Classification of Cold Vortex Snowstorms in Northeast China Based on K- means Clustering Algorithm and their Features

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Subsequently, using the observational data from 245 meteorological stations in Northeast China, cold vortex snowstorm (CVS) events in Northeast China are defined and further classified into three types based on the NECV intensity, which are named as northwestern-path, southern-path and northern-path types according to the movement paths of the associated NECVs. The results show that for CVSs of the southern-path type, the NECVs feature the southernmost position, with the whole Northeast China dominated by abnormal cyclone at 850 hPa. For CVSs of the northern-path type, there are two abnormal cyclones in a stepped distribution. However, at the sea level, the surface systems of the southern-path and northern-path types develop into cyclones, leading to distinct moisture transport paths among the three types. The high moisture flux regions correspond well with the snowfall area across all three types, as do the high moisture divergence regions with the extreme snowfall regions. The upper-level jet core is located near 30°N. As the Northeast China region is situated north of the upper-level jet exit, the upward motions in the southern-path and northern-path types are stronger than that in the northwestern-path type. For CVSs of the northern type, significant positive vorticity is located away from the snowstorm center. Warm advection dominates the region from the lower to middle troposphere, and strong upward motion lifts warm and humid air, thereby resulting in the snowstorm. CVSs of the northwestern-path type intermediate in upper-lower level configurations between the other two types. For CVSs of the southern-path type, there is a deep vertical system. Cold advection prevails the mid-to-low levels over the snowfall area, which provides a cold cushion near the ground that forces warm and humid air to ascend, thereby producing heavy snow. Furthermore, the downward extension of the high-level potential vorticity to the mid-to-low levels, along with the intrusion of dry-cold air from the upper levels into the mid-to-low levels facilitate the persistence of the cold vortex and the intensification of snowfall. Northeast China Northeast cold vortex Cold vortex snowstorm K-means clustering Circulation Features Thermodynamic mechanisms 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 Cutoff lows are closed cold system at low latitudes developed from the upper-level cold trough in mid-high latitudes that deepens and develops southward (Nieto et al. 2005 ). In Northeast China, the cutoff low system featured by persistence and quasi-stationary is referred to as the Northeast Cold Vortex (NECV). As a fundamental work for NECV related research, the identification of the NECV can be classified into subjective identification (Kentarchos et al. 1998), objective identification (Hu et al. 2011 ; Kentarchos et al. 1998), and a combination of both. The NECV exerts significant impacts on regional climate (Ren et al. 2018 ), especially on precipitation and temperature (Liu et al. 2019 ; Ding et al. 2019 ).Current studies on cold vortex have focused on the following aspects: the mesoscale convective systems influencing cold vortexes through numerical simulations (Chen et al. 2005 ; Yang et al. 2020 ), the mechanisms of cold vortex triggering extreme weather events rainstorm, snowstorm, and severe convection (Ren et al. 2021; Yang et al. 2023; He et al. 2023), and the climatic statistical characteristics of cold vortexes (Yang et al. 2021; Liu et al. 2020; Hao et al. 2023 ). Snowstorms are among the major meteorological disasters in Northeast China, severely impacting transportation, electricity, and agricultural production. The NECV is a crucial weather system affecting the snowstorm events in this region (He et al. 2022 ). Previous research has pointed that the configuration of the southward moving dry cold air at the rear of the upper-level cold vortex and the low-level southwest jet as well as the strengthened convergence in front of the cold vortex jointly provided favorable circulations for snowstorm event (Zhang et al. 2018 ). Snowstorms induced by cold vortexes are accompanied by strong cold advection throughout the entire layer (Xu et al. 2018 ). A comparison of two snowstorm processes have revealed that the NECV facilitates the accumulation and southward intrusion of cold air, providing dynamic lifting conditions for the ascent of warm and humid air and thereby enhancing the impact of the southern branch trough on snowstorms (Xiao et al. 2020). Some efforts have been made for NECV classification in previous studies. Xie and Bueh ( 2012 , 2015 ) used a rotating EOF analysis method to calculate the circulation fields on the peak value days of the NECV system and further divided the NECV into four types: Yenisei River type, Lake Baikal type, Ural − Yakutsk type, and Okhotsk Sea − Arctic Ocean type. Sun et al. ( 1994 ) classified the NECVs into northern vortex, middle vortex, and southern vortex according to their positions. Additionally, machine learning algorithms have been increasingly applied in objective classification of weather systems. For example, based on Nakamura et al. (2009), Zheng et al. ( 2013 ) and Peng et al. ( 2019 ) adopted the K-means clustering algorithm to classify the tracks of tropical cyclones over the northwest Pacific Ocean according to feature parameters including typhoon locations, intensities, track lengths, and track directions. Wang et al. ( 2019 ) also objectively classified the propagation paths of the Madden-Julian Oscillation (MJO) using the K-means clustering algorithm. In this study, the CVSs are clustered based on the mean geopotential height of the NECVs’ inner circle at 500 hPa using the machine learning method of K-means clustering. The following aspects are comprehensively considered during clustering: the NECV’s initiation position, moving direction, moving speed (distance), and especially the relationship between the NECV and snowstorms. On this basis, the temporal characteristics and circulation patterns for different types of CVSs are illustrated, and the correspondence between the snowfall area and the spatial structure of large-scale weather systems are further obtained, thus revealing the influencing mechanism of the NECV on snowstorms in Northeast China. The objective is to obtain a comprehensive circulation configuration and conceptual model for NECV-induced snowstorms, thereby providing a reference for forecasting extreme CVS events. 2. Data and methods 2.1 Data This study adopts the daily precipitation data at 245 meteorological stations in Northeast China (38°–54°N, 118°–134°E) in winters (December to February of the next year) of 1951–2021, which are derived from the China Meteorological Administration (CMA). The study area covers Heilongjiang, Jilin, Liaoning Provinces, and parts of eastern Inner Mongolia. The spatial distribution of stations is shown in Fig. 1 . The hourly data from the fifth-generation atmospheric reanalysis of the global climate (ERA5) released by the European Centre for Medium-Range Weather Forecasts (ECMWF) are also used. ERA5 has 30 vertical layers and a horizontal resolution of 0.25°×0.25°, covering the period from January 1951 to December 2021. The meteorological elements of ERA5 include geopotential height, sea-level pressure, zonal and meridional wind speed, temperature, specific humidity, and vertical velocity. 2.2 Methods 2.2.1 Objective identification of the northeast cold vortex The weather process that meets the following three conditions can be defined as an NECV event. First, there is a closed low-pressure system on the 500 hPa geopotential height field in the range between 30°N–80°N and 85°E–150°E, with the center located between 30°N–70°N and 95°E–140°E. Second, this low-pressure system has a cold core, that is, negative temperature advection are found at 500 hPa. Third, the duration should last at least 24 hours. 2.2.2 Definition of cold vortex snowstorm event A CVS event is recorded when at least 5 stations in Northeast China experience a cumulative snowfall of more than 10 mm in 24 hours during the occurrence of a NECV event. CVS events in consecutive days or those occur with intermittence of no more than one day are considered as the same event. During a CVS event, the day with the largest number of stations where the snowfall exceeds 10 mm is defined as the peak day. 2.2.3 K-means clustering method K-means clustering method, which is able to automatically classify the samples, has been widely applied in circulation classification in climate research (Michelangeli et al. 1995 ; Roller et al. 2016 ; Agel et al. 2015 ). To determine the optimal number of clusters for this method, the silhouette coefficient (Rousseeuw 1987 ) is calculated following the studies of Fang et al. ( 2021 ) and Fan et al. ( 2023 ). This coefficient assesses the quality of clustering by measuring intra-cluster cohesion and inter-cluster dispersion over a range of cluster numbers through the distances between vectors. The coefficient value ranges in [− 1, 1], where values closer to 1 indicate better clustering results. 