation microphysical characteristics GPM-DPR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction The Sichuan Basin and its surrounding complex terrain in southwestern China are influenced by a monsoon-dominated climate (Shen et al., 2022 ). The region’s diverse topography—including basins, plateaus, and mountains—profoundly modulates the spatial distribution, intensity, and microphysical characteristics of precipitation (Houze, 1997 ; Liu and Fu, 2001 ; Zhang et al., 2025). Topographic effects can promote convective development and increase precipitation efficiency (Houze, 1982 ; Houze, 2004 ; Smith et al., 2009 ). Moreover, topography also influences the evolution of precipitation systems, with its induced dynamical and thermodynamical effects shaping the vertical structure and microphysical processes of precipitation (Li et al., 2022 ; Liang et al., 2007 ; Maddox, 1980 ; McFarquhar et al., 2007 ; Rotunno and Houze, 2007 ). Collectively, the inter-action between monsoon forcing and complex terrain provides a consistent framework for interpreting precipitation across the Sichuan Basin and surrounding complex terrain. Topographic factors, especially elevation and slope, are key factors influencing the spatial distribution and microphysical mechanisms of precipitation. Statistical studies have long confirmed that elevation and its interaction with prevailing wind exposure are the best predictors of precipitation spatial patterns in mountainous areas (Basist et al., 1994 ). Fu's (1992) theoretical model quantitatively reveals the nonlinear relationship between slope and precipitation enhancement. In terms of physical mechanisms, elevation influences the vertical structure of precipitation by determining the lifting condensation level and the activation height of ice-phase processes, while slope directly controls the strength of dynamic lifting. For example, in the western Sichuan Basin of China, the steep slope of the Longmen Mountains causes strong lifting of warm, moist air, which is key to triggering and enhancing heavy rainfall (Chen et al., 2024 ). In addition, topography-induced microphysical responses have been observed in eastern Zhejiang, China: as elevation increases, the collision-coalescence process intensifies, leading to an increase in the diameter of raindrops, and a significant change in the vertical distribution of radar reflectivity (Lu et al., 2025 ). The Global Precipitation Measurement (GPM) mission's Dual-Frequency Precipitation Radar (DPR) enables simultaneous observations of precipitation vertical structure and microphysical parameters (Hou et al., 2014 ; Iguchi et al., 2012 ). Its combined Ku and Ka-band observations are highly sensitive to changes in particle size distribution, making it particularly suitable for studying the precipitation vertical structure and micro-physical processes. In the Sichuan Basin and surrounding areas, DPR observations show that as elevation in-creases, the probability of large particles in convective precipitation increases, while particle concentration decreases, reflecting the enhancement of particle growth due to topographic lifting (Shen et al., 2022 ). Observations over North China show that, compared with mountainous regions, convective precipitation over the plains is characterized by larger particle diameters, lower particle concentrations, deeper vertical development, and more pronounced collision–coalescence processes. This highlights the role of topography in modulating precipitation vertical structure, and microphysical processes (Li et al., 2024 ; Wu et al., 2024 ). In Taiwan, the study showed significant regional differences in precipitation microphysical characteristics, mainly influenced by the interaction between topography and the southwest monsoon. The central and southern regions, with deep convection and more extensive particle growth, experience stronger precipitation, while the northern regions, dominated by shallow convection and small droplets, experience weaker precipitation (Janapati et al., 2023 ). In the Western Ghats of India, DPR data clearly reveal the modulation effect of topography on the seasonal distribution of precipitation types and microphysical processes, with windward slope convective precipitation dominated by collision-coalescence and stratiform precipitation dominated by the breakup process (Kumar et al., 2025 ). Although numerous previous studies have utilized GPM observations to reveal the topographic effects and regional variations of precipitation structures, a comprehensive evaluation of the underlying mechanisms associated with different topographic factors remains limited. In particular, few studies have examined how elevation and slope jointly regulate the vertical structure and microphysical processes of convective and stratiform precipitation over complex terrain. Based on GPM-DPR observations from 2015–2022, this study investigates the responses of precipitation vertical structure and microphysical processes to elevation and slope across the Sichuan Basin and its surrounding regions, with the aim of elucidating the mechanisms by which topography regulates precipitation under a monsoon-dominated climate. 2 Data Description The GPM satellite, with a 65° inclined orbit covering 65°S–65°N, completes ~ 16 orbits daily. It carries the Dual-Frequency Precipitation Radar (DPR), including KuPR (13.6 GHz, ≥ 0.5 mm/h) and KaPR (35.5 GHz, ≥ 0.3 mm/h), designed to detect moderate-to-heavy and light precipitation, respectively. In 2021, the DPR algorithm was upgraded to Version 07, introducing the Full Swath Dual-Frequency (FS) mode, which enhanced estimation accuracy across the full swath. The 2A.DPR_FS product provides comprehensive precipitation in-formation, including rain rate (RR), radar reflectivity factor (Z e ), precipitation type, drop size distribution, freezing level height (FzH), storm top height (STH), mass-weighted mean diameter (D m ), and particle concentration parameter (N w ), among others. Based on the bright band information, the DPR algorithm classifies precipitation into three categories: stratiform precipitation (SP), convective precipitation (CP), and other precipitation types. This study uses GPM 2A.DPR_FS data (2015–2022) with 5 km horizontal and 125m vertical resolution. This study focuses on stratiform and convective precipitation, and classifies precipitation intensity into four categories based on the DPR detection thresholds and sample sizes: (1) 0.5-2 mm/h; (2) 2–4 mm/h; (3) 4–8 mm/h (4) ≥ 8 mm/h (Shen et al., 2022 ). The study area encompasses the Sichuan Basin and its surrounding regions (100°–108°E, 28°–32.5°N), as illustrated in Fig. 1 . Terrain information, including elevation, slope, and slope aspect, is obtained from the Shuttle Radar Topography Mission (SRTM) dataset with a spatial resolution of 30 arc-seconds. The slope values corresponding to each precipitation sample are extracted by matching the geographic coordinates of the GPM observations with the SRTM data. To explore the microphysical characteristics of SP and CP under varying terrain conditions, the study area is categorized by elevation and slope gradient as follows: (1) Low elevation: 0–1 km; (2) Mid elevation: 1-3.5 km; (3) High elevation: >3.5 km; (4) Gentle slope: 0–15°; (5) Moderate slope: 15°-30°; (6) Steep slope: >30°. Table 1 presents the sample statistics of SP and CP after terrain classification over the study region. Overall, the number of SP samples in the Sichuan Basin and surrounding regions is greater than that of CP. Similar patterns have been reported in other regional studies. Table 1 Sample sizes of SP and CP in different terrains from 2015–2022. Characteristic SP CP Low elevation 259111 74109 Mid elevation 179641 47235 High elevation 180418 18146 Gentle slope 318387 81640 Moderate slope 261658 46551 Steep slope 39125 11299 Normalized Contoured Frequency by Altitude Diagrams (NCFADs) are used to reveal the vertical structural characteristics of precipitation (Yuter and Houze, 1995). The calculation method involves determining the frequency ratio of a specific physical quantity (e.g., Z e ) occurring within a certain range at a certain altitude relative to its total occurrences across all altitudes. The formula is expressed as: $$\:{NCFAD}_{i,j}=\frac{{N}_{z}\left(i,j\right)}{{\sum\:}_{i=1}^{h}{\sum\:}_{j=1}^{n}{N}_{z}\left(i,j\right)}$$ Where Nz(i, j) represents the sample in the j-th range at the i-th height level. 3 Results 3.1 Statistical characteristics of FzH and STH Figure 2 and Fig. 3 show statistical characteristics of FzH and STH for SP and CP. Analysis of the violin plots and box plots for FzH reveals CP exhibits a relatively concentrated distribution, with a smaller interquartile range (IQR) and narrower box span. In contrast, SP displays a more dispersed distribution, characterized by a significantly larger IQR, a wider box span, and a greater likelihood of outliers, indicating higher variability in FzH. Overall, CP generally exhibits higher values than SP across statistical indicators, including the upper quartile, median, mean, and the third quartile. This is attributed to the stronger updrafts in convective clouds, which lift low-level warm air, thereby elevating the FzH. With increasing terrain elevation, the FzH for both precipitation types shows an upward trend. At slopes below 30°, the FzH increases with slopes steepness. However, when the slope exceeds 30°, the FzH of SP continues to increase slightly, while that of CP exhibits a moderate decrease. As shown in Fig. 3 , the STH distribution for CP exhibits a larger IQR than that for SP, in contrast to the freezing height distribution. This difference arises from variations in convection strength: shallow convection produces lower STH, whereas deep convection results in higher STH. Violin plots of CP at low elevations and gentle slopes reveal a lower STH compared to SP and more concentrated distribution, with most heights below 5 km, indicating the prevalence of shallow convective activity in these regions. Generally, the STH for both precipitation types increases with elevation and slope, except at steep slopes, where the STH for CP is lower than that at moderate slopes. 3.2 Statistical characteristics of Z e Figure 4 presents NCFADs of Z e distributions across various elevations, demonstrating that CP exhibits a higher vertical extent and a broader distribution of Z e values at all altitudes compared to SP. This reflects stronger ground precipitation intensity and a greater abundance of high-altitude ice-phase particles in CP. Above the FzH, the maximum frequency profile of the Z e for CP shows a steeper rate of change with altitude than that for SP, indicating more active microphysical processes, such as ice-phase particle collisions, riming, and aggregation, in convective clouds. In high-elevation regions, the near-surface Z e distribution is narrower for both precipitation types compared to low- and mid-elevation regions, suggesting weaker precipitation intensity at higher elevations. The height of the maximum frequency increases with elevation, corresponding to the higher STH observed in high elevation regions (Fig. 3 ). Notably, in low elevation regions, CP exhibits a maximum frequency around 2 km and 20 dBZ, confirming the previously proposed prevalence of shallow convective activity at these regions. The NCFADs of the Z e across varying slopes (not shown) closely resembles the distribution based on elevation (Fig. 4 ). Specifically, the Z e for CP exhibits a broader distribution across all slopes compared to SP. At gentle slopes, the maximum frequency Z e for CP peaks at approximately 20 dBZ at 2 km, indicating a predominance of shallow convective activity at those regions. To further investigate the influence of topography on precipitation structure, the Z e profiles are classified according to elevation and slope. As shown in Fig. 5 SP and CP exhibit distinct vertical structures of Z e under varying rain rates, elevations, and slopes. SP exhibits a bright band near the FzH. In contrast, the Z e of CP is generally higher than that of SP, and the difference is particularly pronounced under heavy rain rates conditions (RR ≥ 8 mm/h), reflecting stronger upward motion. With increasing rain rates, the Z e of both SP and CP increases. When RR ≥ 4 mm/h, the near-surface Z e of CP significantly increases in mid to high elevation regions. Under heavy rainfall conditions at mid elevations (RR ≥ 8 mm/h), the Z e of CP exceeds 40 dBZ, and the STH is significantly higher than that of SP, indicating deep convection. For high elevations, the near-surface Z e is lower than that in low- and mid-elevation regions, indicating that the colder and drier atmospheric conditions at higher altitudes limit the availability of water vapor and suppress both condensation and the collisional growth of hydrometeors. Studies conducted in the Yushu region of the Tibetan Plateau (Gong et al., 2022 ) and in southwestern India (Andrews et al., 2025 ) have also reported similar findings. Notably, compared with the strong influence of elevation, the effect of slope on the vertical structure of Z e is relatively minor. 3.3 Vertical structure of D m and N w To further investigate the vertical structure and microphysical processes of SP and CP, Fig. 6 shows the vertical profiles of D m for SP and CP at different elevations and slopes. When 0.5 ≤ RR< 2 mm/h, the difference in D m between the near-surface SP and CP is small. With increasing rain rates, D m for both types of precipitation increases, with CP significantly exceeding that for SP. When RR ≥ 4 mm/h, D m for CP near the surface is approximately 1.7–2.0 mm, significantly higher than that for SP (approximately 1.4–1.7 mm). At mid elevations, D m for both SP and CP increases first and then decreases with decreasing altitude. D m is relatively large at gentle slopes, while it is generally smaller at moderate- and steep-slopes, particularly for CP. Li et al. ( 2024 ) also found in their study of North China that increases in low-level particle radius and Z e are more pronounced over the plains than in mountainous regions. Figure 7 shows the vertical profiles of N W for SP and CP under different rain rates and topographic conditions. As RR increasing, the N w of both CP and SP increases. Except when the RR is between 0.5–2 mm/h, the N w of SP is generally much higher than that of CP. In particular, when RR ≥ 8 mm/h, the N w of SP in low- and mid-elevations regions can exceed 40 mm⁻ 1 m⁻ 3 , far greater than that of CP. At high elevations, the N W of both SP and CP is significantly lower than that at low- and mid-elevations. This finding is consistent with the study by Janapati et al ( 2023 ). Slope effects are pronounced: N w over moderate- and steep-slopes generally exceeds that over gentle slopes, and the previous analysis of D m indicates D m is smaller on moderate- and steep-slopes than on gentle slopes, reflecting stronger orographic lifting that enhances turbulence and shortens particle residence time, thereby intensifying breakup and evaporation. 3.4 Particle evolution characteristics of precipitation in different terrains Previous analysis of the vertical profiles of D m and N w revealed that when the RR ≥ 4 mm/h, D m initially increases and then decreases with decreasing height in the lower atmosphere of low and mid elevation regions, while N w exhibits the opposite trend. In contrast, in high elevation regions, D m and N w show more complex vertical variations across different slopes, highlighting the distinct impacts of topographic factors on precipitation structure and microphysical processes. To investigate these differences, this study characterizes the microphysical processes by analyzing the vertical variations in D m and Z e (ΔD m , ΔZ e ), following the method of Kumjian and Prat ( 2014 ). Here, Δ is defined as the value at the lower altitude minus the value at the higher altitude within a specified layer. The layers were selected based on the previously described characteristic variations: 0.5–3 km for low elevation regions, 1–3 km for mid elevation regions, and 4–5 km for high elevation regions. According to Wu et al. ( 2024 ), the dominant microphysical processes are classified as follows: Collision-coalescence: ΔD m >0 and ΔZ e >0; Breakup: ΔD m < 0 and Δ Z e 0 and ΔZ e < 0; Equilibrium (coalescence-breakup equilibrium): ΔD m 0. In low elevation regions, particle growth for both SP and CP is primarily controlled by the collision-coalescence mechanism. On gentle slopes and moderate- and steep-slopes, the collision-coalescence proportions for SP are 53.69% (Fig. 8 a) and 52.12% (Fig. 8 d), respectively, while those for CP are 48.23% (Fig. 9 a) and 47.62% (Fig. 9 d). With increasing elevation, the proportion of coalescence-breakup equilibrium increases, becoming dominant in high elevation regions. Specifically, the equilibrium proportions for SP and CP exceed 55% (Figs. 8 c and 8 f) and 73% (Figs. 9 c and 9 f), respectively. In mid elevation regions, an increase in slope reduces the collision-coalescence proportion but increases the coalescence-breakup equilibrium proportion. For SP, the collision-coalescence proportion decreases from 47.17% to 39.15%, while the equilibrium proportion increases from 29.56% to 38.79% (Figs. 8 b and 8 e). For CP, the collision-coalescence proportion drops from 36.46% to 31.91%, whereas the equilibrium proportion rises from 39.58% to 50.21% (Figs. 9 b and 9 e). To verify the above interpretations, the probability density differences of the D m –N w spectra (lower altitude minus upper altitude) are