3. Characteristics and classification of cold vortex snowstorm events According to the abovementioned criteria, a total of 52 CVS events over Northeast China are identified (Table 1 ). These 52 events impact a wide range of area with heavy snowfall intensity, which brought freezing rain and snow disaster to Northeast China. According to the cases list on Table 1 , the ten-day features of CVSs are statistically analyzed, as shown in Fig. 2 a. In general, snowstorm events mainly occur in early winter and early spring, which is consistent with the variation trend of snowstorm weather in China as reported by previous studies (Wetzel et al. 2001). The snowstorm events in Northeast China occur most frequently in early December and late February. Specifically, the frequency of snowstorms gradually decreases from December, falls to the lowest in early February, and then increases substantially from mid-February. The CVS events account for over 40% of the snowstorms on the ten-day time scale, peaking at 100% in early January before decreasing. Because the NECV intensity significantly affects the precipitation distribution in Northeast China, it is necessary to classify the snowstorm events according to the cold vortex intensity for further study. Using the K-means clustering method, the silhouette coefficient values over 3–7 clusters are calculated respectively based on the lowest geopotential height of cold vortex during CVS events. It is found that the silhouette coefficient value is the highest over three clusters. Therefore, the 52 CVS events are objectively classified into three types. The line passing through the cold vortex center in each CVS process is used to represent the moving track of the cold vortex. Figure 2 b shows the moving tracks for the three types of cold vortexes. The first type of NECVs are mainly generated north of the Lake Baikal and its surroundings, moving long distances eastward, and most of the southern-path type cold vortexes disappear on the west coast of the Sea of Japan. The second type of NECVs are mainly generated in southeast Lake Baikal and moves southeastward. Most cold vortexes travel through the Northeast China, with some of them moving eastward into the sea. The third type of NECVs are mainly generated in the upstream and north of the Lena River, where most move within Russia, indicating northerly positions. Only a few of them pass through the northern region of Northeast China. Through the composite path obtained by averaging the starting and ending positions of the moving tracks for each type of NECVs, it is evident that the tracks of the second type are the southernmost, while those of the third type are the northernmost. Thus, we define the three types of cold vortexes as northwestern-path type, southern-path type, and northern-path type. Table 1 Cold vortex snowstorm events database. No. Date of event (Unit: Year/Month/Day) Maximum snowfall on the peak day (unit: mm) Number of stations with snowfall > 10 mm on the peak day Path type 1 1954/2/11 12.9 5 1 2 1954/2/25 14.9 7 1 3 1956/2/27 − 2/28 21.2 10 2 4 1957/12/17 − 12/18 45.8 36 2 5 1958/1/15 24.3 8 2 6 1959/12/2 20.3 16 1 7 1962/2/10 41.1 87 1 8 1965/12/14 14.6 6 1 9 1966/1/10 11.7 8 3 10 1970/12/12 26.9 33 1 11 1971/2/16 − 2/17 17.5 8 1 12 1975/12/4 17.7 7 1 13 1977/12/15 − 12/16 23.2 23 3 14 1978/12/1 14.1 7 1 15 1979/1/29 13.3 7 2 16 1979/12/19 51.4 97 3 17 1980/12/2 21.4 21 2 18 1980/12/22 14.1 9 2 19 1981/1/1 20.1 9 2 20 1984/12/9 19.8 22 3 21 1990/1/28 23.1 7 1 22 1990/2/18 − 2/20 22 46 1 23 1990/12/1 23.3 27 2 24 1990/12/22 13.2 8 1 25 1991/12/10 12.6 8 1 26 1993/12/10 13.7 5 3 27 1996/1/14 14.8 5 3 28 1997/1/1 27.4 53 2 29 1997/2/28 19.5 18 1 30 2000/1/2 20.6 38 1 31 2000/1/6 19.5 19 1 32 2002/1/7 34.7 11 1 33 2004/2/21 38.5 20 1 34 2007/2/14 14.3 12 1 35 2007/2/22 15.9 6 3 36 2008/12/3 19.7 16 1 37 2009/1/22 13.1 9 3 38 2009/2/13 43.9 72 1 39 2010/2/24 − 2/25 30.1 34 1 40 2010/12/10 24.5 20 1 41 2012/12/3 25.4 25 1 42 2014/12/1 27.7 20 1 43 2015/2/21 − 2/22 14.8 15 2 44 2016/1/18 15.5 6 2 45 2016/2/12 19.7 5 1 46 2016/12/22 29.2 14 3 47 2017/2/22 13.5 7 1 48 2018/2/28 14.4 11 2 49 2018/12/3 18.4 16 1 50 2019/12/16 18.5 11 1 51 2019/12/29 11.4 6 2 52 2021/2/14 21.3 20 2 The snowfall distribution during CVS events varies significantly with different types. Figure 3 shows the cumulative snowfall of the three types of CVS events. The snowfall area of the northwestern-path type is located more eastward and southward compared with the other two types, with the heavy snowfall occurring in the southeast part of Northeast China. The southern-path type has two large snowfall regions, one in the central-southern part of Liaoning Province and the other in the southeast part of Heilongjiang Province, forming a snowfall belt that decreases from south to north. For the northern-path type, the large snowfall region lies in the southwest part of Northeast China, extending further westward than the other two types. These differences in snowfall location and intensity are likely due to the varying positions and intensities of cold vortexes. In the next section, the circulation patterns and the vertical configurations will be investigated among the three types of CVS events. 4. Large-scale circulation and vertical configurations 4.1 Upper-level situation To investigate whether there are differences in the atmospheric circulation backgrounds of the three types of CVSs, a composite analysis is conducted on the circulation pattern on the peak day for each type. Figure 4 shows that during the three types of CVS events, the middle troposphere all exhibits a “+ − + −” Rossby wave train extending from east of the Ural Mountains to the West Pacific region. The area from the Lake Baikal to the west of Northeast China shows significant negative anomalies due to the influence of cold vortex, while the downstream Okhotsk Sea blocking shows significant positive anomalies. The Okhotsk Sea blocking impedes the eastward movement of the cold vortex, prolonging the influencing time of the cold vortex on Northeast China. In addition, the area east of the Ural Mountains in the upstream of the cold vortex exhibits obvious positive anomalies. As a result, the double blocking pattern over the Ural Mountains–Okhotsk Sea region substantially strengthens the meridional circulation in the mid-high latitudes (Xie and Bueh 2017), facilitating the formation and development of the NECVs. Moreover, similar to the negative phase of the western Pacific pattern (Xie and Bueh 2012 ), negative geopotential height anomalies in the Northwest Pacific in conjunction with the Okhotsk Sea blocking provide necessary conditions for the southward development of the cold vortex. Despite similar circulation configurations, the three types of NECVs vary in their central locations and intensities, as well as their influencing mechanisms on snowfall in Northeast China. The southern-path type with a farthest center exhibits the weakest central intensity, whereas the northern-path type is the opposite. In terms of the anomaly field, the northwestern-path type is mainly located near the Lake Baikal in a relatively wide zonal span. The high-pressure ridge over the Ural Mountains pushes the Siberian cold air southward along the rear of the cold vortex to the vortex center, which enables the maintaining of the cold vortex structure. At 850 hPa, there is an anomalous cyclone in front of the cold vortex affecting the Northeast China, leading to strong baroclinicity in the troposphere. The configuration of the anomalous cyclone and the downstream anticyclone cause the convergence in the southern part of Northeast China, resulting in heavy snowfall in the southeast part of the Northeast China. The southern-path type located in the North China-Northeast China region in conjunction with the downstream Okhotsk Sea blocking form a blocking circulation of “east high, west low” pattern. The Okhotsk Sea blocking is located more northward than the other two types, resulting in a more northerly influence of the southerly airflow and consequently a more northerly snowfall area. From the aspect of wind anomaly field, the center of anomalous cyclone at 850 hPa coincides well with the anomalous center at 500 hPa. 4.2 Surface situation Figure 5 displays the composite sea-level pressure (SLP) and corresponding anomaly fields on the peak day of the three types of CVSs. The northwest side of the main body of each type of cold vortex is controlled by positive anomalies, and the supplement from southward-moving cold air helps maintain the cold vortex. The high-pressure center of the northwestern-path type is located north of the Lake Baikal. The high-pressure center of the southern-path type is located over the Lake Baikal area, and its intensity is considerably weaker than the other two types due to weaker cold air from higher latitudes. The high-pressure center of the northern-path type is located near the Ural Mountains, and the main body of the downstream cold vortex east of the high-pressure center is relatively deep and controlled by negative anomalies throughout the entire layer. The stable maintenance of cold vortex is conducive to the continuous downstream transport of cold air. For all types of CVS, surface inverted troughs form and develop over regions from the East China Sea to the Yellow Sea and Bohai Sea on the SLP field. Specifically, for the southern-path and northern-path types, the inverted troughs develop into closed centers or even cyclones, providing strong uplifting conditions and moisture transport for the system. Moreover, the blocking high downstream of the inverted trough makes it move slowly, favoring the continuous transport of moisture. Additionally, the regions where the high pressure confronts with low pressure in the northwestern-path and northern-path types are on the east side of the inverted trough, whereas that of the southern-path type is located at the top of the inverted trough. The difference in moisture transport paths finally results in different snowfall regions. 5. Causes of cold vortex snowstorm Wetzel et al. (2001) identified five factors influencing winter precipitation, which are moisture, stability, dynamic lifting, snowfall efficiency, and thermal conditions. Therein, sufficient moisture and strong upward motion are necessary conditions for snowfall (Yang et al. 2016; Sun et al. 2016 ; Hu et al. 2017 ). This section discusses the causes of extreme heavy rain and snow from the perspectives of moisture, thermal conditions, and dynamic lifting conditions. 5.1 Moisture conditions As sufficient moisture is crucial for the maintenance of snowstorms, an analysis is performed from the aspect of moisture flux. Figure 6 displays the whole layer moisture flux and moisture divergence for three types of CVSs in Northeast China, which all show a clear decreasing trend from south to north. The spatial distribution patterns of moisture flux and moisture divergence for northwestern-path type and northern-path type are similar, with moisture flux exceeding 3.5 kg·m − 1 ·s − 1 over nearly half of the Northeast China. As for transport path, the moisture is mainly transported through westerly and southwesterly flows in the northwestern-path type, through more southerly flows in the southern-path type, and through southwesterly flows in the northern-path type. Compared with the other two types, the northwestern-path type affects a broader range in Northeast China. The large-value regions of moisture transport flux of the northwestern-path and northern-path types are located in the southern part of Northeast China, while that of the southern-path type is in the east of Northeast China. These features correspond well with the snowfall regions of three types of CVSs. Regarding the moisture flux divergence, the extreme value of the northwestern-path type ranges from − 4 to − 3 (in unit of 10e − 6 kg·m − 2 ·s − 1 , the same below), while that of the southern-path type, which is the weakest, ranges from − 2 to − 1. The moisture flux convergence of the northern-path type is the most pronounced, with the large value reaching about − 6. The large-value regions of moisture flux divergence for the three types correspond well to the maximum snowfall regions in Fig. 3 . 5.2 Dynamic and thermodynamic mechanisms Figure 8 depicts the zonal cross-sections of temperature advection, pseudo-equivalent potential temperature, and vertical velocity along the snowstorm center on the peak days for the three types of CVSs. The intersection of cold air at the rear of the cold vortex and warm, moist air leads to baroclinic energy conversion, resulting in the rising of warm air. Strong upward motion is found at the center of the warm advection. Since the above section has revealed that the upper-level divergence fields of the southern-path and northern-path types are relatively strong, the vertical upward motions of these two types are stronger than that of the northwestern-path type due to the pumping effect in the upper level. In the northwestern-path type, the vertical velocity reaches a maximum of − 0.5 hPa·s − 1 in the large-value area, while the maximum vertical velocity in the southern-path and northern-path types exceed − 0.9 hPa·s − 1 . Especially in the northern-path type, more upward kinetic energy is converted due to the strong warm advection, allowing the upward extension of strong vertical motion to the middle troposphere. Besides favorable moisture and vertical motion conditions, the frontal process caused by the intersection of cold and warm advection also plays a vital role in forming large-scale precipitation. In the southern-path type, significant temperature difference is observed between the cold and warm air masses, with the cold air mass being particularly strong. The area west of the snowfall region is controlled by cold advection from the surface to the middle troposphere, causing the frontal zone to tilt westward with altitude. As the cold vortex moves eastward, a cold cushion forms near the surface, which produces heavy snow by forcing the warm, moist air to climb along this cold cushion. As a result, the heavy snowfall area is at the rear of the frontal zone. In contrast, the northern-path type has a steep frontal surface, where the cold air mass is comparable to the warm air masses in the intensity, or the warm air mass is even stronger. This results in a slow-moving frontal surface and warm advection dominating the lower to middle troposphere. The strong upward motion lifts the warm-moist air and causes snowfall at the front of the frontal zone. This difference in the falling areas of snow is caused by the varying positions of the main body of cold vortex. For the southern-path type, as the main body is located farther south, the cold advection at its rear affects Northeast China. While for the northern-path type, the main body affects Northeast China by transporting cold air eastward and southward. The vortex or deep trough detached from the cold vortex may remain a certain distance from Northeast China, leading to relatively weak cold air penetrating into this region. As for the northwestern-path type, the main body of cold vortex is located between those of the northern-path and southern-path types, and the cold air carried exerts a moderate impact on Northeast China, resulting in a snowstorm area near the frontal zone. Figure 9 b shows that the southern-path type is influenced by the cold advection at the rear of the cold vortex. The cold-dry air at upper levels penetrates into the middle levels, with a noticeable dry tongue extending to the mid-level. The notable dry intrusion promotes the persistence of the cold vortex system. There is evident moisture lifting on both sides of the low-level snowstorm center. This is because the mid-level dry-cold air intrusion forces the low-level moisture to rise. As moisture on the west side of the snowstorm center ascends to a certain height along the frontal surface, it encounters the dry layer in the middle troposphere, which impedes the vertical transport and diffusion of moisture and energy, leading to the accumulation of a large amount of unstable energy with high temperature and high humidity. Simultaneously, the presence of dry layer enhances the convective instability, which facilitates the occurrence, development and enhancement of heavy precipitation events. From the perspective of potential vorticity (PV), the high-level PV core extends downward into the middle levels, with PV values reaching 2–4 PVU at 300–400 hPa on the west side of the snowfall area. The PV core rapidly develops downward, extending to the east of the surface snowfall area and inducing the initiation and development of surface systems. In summary, the dry intrusion process is essential for the persistence of cold vortex and the enhancement of snowfall. The northwestern-path type also has dry intrusion and downward PV transportation, but the intensity is weaker than that of the southern-path type. The minimum relative humidity of the dry layer is 45%. While the dry layer inhibits the vertical transportation of moisture and energy, it does not prevent the horizontal diffusion. Consequently, due to the sustained intense moisture flux transport, the northwestern-path type causes a broader area of heavy snowfall in Northeast China. In contrast, the dry intrusion is less pronounced for the northern-path type, and there is no notable PV core at upper levels. This is because the deep trough or low vortex which splits from the cold vortex has not yet fully entered Northeast China. Nevertheless, the warm-moist southwesterly condensates in front of the low-level cyclonic shear. As a result, the PV core beneath the heating zone is substantially enhanced, which intensifies the surface cyclone in front of the snowstorm center, thereby promoting the development of CVS. The cold vortex is a large-scale system. To explore its evolution, we analyze the cross-sectional characteristics of vorticity by extending the latitudinal range to 10 longitudes around the snowstorm center, as shown in Fig. 10 a. As for the northwestern-path type, the vorticity and positive vorticity advection in front of the vortex are notably weaker than other types, indicating that the cold vortex is still in its developing stage. The vorticity of the southern-path type is the strongest among the three types, with a well-developed deep vertical structure throughout the troposphere at the snowstorm center. Additionally, the positive vorticity advection in front of the cold vortex increases with height, indicating that this vortex is in the mature stage. Although the vorticity of the northern-path type is larger than that of the northwestern-path type, there is no obvious positive vorticity above the snowstorm center, which is a certain distance away from the snowstorm center. The positive vorticity advection in front of the vortex, in conjunction with the warm advection in the lower layer, further enhances the ascending motion. The intensified ascending motion accelerates the convergence at low levels, strengthening the low-level cyclonic circulation. As a result of the combined effect of strong warm advection and positive vorticity advection, the low vortex that splits from the main body of the cold vortex gradually evolves into a deep and nearly stable system. 