analyzed as evidence. As shown in Fig. 10 a, for SP over low-elevation gentle-slope regions, positive values are mainly concentrated in the upper-right portion of the spectrum, whereas negative values are primarily located in the lower-left. This pattern indicates an increase in large-droplet concentrations and a decrease in small-droplet concentrations, suggesting that collision–coalescence is the dominant process. The positive center (D m ≈ 1.58 mm, N w ≈ 3.69 mm⁻ 1 m⁻ 3 ) and the negative center (D m ≈ 1.45 mm, N w ≈ 3.36 mm⁻ 1 m⁻ 3 ) are relatively close to each other. In the mid-elevation gentle-slope regions (Fig. 10 b), the positive center of the spectrum shifts toward the upper-left (D m ≈ 1.18 mm, N w ≈ 4.34 mm⁻ 1 m⁻ 3 ), while the negative center appears to the lower-right of the positive center (D m ≈ 1.61 mm, N w ≈ 3.45 mm⁻ 1 m⁻ 3 ). This pattern indicates an increase in small droplet concentrations accompanied by a reduction in medium-sized droplets (~ 1.6 mm), reflecting the influence of breakup. Similar to the low-elevation regions, positive values are still present in the upper-right portion of the spectrum, implying an increase in large droplets and suggesting that collision–coalescence remains active. Therefore, at mid elevations, the relative contribution of coalescence–breakup equilibrium becomes more pronounced. In the high-elevation region (Fig. 10 c), the spectrum exhibits a distribution of positive and negative centers similar to that at mid elevations, with the positive center located to the upper left of the negative center, indicating the presence of droplet breakup. Additionally, the positive-value areas in both the upper-left and lower-right corners reflect increases in high concentrations of small droplets and low concentrations of large droplets, respectively, suggesting the coexistence of coalescence and breakup. This pattern highlights the more complex microphysical processes at high elevations, where a coalescence–breakup equilibrium becomes the dominant mechanism. For moderate- and steep-slope regions (Fig. 10 d–f), the spatial patterns of positive and negative centers closely resemble those in gentle-slope regions at the same elevation. Overall, SP transitions from a collision–coalescence–dominated regime at low elevations to a coalescence–breakup equilibrium at high elevations. In addition, in the moderate- and steep-slope regions at low and mid elevations (Fig. 10 d and Fig. 10 e), the positive area in the upper-right portion of the spectra are noticeably smaller than that over gentle slopes, indicating that increasing slope tends to suppress the collision–coalescence process. These findings support and reinforce the interpretations derived from Fig. 8 . Figure 11 shows the probability density differences of the D m –N w spectra for CP under different elevations and slope conditions. In the low-elevation, gentle-slope region (Fig. 11 a), the spectral distribution of CP closely resembles that of SP, indicating that collision–coalescence is the primary mechanism for particle growth in CP in this terrain. For mid- to high-elevation and moderate- to steep-slope regions (Figs. 11 b–f), the spectra exhibit a consistent pattern in which the positive center lies up and to the left of the negative center, suggesting a prominent role of breakup. In addition, the presence of positive values in the upper-right portion of the spectra highlights that collision–coalescence remains important. Similar features of CP have been observed in the mountainous regions of Taiwan and the Tibetan Plateau (Janapati et al., 2023 ; Yang et al., 2024 ). Compared with SP, the upper-right positive region in CP is larger, indicating a stronger contribution from collision–coalescence. Moreover, the negative centers of CP generally shift further toward the lower-right portion of the spectra, implying that the breakup threshold for CP droplets is higher than that for SP. In summary, CP transitions from being dominated by collision–coalescence at low elevations to a breakup–coalescence equilibrium at higher elevations. With increasing slope, collision–coalescence is further suppressed, strengthening this equilibrium and underscoring the impact of complex terrain on microphysical processes during heavy precipitation. 4 Discussion and Conclusion Using GPM-DPR data from 2015 to 2022, this study investigates the vertical precipitation structures across the Sichuan Basin and its surrounding regions under different topographic factors (elevation and slope), and further explores the microphysical processes responsible for these structural differences. The main findings are as follows. The FzH of CP is generally higher and more narrowly distributed than that of SP, and it increases with elevation. The STH of CP exhibits a much broader range, with shallow convection occurring primarily over low elevations and gentle slopes. Overall, the STH of both precipitation types increases with elevation and slope. SP exhibits a pronounced bright band near the FzH, whereas CP shows stronger Z e throughout the entire precipitation column. The D m for CP is larger than that for SP, and both increase with RR. In mid-elevation regions, D m displays a distinctly non-monotonic vertical pattern, increasing and then decreasing with decreasing altitude. The N w is higher for SP than for CP and increases with RR. N w values over low and mid elevations exceed those at high elevations. Regarding slope, N w on moderate and steep slopes is generally higher than on gentle slopes, while Dm is lower, resulting in a characteristic “small-diameter–high-concentration” spectral signature. Analyses of ΔD m –ΔZ e and the D m –N w spectra indicate that SP and CP at low elevations are primarily governed by collision–coalescence processes. As elevation and slope increase, breakup becomes more prominent and coalescence is progressively suppressed. In high-elevation and steep-slope regions, a coalescence–breakup equilibrium emerges as the dominant microphysical regime. In conclusion, these findings highlight the critical role of terrain in effecting the microphysical characteristics of both SP and CP. The joint impacts of elevation and slope lead to substantial variations in vertical structure, growth mechanisms, and radar observables. These findings contribute to improving satellite retrieval algorithms, deepening the understanding of orographic precipitation mechanisms, and providing theoretical support for enhancing numerical models and quantitative precipitation estimation. Declarations Funding This research is funded by Science & Technology Fundamental Resources Investigation Program (Grant No. 2025FY101502), the Fundamental Research Funds for the Central Universities (Grant No. 24CAFUC01003). Capacity Building for Weather Modification in Southwest China-Research and Experiment Project on Detection of Stratocumulus-Cumulus Mixed Clouds and Convective Clouds in Complex Terrain and Artificial Catalysis Technology (grant no. SCIT-ZG(Z)-2024100001-3). Competing Interests The authors declare no conflicts of interest. Author Contributions Min Yuan: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing—review and editing, supervision, administration and funding acquisition. Meilin Yan: Conceptualization, methodology, validation, software, investigation, writing—original draft preparation and visualization. Yongren Chen: resources and data curation. Delong Zhao: resources and data curation. Data Availability The 2A.DPR V07 dataset were download from https://disc.gsfc.nasa.gov/datasets/GPM_2ADPR_07/summary. The SRTM dataset were download from https://srtm.csi.cgiar.org/. References Andrews, A., Sumesh, R. K., Resmi, E. 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Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 05 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor assigned by journal 03 Dec, 2025 Submission checks completed at journal 03 Dec, 2025 First submitted to journal 01 Dec, 2025 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. We do this by developing innovative software and high quality services for the global research community. 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04:01:15","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92304,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/bdabffb5a0773cf89802e515.html"},{"id":97570563,"identity":"e32f66e5-bd4a-42bd-ab11-890b8b971c34","added_by":"auto","created_at":"2025-12-06 04:01:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2847560,"visible":true,"origin":"","legend":"\u003cp\u003eTopographic map of the Sichuan basin and its surrounding regions\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/efdf1d16bdbf37c90d898e70.png"},{"id":97673026,"identity":"1dad2077-8285-4af8-a373-b4b2cfb0bf32","added_by":"auto","created_at":"2025-12-08 09:39:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":567678,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot and box plot of the FzH of SP (blue) and CP (orange) under different elevation range (left) and slope range (right)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/d880cbb5e49c07cf3b00af51.png"},{"id":97570565,"identity":"25f5449e-cf29-44c1-abd6-9cb9b822003e","added_by":"auto","created_at":"2025-12-06 04:01:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":605974,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 2 but for STH\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/6a8eea7e3456eb8fdaa1b7eb.png"},{"id":97570567,"identity":"3fd8df66-1c27-4a48-963e-4bcf71bc3a5c","added_by":"auto","created_at":"2025-12-06 04:01:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1293472,"visible":true,"origin":"","legend":"\u003cp\u003eNCFAD of reflectivity factor (shaded) for SP (a-c) and CP (d-f) at low elevations (left), mid elevations (middle) and high elevations (right). Black solid lines represent maximum frequency profile of reflectivity factor, black dashed line represents the FzH, and the blue dashed line represents the STH\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/bac03c7f15d37861ce61f45c.png"},{"id":97673358,"identity":"e25860b7-3a09-46f7-9e07-e946f99c3903","added_by":"auto","created_at":"2025-12-08 09:39:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2387327,"visible":true,"origin":"","legend":"\u003cp\u003eVertical profiles of Z\u003csub\u003ee\u003c/sub\u003e for SP (blue) and CP (red) at different rain rates (0.5-2 mm/h, 2-4 mm/h, 4-8 mm/h, ≥ 8 mm/h), elevations (low, mid, and high), and slopes (gentle, moderate- and steep). The FzH and STH are marked by the squares and triangles\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/3e70ea4dbd4dac592e6ad662.png"},{"id":97673333,"identity":"46eb80ce-085a-4d01-9336-5113195bbaaf","added_by":"auto","created_at":"2025-12-08 09:39:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2432336,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Fig. 5, but for D\u003csub\u003em\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/c8df0efc6ea4d7a27c380472.png"},{"id":97673032,"identity":"e2067d35-f7e4-4c7e-9c19-931d94b968fa","added_by":"auto","created_at":"2025-12-08 09:39:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2387420,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Fig. 6, but for N\u003csub\u003ew\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/e99e21560a18dc72fc7e1fb3.png"},{"id":97570592,"identity":"a608c63e-6520-4a46-964e-3320af370545","added_by":"auto","created_at":"2025-12-06 04:01:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1412574,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency patterns of ΔZ\u003csub\u003ee\u003c/sub\u003e and ΔD\u003csub\u003em\u003c/sub\u003e for SP under low elevations (left), mid elevations (middle), and high elevations (right), and for gentle slopes (top) and moderate-to-steep slopes (bottom). The numerical values in each quadrant indicate the proportion of samples corresponding to different microphysical processes\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/d9c8f15ab360177d0c99ecc2.png"},{"id":97570578,"identity":"22749251-3298-4e22-a90e-89931b07d525","added_by":"auto","created_at":"2025-12-06 04:01:14","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1425056,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Fig. 8, but for CP\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/e36e3a0238906496e0101113.png"},{"id":97673300,"identity":"4f245db0-1c83-4da2-9d39-5e407daa3c39","added_by":"auto","created_at":"2025-12-08 09:39:49","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1213526,"visible":true,"origin":"","legend":"\u003cp\u003eProbability density differences of the D\u003csub\u003em\u003c/sub\u003e–N\u003csub\u003ew\u003c/sub\u003e spectra for SP (RR ≥ 4 mm/h) under different elevations (low, mid, and high) and slopes (gentle, moderate- and steep-slopes). White \"+\" represents the region where the change in the lower layers relative to the higher layers is most significant\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/8bc4a1a3bd3b9b895e9334ea.png"},{"id":97672969,"identity":"ae64ecae-1080-4bdc-b540-0a4771ffb7e7","added_by":"auto","created_at":"2025-12-08 09:39:09","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1276273,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Fig 10, but for CP\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/15e9f06b0675540eff6d01e9.png"},{"id":103251069,"identity":"61436ea3-0d67-406f-bb19-1e969c14e47d","added_by":"auto","created_at":"2026-02-23 16:03:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18414077,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8252478/v1/b4d7972e-e0d5-461a-9d99-47124d195639.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOrographic Effects on Precipitation Microphysics and Vertical Structure over the Sichuan Basin and Its Surrounding Regions Using GPM-DPR Data (2015–2022)\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Sichuan Basin and its surrounding complex terrain in southwestern China are influenced by a monsoon-dominated climate (Shen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The region\u0026rsquo;s diverse topography\u0026mdash;including basins, plateaus, and mountains\u0026mdash;profoundly modulates the spatial distribution, intensity, and microphysical characteristics of precipitation (Houze, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Liu and Fu, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Zhang et al., 2025). Topographic effects can promote convective development and increase precipitation efficiency (Houze, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Houze, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Smith et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Moreover, topography also influences the evolution of precipitation systems, with its induced dynamical and thermodynamical effects shaping the vertical structure and microphysical processes of precipitation (Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Maddox, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1980\u003c/span\u003e; McFarquhar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Rotunno and Houze, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Collectively, the inter-action between monsoon forcing and complex terrain provides a consistent framework for interpreting precipitation across the Sichuan Basin and surrounding complex terrain.\u003c/p\u003e\u003cp\u003eTopographic factors, especially elevation and slope, are key factors influencing the spatial distribution and microphysical mechanisms of precipitation. Statistical studies have long confirmed that elevation and its interaction with prevailing wind exposure are the best predictors of precipitation spatial patterns in mountainous areas (Basist et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Fu's (1992) theoretical model quantitatively reveals the nonlinear relationship between slope and precipitation enhancement. In terms of physical mechanisms, elevation influences the vertical structure of precipitation by determining the lifting condensation level and the activation height of ice-phase processes, while slope directly controls the strength of dynamic lifting. For example, in the western Sichuan Basin of China, the steep slope of the Longmen Mountains causes strong lifting of warm, moist air, which is key to triggering and enhancing heavy rainfall (Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, topography-induced microphysical responses have been observed in eastern Zhejiang, China: as elevation increases, the collision-coalescence process intensifies, leading to an increase in the diameter of raindrops, and a significant change in the vertical distribution of radar reflectivity (Lu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Global Precipitation Measurement (GPM) mission's Dual-Frequency Precipitation Radar (DPR) enables simultaneous observations of precipitation vertical structure and microphysical parameters (Hou et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Iguchi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Its combined Ku and Ka-band observations are highly sensitive to changes in particle size distribution, making it particularly suitable for studying the precipitation vertical structure and micro-physical processes. In the Sichuan Basin and surrounding areas, DPR observations show that as elevation in-creases, the probability of large particles in convective precipitation increases, while particle concentration decreases, reflecting the enhancement of particle growth due to topographic lifting (Shen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Observations