6. Conclusions This study objectively identifies the cold vortex systems in Northeast China and defines the CVS events in this region. Based on the intensity of cold vortexes, the K-means clustering method is used to classify the CVS events into three types. Then, the atmospheric circulation system and the vertical configurations of CVS events in winter in Northeast China are discussed. The main conclusions are as follows. CVS events account for over 40% of all snowstorm events in Northeast China. According to the intensity of cold vortexes, the K-means clustering method is used to classify the CVS events into three types: northwestern-path type, southern-path type, and northern-path type. The main body of cold vortex of the northwestern-path type is located near the Lake Baikal, with strong baroclinicity. An abnormal cyclone is in front of the cold vortex at 850 hPa. The moisture flux path is mainly westerly and southwesterly routes, and the moisture convergence area is located over the southern part of Northeast China. The main body of cold vortex of the southern-path type is the southernmost, which locates over the region of North China-Northeast China. At 500 hPa, there is an “east high, west low” blocking circulation pattern. The center of 850 hpa abnormal cyclone coincides with that at 500 hPa. A cyclone initiates near the surface, and in the downstream areas there is anticyclone high pressure. The moisture path is southward The main body of cold vortex of the northern-path type old vortex is located in the northwest of the Lake Baikal, with a more northern position than other types. At 500 hPa, the cold vortex and the Ural Mountains high pressure constitute a “west high, east low” circulation pattern. At 850 hPa, there are two abnormal cyclone centers, one near the main body of the cold vortex and the other in Northeast China. The main body of the cold vortex in the north continuously transports cold air southward, which deepens the southward-moving short-wave trough into a low vortex or deep trough. A cyclone initiates near the surface, and the moisture path is southwestward. Compared with other types, the moisture convergence, which is located in southern part of Northeast China, is the most remarkable among the three types. Since the process of CVS of the southern-path type is relatively complex, we have made a schematic diagram, as shown in Fig. 11 . It can be seen that the cold vortex of the southern-path type is a deep system, with a significant temperature difference between the cold and warm air masses on both sides of the snowfall area. The warm-moist air masses climb up along the cold wedge and produce heavy snowfall, with the intense snowfall located at the rear of the frontal zone. The penetration of high-level cold-dry air into the middle atmosphere inhibits the vertical movement of moisture, allowing the accumulation of unstable energy and increasing convective instability. The high-level PV core extends downward to the middle level, in conjunction with the increasing of positive vorticity advection in front of the cold vortex with height, inducing the development of surface systems, which supports the persistence of the cold vortex and the enhancing of snowfall. For the northwestern-path type, the coupling characteristics of high levels and low levels are between those of the northern-path type and southern-path type. Although the vorticity of the northern-path type is larger than the northwestern-path type, there is no obvious positive vorticity above the snowstorm center. The snowstorm of the northern-path type is mainly caused by strong upward motion lifting the warm, moist air, and the snowfall area is in front of the frontal zone. In the front part of the low-level cyclonic shear zone, the warm-moist southwesterly air flow undergoes a significant condensation process, causing a notable enhancement of the PV core beneath the heating zone, promoting the deepening of the surface cyclone in front of the snowstorm center. This study establishes a database of CVS events and explores the influencing mechanisms for different types of CVS events. In the future, we will analyze the possible precursor signals triggering the CVSs, such as the SST anomalies preceding the occurrence of extreme snowstorms, the formation of the cold vortex associated with the splitting of polar vortex, and the relationships of CVSs with the elements like polar sea ice, sea surface temperature, and so on. Declarations All authors disclosed no relevant relationship. Author Contribution Author 1 - Yue Wang:Methodology, Software, Investigation, Writing - Original Draft;Author 2(Correspondence) - Chenghan Liu: Data Curation, Writing - Original Draft;Formal Analysis.Author 3- Yihe Fang: Visualization, Investigation;Author 4-Haoran Jiao:Resources, Supervision;Author 5-Jing Liu:Software, ValidationAuthor 6-Feng Zhou: EditingAll authors reviewed the manuscript Acknowledgment: This work was jointly supported by the Research Project of the Institute of Shenyang Atmospheric Environment, CMA (Grant No. 2022SYIAEKFMS10); the National Key Research and Development Project (Grant No. 2018YFC1505601); the National Natural Science Foundation of China (Grant No. 42005037); and Liaoning Provincial Meteorological Bureau Annual Foundation Project (Grant No. 202302). References Agel L, Barlow M, Qian JH et al (2015) Climatology of daily precipitation and extreme precipitation events in the northeast United States. J Hydrometeor 16:2537–2557 Chen LQ, Chen SJ, Zhou XS et al (2005) A numerical study of the MCS in a cold vortex over northeastern China. Acta Meteorologica Sinica 63(2):173–183 Ding T, Yuan Y, Zhang JM et al (2019) The hottest summer in China and possible causes. J Meteorological Res 33(4):577–592 Fang YH, Chen HS, Lin Y et al (2021) Classification of northeast China Cold Vortex activity paths in early summer based on K-means clustering and their climate impact. Adv Atmos Sci 38(3):400–412 Fan ZQ, Zhu KF, Xue M (2023) Decay processes and statistical characteristics of continental Northeast China Cold Vortex from April to September. Acta Meteorologica Sinica 81(5):727–740 Hu KX, Lu RY, Wang DH (2011) Cold vortex over Northeast China and its climate effect. Chin J Atmos Sci 35(1):179–191 Hu SQ, Cao ZC, Chen T (2017) Diagnostic analysis of a historical extreme snowprocess in south of Shandong Province. Plateau Meteor 36(4):984–992 He LF, Chyi D, Yu W (2022) Development mechanisms of the Yellow Sea and Bohai Sea cyclone causing extreme snowstorm in Northeast China. J Appl Meteor Sci 33(4):385–399 Hao LS, He LY, Ma N (2023) Climatic characteristics of northeast cold vortex and its impact on summer precipitation in the Haihe river basin. Acta Meteorologica Sinica 81(4):559–568 Kentarchos AS, Davies TD (1998) A climatology of cut-off lows at 200 hPa in the Northern Hemisphere, 1990–1994. Int J Climatol 18(4):379–390 Liu G, Qu MH, Feng GL et al (2019) Application study of monthly precipitation forecast in Northeast China based on the cold vortex persistence activity index. Theor Appl Climatol 135:1079–1090 Liu DH, Zhu WJ (2021) Temporal and spatial variation characteristics of northeast cold vortex in winter. J Meteorological Sci 41(3):331–338 Michelangeli PA, Vautard R, Legras B (1995) Weather regimes: Recurrence and quasi stationarity. J Atmos Sci 52:1237–1256 Nieto R, Gimeno L, Torre DL, L.,., Climatological features of cutoff low systems in the Northern Hemisphere. J Climate, 18(16): 3085–3103., Nakamura J, Lall U, Kushnir Y et al (2005) 2009.Classifying North Atlantic tropical cyclone tracks by mass moments. J. Climate, 22(20): 5481 – 5494 Peng YH, Yi DJ, Wang T, S, et al (2019) Clustering analysis of typhoon track in the North-west Pacific Ocean. Mar Forecasts 36(5):63–70 Rousseeuw PJ (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65 Roller CD, Qian JH, Agel L et al (2016) Winter weather regimes in the northeast United States. J Clim 29:2963–2980 Ren HL, Wu YJ, Bao Q et al (2018) China multi-model ensemble prediction system version 1.0 (CMMEv1.0) and its application to flood-season prediction in 2018. J Meteorological Res 33:542–554 Ren L, Yang YM (2021) Dynamic and thermal characteristics of a heavy rain caused by MCC at bottom of Northeast Cold Vortex. J Arid Meteorol 39(1):65–75 Sun L, Zheng XY, Wang Q (1994) The climatological characteristics of Northeast Cold Vortex in China. Q J Appl Meteorol 5(3):297–303 Sun DG, Huang BF, Xue YB et al (2016) Analysis on the difference of airflow structure of three ocean-effect snowstorms in Shandong Peninsula. Plateau Meteor 35(3):800–809 Wetzel SW, Martin JE (2001) An operational ingredients based methodology for forecasting midlatitude winter season precipitation. Wea Forecast 16(1):156–167 Wang B, Chen GS, Liu F (2019) Diversity of the Madden-Julian Oscillation. Sci Adv 5(7):eaax0220 Xie ZW, Bueh C (2012) Low frequency characteristics of northeast China cold vortex and its background circulation pattern. Acta Meteorologica Sinica 70(4):704–716 Xie ZW, Bueh C (2015) Different types of cold vortex circulations over Northeast China and their weather impacts. Mon Wea Rev 143(3):845–863 Xu JG, Zhao LQ, Jiang FY et al (2018) Statistical analysis of physical quantity fields for a heavy snowstorm in Southeastern Inner Mongolia. Meteorological Sci Technol 46(5):919–931 Xiao YQ, Xiao XH, Lou PX et al 2020.A comparative analysis of two snowstorms in Shanxi province. Desert Oasis Meteorol, 14(2): 27–35 Yang LM, Liu W (2016) Cause analysis of persistent heavy snow processes in the Northern Xinjiang. Plateau Meteor 35(2):507–519 Yang J, Zheng Y, Xia Y, W. M., et al (2020) Numerical analysis of a squall line case influenced by northeast cold vortex over Yangtze–Huaihe River valley. Meteorological Monthly 46(3):357–366 Yang B, Wang LJ (2021) Statistical characteristics and causes of different types of northeast cold vortex from May to September. Trans Atmos Sci 44(5):773–781 Yang L, Zheng YG (2023) Observational characteristics of thunderstorm gusts in Northeast China and their association with the Northeast China Cold Vortex. Acta Meteorologica Sinica 81(3):416–429 Zheng YQ, Yu JH, Wu QS et al (2013) K-means clustering method for classification of the northwestern Pacific tropical cyclone tracks. J Trop Meteorol 29(4):607–615 Zhang GL, Yao XJ, Sun YG et al (2018) Diagnostic analysis of a snowstorm in Dxinganling region. Meteorological Sci Technol 46(5):971–978 Additional Declarations No competing interests reported. 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-4907967","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350941347,"identity":"d1d587fa-6c34-491d-ac84-173383aeb45e","order_by":0,"name":"Yue Wang","email":"","orcid":"","institution":"Shenyang Meteorological Service","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":350941348,"identity":"3f0b740c-4f06-4f95-abd9-5e73b022d02a","order_by":1,"name":"Chenghan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACPmYGBmYQg42B+cCBDz+I0MIG18LGlnhwZg8xWhigWhjYeIwPc7ARo4Wdx/BzAYNdYp98z4fDDDwM8vxiBwg5jMdYegZDcmIbG++GwwUWDIYzZycQ1GLGzMNwAKJlBg9DgsFt4rXwPDjMw0aiFgZitbAVS/MwJBu3saUZAANZgrBf+PkPb/zMw2AnO7/58OMPH37YyPNLE9ACBoz/4EwJIpSPglEwCkbBKCAIACh1NHJvktXEAAAAAElFTkSuQmCC","orcid":"","institution":"Liaoning Meteorological Observatory","correspondingAuthor":true,"prefix":"","firstName":"Chenghan","middleName":"","lastName":"Liu","suffix":""},{"id":350941349,"identity":"5008c3e3-2e20-4c42-836b-98c9f9249891","order_by":2,"name":"Yihe Fang","email":"","orcid":"","institution":"Liaoning Provincial Climate Center, Liaoning Provincial Meteorological Administration","correspondingAuthor":false,"prefix":"","firstName":"Yihe","middleName":"","lastName":"Fang","suffix":""},{"id":350941350,"identity":"e5c62902-a38c-4208-a980-9b0219e0725f","order_by":3,"name":"Haoran Jiao","email":"","orcid":"","institution":"Liaoning Meteorological Observatory","correspondingAuthor":false,"prefix":"","firstName":"Haoran","middleName":"","lastName":"Jiao","suffix":""},{"id":350941351,"identity":"a0c49d91-842a-440b-be41-012c5d962785","order_by":4,"name":"Jing Liu","email":"","orcid":"","institution":"Shenyang Meteorological Service","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":350941352,"identity":"0d021a7a-467b-49e6-8200-bf60bd1a2ab6","order_by":5,"name":"Feng Zhou","email":"","orcid":"","institution":"Shanxi Provincial Atomspheric Sounding Technology Support Center","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-08-13 14:55:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4907967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4907967/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64365841,"identity":"3c28ad5e-0777-4bff-856f-2d36da91660e","added_by":"auto","created_at":"2024-09-12 08:10:17","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1700855,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical location of Northeast China and the spatial distribution of the 245 meteorological stations in Northeast China.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/1b1dab980a76f148e62b35ce.jpeg"},{"id":64365836,"identity":"1a08a845-7d6a-4e96-850b-bf64deddd0db","added_by":"auto","created_at":"2024-09-12 08:10:17","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89918,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Ten-day variations of the overall snowstorm and cold vortex snowstorm (CVS) frequencies from December to February of the next year over the period of 1951 to 2021. (b) Moving paths (colored thin lines) and the composite paths of the northwestern-path type (red thick line), southern-path type (blue thick line), and northern-path type (yellow thick line) of CVSs.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/9300124c7b2c93edecbeabd0.jpeg"},{"id":64365839,"identity":"50ca8696-b78d-434a-88ce-998a9449f757","added_by":"auto","created_at":"2024-09-12 08:10:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1533545,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of the accumulated snowfall on the peak days of CVS events for (a) northwestern-path type, (b) southern-path type, and (c) northern-path type. The red dots represent the composite maximum snowfall (unit: mm).\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/20ec9209c8ee899ec6bafb57.jpeg"},{"id":64366286,"identity":"c72a2455-a185-4ee1-b6e1-7c078b603223","added_by":"auto","created_at":"2024-09-12 08:18:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2541097,"visible":true,"origin":"","legend":"\u003cp\u003eComposite geopotential height fields (contour, unit: dagpm) and associated anomalies (shaded, unit: dagpm) at 500 hPa on the peak day of the CVS process for (a) northwestern-path type, (b) southern-path type, and (c) northern-path type. The thick solid lines represent 544 dagpm, and the contour interval is 8 dagpm. The arrows indicate the 850 hPa wind field (vector, unit: m·s\u003csup\u003e−1\u003c/sup\u003e). The black dots indicate the anomalies passing the significance test at the 90% confidence level.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/348055abe698b0994590371e.jpeg"},{"id":64365838,"identity":"fa8c9cd0-d57e-48e4-8b36-405320651171","added_by":"auto","created_at":"2024-09-12 08:10:17","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2135213,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 4, but for sea-level pressure field (contour, unit: hPa) and associated anomalies (shaded, unit: hPa). The thick solid lines represent 1020 hPa, and the contour interval is 4 hPa.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/779d4eeb9757621e3f27a681.jpeg"},{"id":64366284,"identity":"f5adcd2b-60aa-493f-97f9-03c43de0c780","added_by":"auto","created_at":"2024-09-12 08:18:18","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1391822,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 4, but for moisture flux divergence (colored, unit: 10 e\u003csup\u003e−6\u003c/sup\u003e kg·m\u003csup\u003e−2\u003c/sup\u003e·s\u003csup\u003e−1\u003c/sup\u003e) and vertically integrated moisture flux (vector, unit: 10 kg·m\u003csup\u003e−1\u003c/sup\u003e·s\u003csup\u003e−1\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/a3d71df1c5d0013cd8f1e8a6.jpeg"},{"id":64365846,"identity":"5727fe22-dce9-415d-9b22-bc7e21907110","added_by":"auto","created_at":"2024-09-12 08:10:18","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2821498,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 4, but for 200 hPa wind field (unit: m·s\u003csup\u003e−1\u003c/sup\u003e) and corresponding divergence field (unit: 10\u003csup\u003e5\u003c/sup\u003e·s\u003csup\u003e−1\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/bf531194dfbd990ce910c913.jpeg"},{"id":64366283,"identity":"800553fc-ea4e-489d-a935-072f7b1eb20e","added_by":"auto","created_at":"2024-09-12 08:18:17","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1531630,"visible":true,"origin":"","legend":"\u003cp\u003eZonal cross-section diagrams of temperature advection on the peak days of CVS events along the snowstorm center for the (a) northwestern-path (b), southern-path, and (c) northern-path \u0026nbsp;types of CVSs (unit: 10\u003csup\u003e−5 \u003c/sup\u003eK·s\u003csup\u003e−1\u003c/sup\u003e). The red contours and black lines denote pseudo-equivalent potential temperature (unit: K) and vertical velocity (unit: Pa·s\u003csup\u003e−1\u003c/sup\u003e), respectively. The purple triangles denote the longitudes of snowstorm centers.