over North China show that, compared with mountainous regions, convective precipitation over the plains is characterized by larger particle diameters, lower particle concentrations, deeper vertical development, and more pronounced collision\u0026ndash;coalescence processes. This highlights the role of topography in modulating precipitation vertical structure, and microphysical processes (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Taiwan, the study showed significant regional differences in precipitation microphysical characteristics, mainly influenced by the interaction between topography and the southwest monsoon. The central and southern regions, with deep convection and more extensive particle growth, experience stronger precipitation, while the northern regions, dominated by shallow convection and small droplets, experience weaker precipitation (Janapati et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the Western Ghats of India, DPR data clearly reveal the modulation effect of topography on the seasonal distribution of precipitation types and microphysical processes, with windward slope convective precipitation dominated by collision-coalescence and stratiform precipitation dominated by the breakup process (Kumar et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough numerous previous studies have utilized GPM observations to reveal the topographic effects and regional variations of precipitation structures, a comprehensive evaluation of the underlying mechanisms associated with different topographic factors remains limited. In particular, few studies have examined how elevation and slope jointly regulate the vertical structure and microphysical processes of convective and stratiform precipitation over complex terrain. Based on GPM-DPR observations from 2015\u0026ndash;2022, this study investigates the responses of precipitation vertical structure and microphysical processes to elevation and slope across the Sichuan Basin and its surrounding regions, with the aim of elucidating the mechanisms by which topography regulates precipitation under a monsoon-dominated climate.\u003c/p\u003e"},{"header":"2 Data Description","content":"\u003cp\u003eThe GPM satellite, with a 65\u0026deg; inclined orbit covering 65\u0026deg;S\u0026ndash;65\u0026deg;N, completes\u0026thinsp;~\u0026thinsp;16 orbits daily. It carries the Dual-Frequency Precipitation Radar (DPR), including KuPR (13.6 GHz, \u0026ge;\u0026thinsp;0.5 mm/h) and KaPR (35.5 GHz, \u0026ge;\u0026thinsp;0.3 mm/h), designed to detect moderate-to-heavy and light precipitation, respectively. In 2021, the DPR algorithm was upgraded to Version 07, introducing the Full Swath Dual-Frequency (FS) mode, which enhanced estimation accuracy across the full swath. The 2A.DPR_FS product provides comprehensive precipitation in-formation, including rain rate (RR), radar reflectivity factor (Z\u003csub\u003ee\u003c/sub\u003e), precipitation type, drop size distribution, freezing level height (FzH), storm top height (STH), mass-weighted mean diameter (D\u003csub\u003em\u003c/sub\u003e), and particle concentration parameter (N\u003csub\u003ew\u003c/sub\u003e), among others. Based on the bright band information, the DPR algorithm classifies precipitation into three categories: stratiform precipitation (SP), convective precipitation (CP), and other precipitation types. This study uses GPM 2A.DPR_FS data (2015\u0026ndash;2022) with 5 km horizontal and 125m vertical resolution. This study focuses on stratiform and convective precipitation, and classifies precipitation intensity into four categories based on the DPR detection thresholds and sample sizes: (1) 0.5-2 mm/h; (2) 2\u0026ndash;4 mm/h; (3) 4\u0026ndash;8 mm/h (4)\u0026thinsp;\u0026ge;\u0026thinsp;8 mm/h (Shen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study area encompasses the Sichuan Basin and its surrounding regions (100\u0026deg;\u0026ndash;108\u0026deg;E, 28\u0026deg;\u0026ndash;32.5\u0026deg;N), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Terrain information, including elevation, slope, and slope aspect, is obtained from the Shuttle Radar Topography Mission (SRTM) dataset with a spatial resolution of 30 arc-seconds. The slope values corresponding to each precipitation sample are extracted by matching the geographic coordinates of the GPM observations with the SRTM data. To explore the microphysical characteristics of SP and CP under varying terrain conditions, the study area is categorized by elevation and slope gradient as follows: (1) Low elevation: 0\u0026ndash;1 km; (2) Mid elevation: 1-3.5 km; (3) High elevation: \u0026gt;3.5 km; (4) Gentle slope: 0\u0026ndash;15\u0026deg;; (5) Moderate slope: 15\u0026deg;-30\u0026deg;; (6) Steep slope: \u0026gt;30\u0026deg;.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sample statistics of SP and CP after terrain classification over the study region. Overall, the number of SP samples in the Sichuan Basin and surrounding regions is greater than that of CP. Similar patterns have been reported in other regional studies.\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSample sizes of SP and CP in different terrains from 2015–2022.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMid elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e179641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47235\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh elevation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGentle slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e261658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteep slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11299\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNormalized Contoured Frequency by Altitude Diagrams (NCFADs) are used to reveal the vertical structural characteristics of precipitation (Yuter and Houze,\u0026nbsp;1995). The calculation method involves determining the frequency ratio of a specific physical quantity (e.g., Z\u003csub\u003ee\u003c/sub\u003e) occurring within a certain range at a certain altitude relative to its total occurrences across all altitudes. The formula is expressed as:\u003c/p\u003e\n\u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{NCFAD}_{i,j}=\\frac{{N}_{z}\\left(i,j\\right)}{{\\sum\\:}_{i=1}^{h}{\\sum\\:}_{j=1}^{n}{N}_{z}\\left(i,j\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere Nz(i, j) represents the sample in the j-th range at the i-th height level.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Statistical characteristics of FzH and STH\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show statistical characteristics of FzH and STH for SP and CP. Analysis of the violin plots and box plots for FzH reveals CP exhibits a relatively concentrated distribution, with a smaller interquartile range (IQR) and narrower box span. In contrast, SP displays a more dispersed distribution, characterized by a significantly larger IQR, a wider box span, and a greater likelihood of outliers, indicating higher variability in FzH. Overall, CP generally exhibits higher values than SP across statistical indicators, including the upper quartile, median, mean, and the third quartile. This is attributed to the stronger updrafts in convective clouds, which lift low-level warm air, thereby elevating the FzH. With increasing terrain elevation, the FzH for both precipitation types shows an upward trend. At slopes below 30\u0026deg;, the FzH increases with slopes steepness. However, when the slope exceeds 30\u0026deg;, the FzH of SP continues to increase slightly, while that of CP exhibits a moderate decrease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the STH distribution for CP exhibits a larger IQR than that for SP, in contrast to the freezing height distribution. This difference arises from variations in convection strength: shallow convection produces lower STH, whereas deep convection results in higher STH. Violin plots of CP at low elevations and gentle slopes reveal a lower STH compared to SP and more concentrated distribution, with most heights below 5 km, indicating the prevalence of shallow convective activity in these regions. Generally, the STH for both precipitation types increases with elevation and slope, except at steep slopes, where the STH for CP is lower than that at moderate slopes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Statistical characteristics of Z\u003csub\u003ee\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents NCFADs of Z\u003csub\u003ee\u003c/sub\u003e distributions across various elevations, demonstrating that CP exhibits a higher vertical extent and a broader distribution of Z\u003csub\u003ee\u003c/sub\u003e values at all altitudes compared to SP. This reflects stronger ground precipitation intensity and a greater abundance of high-altitude ice-phase particles in CP. Above the FzH, the maximum frequency profile of the Z\u003csub\u003ee\u003c/sub\u003e for CP shows a steeper rate of change with altitude than that for SP, indicating more active microphysical processes, such as ice-phase particle collisions, riming, and aggregation, in convective clouds. In high-elevation regions, the near-surface Z\u003csub\u003ee\u003c/sub\u003e distribution is narrower for both precipitation types compared to low- and mid-elevation regions, suggesting weaker precipitation intensity at higher elevations. The height of the maximum frequency increases with elevation, corresponding to the higher STH observed in high elevation regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Notably, in low elevation regions, CP exhibits a maximum frequency around 2 km and 20 dBZ, confirming the previously proposed prevalence of shallow convective activity at these regions.