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/0d002773d868bbcf341bd3ea.jpeg"},{"id":64365843,"identity":"bb1187ae-9ef8-4b50-9a02-03ad15c97972","added_by":"auto","created_at":"2024-09-12 08:10:18","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1161259,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8, but for relative humidity (unit: %). The red contours represent the potential vorticity (unit: PVU).\u003c/p\u003e","description":"","filename":"image10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/31617aaa135c6f94ecb46d5c.jpeg"},{"id":64365844,"identity":"52736832-687a-4c0c-9fcf-8a862e7d525e","added_by":"auto","created_at":"2024-09-12 08:10:18","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1253955,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 8, but for vorticity (unit: 10\u003csup\u003e−5\u003c/sup\u003e·s\u003csup\u003e−1\u003c/sup\u003e). The red contours denote the relative vorticity advection (unit: 10\u003csup\u003e−10\u003c/sup\u003e·s\u003csup\u003e−2\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"image11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/0ea694e189fdad7504afd673.jpeg"},{"id":64366285,"identity":"c6acefa0-fcd5-4835-a28b-6f948157b8bc","added_by":"auto","created_at":"2024-09-12 08:18:18","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":2631278,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the mechanisms of the cold vortex snowstorms ofthe southern-path type.\u003c/p\u003e","description":"","filename":"image12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/482f516e79748574cd2a4cf6.jpeg"},{"id":67507987,"identity":"a78e941f-04a0-4308-9c46-9b05c778d5fa","added_by":"auto","created_at":"2024-10-25 19:32:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":47244437,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4907967/v1/342f0ec3-2e82-486e-af09-0c922409c4e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classification of Cold Vortex Snowstorms in Northeast China Based on K- means Clustering Algorithm and their Features","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCutoff lows are closed cold system at low latitudes developed from the upper-level cold trough in mid-high latitudes that deepens and develops southward (Nieto et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In Northeast China, the cutoff low system featured by persistence and quasi-stationary is referred to as the Northeast Cold Vortex (NECV). As a fundamental work for NECV related research, the identification of the NECV can be classified into subjective identification (Kentarchos et al. 1998), objective identification (Hu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kentarchos et al. 1998), and a combination of both.\u003c/p\u003e \u003cp\u003eThe NECV exerts significant impacts on regional climate (Ren et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), especially on precipitation and temperature (Liu et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Current studies on cold vortex have focused on the following aspects: the mesoscale convective systems influencing cold vortexes through numerical simulations (Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the mechanisms of cold vortex triggering extreme weather events rainstorm, snowstorm, and severe convection (Ren et al. 2021; Yang et al. 2023; He et al. 2023), and the climatic statistical characteristics of cold vortexes (Yang et al. 2021; Liu et al. 2020; Hao et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSnowstorms are among the major meteorological disasters in Northeast China, severely impacting transportation, electricity, and agricultural production. The NECV is a crucial weather system affecting the snowstorm events in this region (He et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous research has pointed that the configuration of the southward moving dry cold air at the rear of the upper-level cold vortex and the low-level southwest jet as well as the strengthened convergence in front of the cold vortex jointly provided favorable circulations for snowstorm event (Zhang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Snowstorms induced by cold vortexes are accompanied by strong cold advection throughout the entire layer (Xu et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). A comparison of two snowstorm processes have revealed that the NECV facilitates the accumulation and southward intrusion of cold air, providing dynamic lifting conditions for the ascent of warm and humid air and thereby enhancing the impact of the southern branch trough on snowstorms (Xiao et al. 2020).\u003c/p\u003e \u003cp\u003eSome efforts have been made for NECV classification in previous studies. Xie and Bueh (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) used a rotating EOF analysis method to calculate the circulation fields on the peak value days of the NECV system and further divided the NECV into four types: Yenisei River type, Lake Baikal type, Ural\u0026thinsp;\u0026minus;\u0026thinsp;Yakutsk type, and Okhotsk Sea\u0026thinsp;\u0026minus;\u0026thinsp;Arctic Ocean type. Sun et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) classified the NECVs into northern vortex, middle vortex, and southern vortex according to their positions. Additionally, machine learning algorithms have been increasingly applied in objective classification of weather systems. For example, based on Nakamura et al. (2009), Zheng et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Peng et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) adopted the K-means clustering algorithm to classify the tracks of tropical cyclones over the northwest Pacific Ocean according to feature parameters including typhoon locations, intensities, track lengths, and track directions. Wang et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also objectively classified the propagation paths of the Madden-Julian Oscillation (MJO) using the K-means clustering algorithm.\u003c/p\u003e \u003cp\u003eIn this study, the CVSs are clustered based on the mean geopotential height of the NECVs\u0026rsquo; inner circle at 500 hPa using the machine learning method of K-means clustering. The following aspects are comprehensively considered during clustering: the NECV\u0026rsquo;s initiation position, moving direction, moving speed (distance), and especially the relationship between the NECV and snowstorms. On this basis, the temporal characteristics and circulation patterns for different types of CVSs are illustrated, and the correspondence between the snowfall area and the spatial structure of large-scale weather systems are further obtained, thus revealing the influencing mechanism of the NECV on snowstorms in Northeast China. The objective is to obtain a comprehensive circulation configuration and conceptual model for NECV-induced snowstorms, thereby providing a reference for forecasting extreme CVS events.\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 adopts the daily precipitation data at 245 meteorological stations in Northeast China (38\u0026deg;\u0026ndash;54\u0026deg;N, 118\u0026deg;\u0026ndash;134\u0026deg;E) in winters (December to February of the next year) of 1951\u0026ndash;2021, which are derived from the China Meteorological Administration (CMA). The study area covers Heilongjiang, Jilin, Liaoning Provinces, and parts of eastern Inner Mongolia. The spatial distribution of stations is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe hourly data from the fifth-generation atmospheric reanalysis of the global climate (ERA5) released by the European Centre for Medium-Range Weather Forecasts (ECMWF) are also used. ERA5 has 30 vertical layers and a horizontal resolution of 0.25\u0026deg;\u0026times;0.25\u0026deg;, covering the period from January 1951 to December 2021. The meteorological elements of ERA5 include geopotential height, sea-level pressure, zonal and meridional wind speed, temperature, specific humidity, and vertical velocity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Objective identification of the northeast cold vortex\u003c/h2\u003e \u003cp\u003eThe weather process that meets the following three conditions can be defined as an NECV event. First, there is a closed low-pressure system on the 500 hPa geopotential height field in the range between 30\u0026deg;N\u0026ndash;80\u0026deg;N and 85\u0026deg;E\u0026ndash;150\u0026deg;E, with the center located between 30\u0026deg;N\u0026ndash;70\u0026deg;N and 95\u0026deg;E\u0026ndash;140\u0026deg;E. Second, this low-pressure system has a cold core, that is, negative temperature advection are found at 500 hPa. Third, the duration should last at least 24 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Definition of cold vortex snowstorm event\u003c/h2\u003e \u003cp\u003eA CVS event is recorded when at least 5 stations in Northeast China experience a cumulative snowfall of more than 10 mm in 24 hours during the occurrence of a NECV event. CVS events in consecutive days or those occur with intermittence of no more than one day are considered as the same event. During a CVS event, the day with the largest number of stations where the snowfall exceeds 10 mm is defined as the peak day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 K-means clustering method\u003c/h2\u003e \u003cp\u003eK-means clustering method, which is able to automatically classify the samples, has been widely applied in circulation classification in climate research (Michelangeli et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Roller et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Agel et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To determine the optimal number of clusters for this method, the silhouette coefficient (Rousseeuw \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) is calculated following the studies of Fang et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Fan et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This coefficient assesses the quality of clustering by measuring intra-cluster cohesion and inter-cluster dispersion over a range of cluster numbers through the distances between vectors. The coefficient value ranges in [\u0026minus;\u0026thinsp;1, 1], where values closer to 1 indicate better clustering results.