\u003c/p\u003e\u003cp\u003eThe NCFADs of the Z\u003csub\u003ee\u003c/sub\u003e across varying slopes (not shown) closely resembles the distribution based on elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, the Z\u003csub\u003ee\u003c/sub\u003e for CP exhibits a broader distribution across all slopes compared to SP. At gentle slopes, the maximum frequency Z\u003csub\u003ee\u003c/sub\u003e for CP peaks at approximately 20 dBZ at 2 km, indicating a predominance of shallow convective activity at those regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the influence of topography on precipitation structure, the Z\u003csub\u003ee\u003c/sub\u003e profiles are classified according to elevation and slope. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e SP and CP exhibit distinct vertical structures of Z\u003csub\u003ee\u003c/sub\u003e under varying rain rates, elevations, and slopes. SP exhibits a bright band near the FzH. In contrast, the Z\u003csub\u003ee\u003c/sub\u003e of CP is generally higher than that of SP, and the difference is particularly pronounced under heavy rain rates conditions (RR\u0026thinsp;\u0026ge;\u0026thinsp;8 mm/h), reflecting stronger upward motion. With increasing rain rates, the Z\u003csub\u003ee\u003c/sub\u003e of both SP and CP increases. When RR\u0026thinsp;\u0026ge;\u0026thinsp;4 mm/h, the near-surface Z\u003csub\u003ee\u003c/sub\u003e of CP significantly increases in mid to high elevation regions. Under heavy rainfall conditions at mid elevations (RR\u0026thinsp;\u0026ge;\u0026thinsp;8 mm/h), the Z\u003csub\u003ee\u003c/sub\u003e of CP exceeds 40 dBZ, and the STH is significantly higher than that of SP, indicating deep convection. For high elevations, the near-surface Z\u003csub\u003ee\u003c/sub\u003e is lower than that in low- and mid-elevation regions, indicating that the colder and drier atmospheric conditions at higher altitudes limit the availability of water vapor and suppress both condensation and the collisional growth of hydrometeors. Studies conducted in the Yushu region of the Tibetan Plateau (Gong et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and in southwestern India (Andrews et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) have also reported similar findings. Notably, compared with the strong influence of elevation, the effect of slope on the vertical structure of Z\u003csub\u003ee\u003c/sub\u003e is relatively minor.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Vertical structure of D\u003csub\u003em\u003c/sub\u003e and N\u003csub\u003ew\u003c/sub\u003e\u003c/h2\u003e\u003cp\u003eTo further investigate the vertical structure and microphysical processes of SP and CP, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the vertical profiles of D\u003csub\u003em\u003c/sub\u003e for SP and CP at different elevations and slopes. When 0.5\u0026thinsp;\u0026le;\u0026thinsp;RR\u0026lt; 2 mm/h, the difference in D\u003csub\u003em\u003c/sub\u003e between the near-surface SP and CP is small. With increasing rain rates, D\u003csub\u003em\u003c/sub\u003e for both types of precipitation increases, with CP significantly exceeding that for SP. When RR\u0026thinsp;\u0026ge;\u0026thinsp;4 mm/h, D\u003csub\u003em\u003c/sub\u003e for CP near the surface is approximately 1.7\u0026ndash;2.0 mm, significantly higher than that for SP (approximately 1.4\u0026ndash;1.7 mm). At mid elevations, D\u003csub\u003em\u003c/sub\u003e for both SP and CP increases first and then decreases with decreasing altitude. D\u003csub\u003em\u003c/sub\u003e is relatively large at gentle slopes, while it is generally smaller at moderate- and steep-slopes, particularly for CP. Li et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) also found in their study of North China that increases in low-level particle radius and Z\u003csub\u003ee\u003c/sub\u003e are more pronounced over the plains than in mountainous regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the vertical profiles of N\u003csub\u003eW\u003c/sub\u003e for SP and CP under different rain rates and topographic conditions. As RR increasing, the N\u003csub\u003ew\u003c/sub\u003e of both CP and SP increases. Except when the RR is between 0.5\u0026ndash;2 mm/h, the N\u003csub\u003ew\u003c/sub\u003e of SP is generally much higher than that of CP. In particular, when RR\u0026thinsp;\u0026ge;\u0026thinsp;8 mm/h, the N\u003csub\u003ew\u003c/sub\u003e of SP in low- and mid-elevations regions can exceed 40 mm⁻\u003csup\u003e1\u003c/sup\u003e m⁻\u003csup\u003e3\u003c/sup\u003e, far greater than that of CP. At high elevations, the N\u003csub\u003eW\u003c/sub\u003e of both SP and CP is significantly lower than that at low- and mid-elevations. This finding is consistent with the study by Janapati et al (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Slope effects are pronounced: N\u003csub\u003ew\u003c/sub\u003e over moderate- and steep-slopes generally exceeds that over gentle slopes, and the previous analysis of D\u003csub\u003em\u003c/sub\u003e indicates D\u003csub\u003em\u003c/sub\u003e is smaller on moderate- and steep-slopes than on gentle slopes, reflecting stronger orographic lifting that enhances turbulence and shortens particle residence time, thereby intensifying breakup and evaporation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Particle evolution characteristics of precipitation in different terrains\u003c/h2\u003e\u003cp\u003ePrevious analysis of the vertical profiles of D\u003csub\u003em\u003c/sub\u003e and N\u003csub\u003ew\u003c/sub\u003e revealed that when the RR\u0026thinsp;\u0026ge;\u0026thinsp;4 mm/h, D\u003csub\u003em\u003c/sub\u003e initially increases and then decreases with decreasing height in the lower atmosphere of low and mid elevation regions, while N\u003csub\u003ew\u003c/sub\u003e exhibits the opposite trend. In contrast, in high elevation regions, D\u003csub\u003em\u003c/sub\u003e and N\u003csub\u003ew\u003c/sub\u003e show more complex vertical variations across different slopes, highlighting the distinct impacts of topographic factors on precipitation structure and microphysical processes. To investigate these differences, this study characterizes the microphysical processes by analyzing the vertical variations in D\u003csub\u003em\u003c/sub\u003e and Z\u003csub\u003ee\u003c/sub\u003e (ΔD\u003csub\u003em\u003c/sub\u003e, ΔZ\u003csub\u003ee\u003c/sub\u003e), following the method of Kumjian and Prat (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Here, Δ is defined as the value at the lower altitude minus the value at the higher altitude within a specified layer. The layers were selected based on the previously described characteristic variations: 0.5\u0026ndash;3 km for low elevation regions, 1\u0026ndash;3 km for mid elevation regions, and 4\u0026ndash;5 km for high elevation regions. According to Wu et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the dominant microphysical processes are classified as follows: Collision-coalescence: ΔD\u003csub\u003em\u003c/sub\u003e \u0026gt;0 and ΔZ\u003csub\u003ee\u003c/sub\u003e \u0026gt;0; Breakup: ΔD\u003csub\u003em\u003c/sub\u003e \u0026lt; 0 and Δ Z\u003csub\u003ee\u003c/sub\u003e \u0026lt; 0; Evaporation: ΔD\u003csub\u003em\u003c/sub\u003e \u0026gt;0 and ΔZ\u003csub\u003ee\u003c/sub\u003e \u0026lt; 0; Equilibrium (coalescence-breakup equilibrium): ΔD\u003csub\u003em\u003c/sub\u003e \u0026lt; 0 and ΔZ\u003csub\u003ee\u003c/sub\u003e \u0026gt;0.\u003c/p\u003e\u003cp\u003eIn low elevation regions, particle growth for both SP and CP is primarily controlled by the collision-coalescence mechanism. On gentle slopes and moderate- and steep-slopes, the collision-coalescence proportions for SP are 53.69% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) and 52.12% (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed), respectively, while those for CP are 48.23% (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea) and 47.62% (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed). With increasing elevation, the proportion of coalescence-breakup equilibrium increases, becoming dominant in high elevation regions. Specifically, the equilibrium proportions for SP and CP exceed 55% (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef) and 73% (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ef), respectively.\u003c/p\u003e\u003cp\u003eIn mid elevation regions, an increase in slope reduces the collision-coalescence proportion but increases the coalescence-breakup equilibrium proportion. For SP, the collision-coalescence proportion decreases from 47.17% to 39.15%, while the equilibrium proportion increases from 29.56% to 38.79% (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). For CP, the collision-coalescence proportion drops from 36.46% to 31.91%, whereas the equilibrium proportion rises from 39.58% to 50.21% (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo verify the above interpretations, the probability density differences of the D\u003csub\u003em\u003c/sub\u003e\u0026ndash;N\u003csub\u003ew\u003c/sub\u003e spectra (lower altitude minus upper altitude) are analyzed as evidence. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea, for SP over low-elevation gentle-slope regions, positive values are mainly concentrated in the upper-right portion of the spectrum, whereas negative values are primarily located in the lower-left. This pattern indicates an increase in large-droplet concentrations and a decrease in small-droplet concentrations, suggesting that collision\u0026ndash;coalescence is the dominant process. The positive center (D\u003csub\u003em\u003c/sub\u003e \u0026asymp; 1.58 mm, N\u003csub\u003ew\u003c/sub\u003e \u0026asymp; 3.69 mm⁻\u003csup\u003e1\u003c/sup\u003e m⁻\u003csup\u003e3\u003c/sup\u003e) and the negative center (D\u003csub\u003em\u003c/sub\u003e \u0026asymp; 1.45 mm, N\u003csub\u003ew\u003c/sub\u003e \u0026asymp; 3.36 mm⁻\u003csup\u003e1\u003c/sup\u003e m⁻\u003csup\u003e3\u003c/sup\u003e) are relatively close to each other.\u003c/p\u003e\u003cp\u003eIn the mid-elevation gentle-slope regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb), the positive center of the spectrum shifts toward the upper-left (D\u003csub\u003em\u003c/sub\u003e \u0026asymp; 1.18 mm, N\u003csub\u003ew\u003c/sub\u003e \u0026asymp; 4.34 mm⁻\u003csup\u003e1\u003c/sup\u003e m⁻\u003csup\u003e3\u003c/sup\u003e), while the negative center appears to the lower-right of the positive center (D\u003csub\u003em\u003c/sub\u003e \u0026asymp; 1.61 mm, N\u003csub\u003ew\u003c/sub\u003e \u0026asymp; 3.45 mm⁻\u003csup\u003e1\u003c/sup\u003e m⁻\u003csup\u003e3\u003c/sup\u003e). This pattern indicates an increase in small droplet concentrations accompanied by a reduction in medium-sized droplets (~\u0026thinsp;1.6 mm), reflecting the influence of breakup. Similar to the low-elevation regions, positive values are still present in the upper-right portion of the spectrum, implying an increase in large droplets and suggesting that collision\u0026ndash;coalescence remains active. Therefore, at mid elevations, the relative contribution of coalescence\u0026ndash;breakup equilibrium becomes more pronounced.\u003c/p\u003e\u003cp\u003eIn the high-elevation region (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ec), the spectrum exhibits a distribution of positive and negative centers similar to that at mid elevations, with the positive center located to the upper left of the negative center, indicating the presence of droplet breakup. Additionally, the positive-value areas in both the upper-left and lower-right corners reflect increases in high concentrations of small droplets and low concentrations of large droplets, respectively, suggesting the coexistence of coalescence and breakup. This pattern highlights the more complex microphysical processes at high elevations, where a coalescence\u0026ndash;breakup equilibrium becomes the dominant mechanism.\u003c/p\u003e\u003cp\u003eFor moderate- and steep-slope regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed\u0026ndash;f), the spatial patterns of positive and negative centers closely resemble those in gentle-slope regions at the same elevation. Overall, SP transitions from a collision\u0026ndash;coalescence\u0026ndash;dominated regime at low elevations to a coalescence\u0026ndash;breakup equilibrium at high elevations. In addition, in the moderate- and steep-slope regions at low and mid elevations (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ed and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ee), the positive area in the upper-right portion of the spectra are noticeably smaller than that over gentle slopes, indicating that increasing slope tends to suppress the collision\u0026ndash;coalescence process. These findings support and reinforce the interpretations derived from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the probability density differences of the D\u003csub\u003em\u003c/sub\u003e\u0026ndash;N\u003csub\u003ew\u003c/sub\u003e spectra for CP under different elevations and slope conditions. In the low-elevation, gentle-slope region (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003ea), the spectral distribution of CP closely resembles that of SP, indicating that collision\u0026ndash;coalescence is the primary mechanism for particle growth in CP in this terrain. For mid- to high-elevation and moderate- to steep-slope regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eb\u0026ndash;f), the spectra exhibit a consistent pattern in which the positive center lies up and to the left of the negative center, suggesting a prominent role of breakup. In addition, the presence of positive values in the upper-right portion of the spectra highlights that collision\u0026ndash;coalescence remains important. Similar features of CP have been observed in the mountainous regions of Taiwan and the Tibetan Plateau (Janapati et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Compared with SP, the upper-right positive region in CP is larger, indicating a stronger contribution from collision\u0026ndash;coalescence. Moreover, the negative centers of CP generally shift further toward the lower-right portion of the spectra, implying that the breakup threshold for CP droplets is higher than that for SP.\u003c/p\u003e\u003cp\u003eIn summary, CP transitions from being dominated by collision\u0026ndash;coalescence at low elevations to a breakup\u0026ndash;coalescence equilibrium at higher elevations. With increasing slope, collision\u0026ndash;coalescence is further suppressed, strengthening this equilibrium and underscoring the impact of complex terrain on microphysical processes during heavy precipitation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion and Conclusion","content":"\u003cp\u003eUsing GPM-DPR data from 2015 to 2022, this study investigates the vertical precipitation structures across the Sichuan Basin and its surrounding regions under different topographic factors (elevation and slope), and further explores the microphysical processes responsible for these structural differences. The main findings are as follows.\u003c/p\u003e\u003cp\u003eThe FzH of CP is generally higher and more narrowly distributed than that of SP, and it increases with elevation. The STH of CP exhibits a much broader range, with shallow convection occurring primarily over low elevations and gentle slopes. Overall, the STH of both precipitation types increases with elevation and slope. SP exhibits a pronounced bright band near the FzH, whereas CP shows stronger Z\u003csub\u003ee\u003c/sub\u003e throughout the entire precipitation column. The D\u003csub\u003em\u003c/sub\u003e for CP is larger than that for SP, and both increase with RR. In mid-elevation regions, D\u003csub\u003em\u003c/sub\u003e displays a distinctly non-monotonic vertical pattern, increasing and then decreasing with decreasing altitude. The N\u003csub\u003ew\u003c/sub\u003e is higher for SP than for CP and increases with RR. N\u003csub\u003ew\u003c/sub\u003e values over low and mid elevations exceed those at high elevations. Regarding slope, N\u003csub\u003ew\u003c/sub\u003e on moderate and steep slopes is generally higher than on gentle slopes, while Dm is lower, resulting in a characteristic \u0026ldquo;small-diameter\u0026ndash;high-concentration\u0026rdquo; spectral signature.\u003c/p\u003e\u003cp\u003eAnalyses of ΔD\u003csub\u003em\u003c/sub\u003e\u0026ndash;ΔZ\u003csub\u003ee\u003c/sub\u003e and the D\u003csub\u003em\u003c/sub\u003e\u0026ndash;N\u003csub\u003ew\u003c/sub\u003e spectra indicate that SP and CP at low elevations are primarily governed by collision\u0026ndash;coalescence processes. As elevation and slope increase, breakup becomes more prominent and coalescence is progressively suppressed. In high-elevation and steep-slope regions, a coalescence\u0026ndash;breakup equilibrium emerges as the dominant microphysical regime.\u003c/p\u003e\u003cp\u003eIn conclusion, these findings highlight the critical role of terrain in effecting the microphysical characteristics of both SP and CP. The joint impacts of elevation and slope lead to substantial variations in vertical structure, growth mechanisms, and radar observables. These findings contribute to improving satellite retrieval algorithms, deepening the understanding of orographic precipitation mechanisms, and providing theoretical support for enhancing numerical models and quantitative precipitation estimation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research is funded by Science \u0026amp; Technology Fundamental Resources Investigation Program (Grant No. 2025FY101502), the Fundamental Research Funds for the Central Universities (Grant No. 24CAFUC01003).\u0026nbsp;Capacity Building for Weather Modification in Southwest China-Research and Experiment Project on \u0026nbsp;Detection of Stratocumulus-Cumulus Mixed Clouds and Convective Clouds in Complex Terrain and Artificial Catalysis Technology (grant no. SCIT-ZG(Z)-2024100001-3).\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMin Yuan: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing\u0026mdash;review and editing, supervision, administration and funding acquisition. Meilin Yan: Conceptualization, methodology, validation, software, investigation, writing\u0026mdash;original draft preparation and visualization. Yongren Chen: resources and data curation. Delong Zhao: resources and data curation.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 2A.DPR V07 dataset were download from https://disc.gsfc.nasa.gov/datasets/GPM_2ADPR_07/summary. The SRTM dataset were download from https://srtm.csi.cgiar.org/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAndrews, A., Sumesh, R. K., Resmi, E. A., Sukumar, N., Gopan, G., Anilkumar, L., Kumar, S., Manoj, M. G., Jash, D., \u0026amp; Unnikrishnan, C. K. (2025). Microphysical characteristics of shallow precipitating systems in the southwest monsoon season: An analysis using in-situ and remote sensing observations. Journal of Atmospheric and Solar-Terrestrial Physics, 269, 106484. https://doi.org/10.1016/j.jastp.2025.106484\u003c/li\u003e\n \u003cli\u003eBasist, A., Bell, G. D., \u0026amp; Meentemeyer, V. (1994). Statistical Relationships between Topography and Precipitation Patterns. https://doi.org/10.1175/1520-0442(1994)007\u0026lt;1305:SRBTAP\u0026gt;2.0.CO;2\u003c/li\u003e\n \u003cli\u003eChen B., Chuhui H., Wenliang G. a. O., \u0026amp; Jiaqi L. I. (2024). 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P. (2014). The Impact of Raindrop Collisional Processes on the Polarimetric Radar Variables. Journal of the Atmospheric Sciences, 71(8), 3052–3067. https://doi.org/10.1175/JAS-D-13-0357.1\u003c/li\u003e\n \u003cli\u003eLi, D., Qi, Y., \u0026amp; Li, H. (2024). Vertical structures and microphysical characteristics of summer precipitation in North China detected by GPM-DPR. Science of The Total Environment, 933, 173129. https://doi.org/10.1016/j.scitotenv.2024.173129\u003c/li\u003e\n \u003cli\u003eLi, Q., Wei, J., Yin, J., Qiao, Z., Cao, J., \u0026amp; Shi, Y. (2022). Microphysical Characteristics of Raindrop Size Distribution and Implications for Radar Rainfall Estimation Over the Northeastern Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 127(12), e2021JD035575. https://doi.org/10.1029/2021JD035575\u003c/li\u003e\n \u003cli\u003eLiang P., He J., Chen longxun, \u0026amp; Li W. (2007). 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Vertical Variability of Cloud Hydrometeors in the Stratiform Region of Mesoscale Convective Systems and Bow Echoes. https://doi.org/10.1175/MWR3444.1\u003c/li\u003e\n \u003cli\u003eRotunno, R., \u0026amp; Houze, R. A. (2007). Lessons on orographic precipitation from the Mesoscale Alpine Programme. Quarterly Journal of the Royal Meteorological Society, 133(625), 811–830. https://doi.org/10.1002/qj.67\u003c/li\u003e\n \u003cli\u003eShen, C., Li, G., \u0026amp; Dong, Y. (2022). Vertical Structures Associated with Orographic Precipitation during Warm Season in the Sichuan Basin and Its Surrounding Areas at Different Altitudes from 8-Year GPM DPR Observations. Remote Sensing, 14(17), 4222. https://doi.org/10.3390/rs14174222\u003c/li\u003e\n \u003cli\u003eSmith, A. M., McFarquhar, G. M., Rauber, R. M., Grim, J. A., Timlin, M. S., Jewett, B. F., \u0026amp; Jorgensen, D. P. (2009). 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Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Monthly weather review, 123(7), 1941-1963. https://doi.org/10.1175/1520-0493(1995)123\u0026lt;1941:TDKAME\u0026gt;2.0.CO;2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Sichuan Basin, orographic precipitation, microphysical characteristics, GPM-DPR","lastPublishedDoi":"10.21203/rs.3.rs-8252478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8252478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing GPM-DPR observations from 2015 to 2022, this study investigates how two key topographic factors\u0026mdash;elevation and slope\u0026mdash;shape the vertical structures and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) over the Sichuan Basin and its surrounding regions. The main findings are as follows. (1) The freezing height (FzH) of CP is higher than that of SP and increases with elevation. The storm top height (STH) of CP exhibits a broader range, with shallow convection occurring primarily over low elevations and gentle slopes. For both precipitation types, STH increases with elevation and slope. (2) SP shows a pronounced bright band near the FzH, whereas CP exhibits stronger Ze throughout the precipitation column; under rainfall rates\u0026thinsp;\u0026ge;\u0026thinsp;8 mm h⁻\u0026sup1;, near-surface Z\u003csub\u003ee\u003c/sub\u003e commonly reaches\u0026thinsp;\u0026ge;\u0026thinsp;40 dBZ over low and mid-elevation areas. The mass-weighted mean diameter (D\u003csub\u003em\u003c/sub\u003e) increases with rainfall rate (CP\u0026thinsp;\u0026gt;\u0026thinsp;SP) and shows a non-monotonic vertical pattern at mid elevations. The particle concentration (N\u003csub\u003ew\u003c/sub\u003e) increases with rainfall rate (SP\u0026thinsp;\u0026gt;\u0026thinsp;CP), is higher at low and mid elevations than at high elevations, and tends to be higher on steeper slopes where D\u003csub\u003em\u003c/sub\u003e is correspondingly smaller. (3) When rainfall rate RR\u0026thinsp;\u0026ge;\u0026thinsp;4 mm h⁻\u0026sup1;, particle growth in low-elevation regions is dominated by collision\u0026ndash;coalescence, but transitions toward a coalescence\u0026ndash;breakup equilibrium with increasing elevation. Steeper slopes further suppress coalescence. These findings advance the understanding of how complex topography modulates precipitation structures and microphysical processes over mountainous regions.\u003c/p\u003e","manuscriptTitle":"Orographic Effects on Precipitation Microphysics and Vertical Structure over the Sichuan Basin and Its Surrounding Regions Using GPM-DPR Data (2015–2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-06 04:01:09","doi":"10.21203/rs.3.rs-8252478/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T00:45:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T11:45:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-20T21:53:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19536262592718749388794749344951925232","date":"2025-12-15T16:52:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246717326222847806460877310736693124156","date":"2025-12-15T05:35:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T03:12:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-03T06:48:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-03T06:46:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-12-01T16:18:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b1b534e3-cbcd-4a58-90e3-a2dc26ab99d0","owner":[],"postedDate":"December 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:00:58+00:00","versionOfRecord":{"articleIdentity":"rs-8252478","link":"https://doi.org/10.1007/s00704-026-06108-y","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2026-02-18 15:57:33","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-12-06 04:01:09","video":"","vorDoi":"10.1007/s00704-026-06108-y","vorDoiUrl":"https://doi.org/10.1007/s00704-026-06108-y","workflowStages":[]},"version":"v1","identity":"rs-8252478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8252478","identity":"rs-8252478","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}