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Characteristics and classification of cold vortex snowstorm events","content":"\u003cp\u003eAccording to the abovementioned criteria, a total of 52 CVS events over Northeast China are identified (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These 52 events impact a wide range of area with heavy snowfall intensity, which brought freezing rain and snow disaster to Northeast China. According to the cases list on Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the ten-day features of CVSs are statistically analyzed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. In general, snowstorm events mainly occur in early winter and early spring, which is consistent with the variation trend of snowstorm weather in China as reported by previous studies (Wetzel et al. 2001). The snowstorm events in Northeast China occur most frequently in early December and late February. Specifically, the frequency of snowstorms gradually decreases from December, falls to the lowest in early February, and then increases substantially from mid-February. The CVS events account for over 40% of the snowstorms on the ten-day time scale, peaking at 100% in early January before decreasing. Because the NECV intensity significantly affects the precipitation distribution in Northeast China, it is necessary to classify the snowstorm events according to the cold vortex intensity for further study.\u003c/p\u003e \u003cp\u003eUsing the K-means clustering method, the silhouette coefficient values over 3\u0026ndash;7 clusters are calculated respectively based on the lowest geopotential height of cold vortex during CVS events. It is found that the silhouette coefficient value is the highest over three clusters. Therefore, the 52 CVS events are objectively classified into three types. The line passing through the cold vortex center in each CVS process is used to represent the moving track of the cold vortex. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb shows the moving tracks for the three types of cold vortexes. The first type of NECVs are mainly generated north of the Lake Baikal and its surroundings, moving long distances eastward, and most of the southern-path type cold vortexes disappear on the west coast of the Sea of Japan. The second type of NECVs are mainly generated in southeast Lake Baikal and moves southeastward. Most cold vortexes travel through the Northeast China, with some of them moving eastward into the sea. The third type of NECVs are mainly generated in the upstream and north of the Lena River, where most move within Russia, indicating northerly positions. Only a few of them pass through the northern region of Northeast China. Through the composite path obtained by averaging the starting and ending positions of the moving tracks for each type of NECVs, it is evident that the tracks of the second type are the southernmost, while those of the third type are the northernmost. Thus, we define the three types of cold vortexes as northwestern-path type, southern-path type, and northern-path type.\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\u003eCold vortex snowstorm events database.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDate of event (Unit: Year/Month/Day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum snowfall on the peak day (unit: mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of stations with snowfall\u0026thinsp;\u0026gt;\u0026thinsp;10 mm on the peak day\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePath type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1954/2/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1954/2/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1956/2/27\u0026thinsp;\u0026minus;\u0026thinsp;2/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1957/12/17\u0026thinsp;\u0026minus;\u0026thinsp;12/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1958/1/15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1959/12/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1962/2/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1965/12/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1966/1/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1970/12/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1971/2/16\u0026thinsp;\u0026minus;\u0026thinsp;2/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1975/12/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1977/12/15\u0026thinsp;\u0026minus;\u0026thinsp;12/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1978/12/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1979/1/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1979/12/19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980/12/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1980/12/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1981/1/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1984/12/9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990/1/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990/2/18\u0026thinsp;\u0026minus;\u0026thinsp;2/20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990/12/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990/12/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1991/12/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1993/12/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1996/1/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1997/1/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1997/2/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000/1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2000/1/6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2002/1/7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2004/2/21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007/2/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2007/2/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2008/12/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009/1/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2009/2/13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010/2/24\u0026thinsp;\u0026minus;\u0026thinsp;2/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2010/12/10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2012/12/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014/12/1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015/2/21\u0026thinsp;\u0026minus;\u0026thinsp;2/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016/1/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016/2/12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016/12/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2017/2/22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018/2/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018/12/3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019/12/16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019/12/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021/2/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\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 \u003cp\u003eThe snowfall distribution during CVS events varies significantly with different types. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the cumulative snowfall of the three types of CVS events. The snowfall area of the northwestern-path type is located more eastward and southward compared with the other two types, with the heavy snowfall occurring in the southeast part of Northeast China. The southern-path type has two large snowfall regions, one in the central-southern part of Liaoning Province and the other in the southeast part of Heilongjiang Province, forming a snowfall belt that decreases from south to north. For the northern-path type, the large snowfall region lies in the southwest part of Northeast China, extending further westward than the other two types. These differences in snowfall location and intensity are likely due to the varying positions and intensities of cold vortexes. In the next section, the circulation patterns and the vertical configurations will be investigated among the three types of CVS events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Large-scale circulation and vertical configurations","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Upper-level situation\u003c/h2\u003e \u003cp\u003eTo investigate whether there are differences in the atmospheric circulation backgrounds of the three types of CVSs, a composite analysis is conducted on the circulation pattern on the peak day for each type. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that during the three types of CVS events, the middle troposphere all exhibits a \u0026ldquo;+ \u0026minus; + \u0026minus;\u0026rdquo; Rossby wave train extending from east of the Ural Mountains to the West Pacific region. The area from the Lake Baikal to the west of Northeast China shows significant negative anomalies due to the influence of cold vortex, while the downstream Okhotsk Sea blocking shows significant positive anomalies. The Okhotsk Sea blocking impedes the eastward movement of the cold vortex, prolonging the influencing time of the cold vortex on Northeast China. In addition, the area east of the Ural Mountains in the upstream of the cold vortex exhibits obvious positive anomalies. As a result, the double blocking pattern over the Ural Mountains\u0026ndash;Okhotsk Sea region substantially strengthens the meridional circulation in the mid-high latitudes (Xie and Bueh 2017), facilitating the formation and development of the NECVs. Moreover, similar to the negative phase of the western Pacific pattern (Xie and Bueh \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), negative geopotential height anomalies in the Northwest Pacific in conjunction with the Okhotsk Sea blocking provide necessary conditions for the southward development of the cold vortex.\u003c/p\u003e \u003cp\u003eDespite similar circulation configurations, the three types of NECVs vary in their central locations and intensities, as well as their influencing mechanisms on snowfall in Northeast China. The southern-path type with a farthest center exhibits the weakest central intensity, whereas the northern-path type is the opposite.\u003c/p\u003e \u003cp\u003eIn terms of the anomaly field, the northwestern-path type is mainly located near the Lake Baikal in a relatively wide zonal span. The high-pressure ridge over the Ural Mountains pushes the Siberian cold air southward along the rear of the cold vortex to the vortex center, which enables the maintaining of the cold vortex structure. At 850 hPa, there is an anomalous cyclone in front of the cold vortex affecting the Northeast China, leading to strong baroclinicity in the troposphere. The configuration of the anomalous cyclone and the downstream anticyclone cause the convergence in the southern part of Northeast China, resulting in heavy snowfall in the southeast part of the Northeast China.\u003c/p\u003e \u003cp\u003eThe southern-path type located in the North China-Northeast China region in conjunction with the downstream Okhotsk Sea blocking form a blocking circulation of \u0026ldquo;east high, west low\u0026rdquo; pattern. The Okhotsk Sea blocking is located more northward than the other two types, resulting in a more northerly influence of the southerly airflow and consequently a more northerly snowfall area. From the aspect of wind anomaly field, the center of anomalous cyclone at 850 hPa coincides well with the anomalous center at 500 hPa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 \u003cem\u003eSurface situation\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e displays the composite sea-level pressure (SLP) and corresponding anomaly fields on the peak day of the three types of CVSs. The northwest side of the main body of each type of cold vortex is controlled by positive anomalies, and the supplement from southward-moving cold air helps maintain the cold vortex. The high-pressure center of the northwestern-path type is located north of the Lake Baikal. The high-pressure center of the southern-path type is located over the Lake Baikal area, and its intensity is considerably weaker than the other two types due to weaker cold air from higher latitudes. The high-pressure center of the northern-path type is located near the Ural Mountains, and the main body of the downstream cold vortex east of the high-pressure center is relatively deep and controlled by negative anomalies throughout the entire layer.\u003c/p\u003e \u003cp\u003eThe stable maintenance of cold vortex is conducive to the continuous downstream transport of cold air. For all types of CVS, surface inverted troughs form and develop over regions from the East China Sea to the Yellow Sea and Bohai Sea on the SLP field. Specifically, for the southern-path and northern-path types, the inverted troughs develop into closed centers or even cyclones, providing strong uplifting conditions and moisture transport for the system. Moreover, the blocking high downstream of the inverted trough makes it move slowly, favoring the continuous transport of moisture. Additionally, the regions where the high pressure confronts with low pressure in the northwestern-path and northern-path types are on the east side of the inverted trough, whereas that of the southern-path type is located at the top of the inverted trough. The difference in moisture transport paths finally results in different snowfall regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Causes of cold vortex snowstorm","content":"\u003cp\u003eWetzel et al. (2001) identified five factors influencing winter precipitation, which are moisture, stability, dynamic lifting, snowfall efficiency, and thermal conditions. Therein, sufficient moisture and strong upward motion are necessary conditions for snowfall (Yang et al. 2016; Sun et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This section discusses the causes of extreme heavy rain and snow from the perspectives of moisture, thermal conditions, and dynamic lifting conditions.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Moisture conditions\u003c/h2\u003e \u003cp\u003eAs sufficient moisture is crucial for the maintenance of snowstorms, an analysis is performed from the aspect of moisture flux. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e displays the whole layer moisture flux and moisture divergence for three types of CVSs in Northeast China, which all show a clear decreasing trend from south to north. The spatial distribution patterns of moisture flux and moisture divergence for northwestern-path type and northern-path type are similar, with moisture flux exceeding 3.5 kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over nearly half of the Northeast China. As for transport path, the moisture is mainly transported through westerly and southwesterly flows in the northwestern-path type, through more southerly flows in the southern-path type, and through southwesterly flows in the northern-path type. Compared with the other two types, the northwestern-path type affects a broader range in Northeast China. The large-value regions of moisture transport flux of the northwestern-path and northern-path types are located in the southern part of Northeast China, while that of the southern-path type is in the east of Northeast China. These features correspond well with the snowfall regions of three types of CVSs. Regarding the moisture flux divergence, the extreme value of the northwestern-path type ranges from \u0026minus;\u0026thinsp;4 to \u0026minus;\u0026thinsp;3 (in unit of 10e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the same below), while that of the southern-path type, which is the weakest, ranges from \u0026minus;\u0026thinsp;2 to \u0026minus;\u0026thinsp;1. The moisture flux convergence of the northern-path type is the most pronounced, with the large value reaching about \u0026minus;\u0026thinsp;6. The large-value regions of moisture flux divergence for the three types correspond well to the maximum snowfall regions in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Dynamic and thermodynamic mechanisms\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the zonal cross-sections of temperature advection, pseudo-equivalent potential temperature, and vertical velocity along the snowstorm center on the peak days for the three types of CVSs. The intersection of cold air at the rear of the cold vortex and warm, moist air leads to baroclinic energy conversion, resulting in the rising of warm air. Strong upward motion is found at the center of the warm advection. Since the above section has revealed that the upper-level divergence fields of the southern-path and northern-path types are relatively strong, the vertical upward motions of these two types are stronger than that of the northwestern-path type due to the pumping effect in the upper level. In the northwestern-path type, the vertical velocity reaches a maximum of \u0026minus;\u0026thinsp;0.5 hPa\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the large-value area, while the maximum vertical velocity in the southern-path and northern-path types exceed \u0026minus;\u0026thinsp;0.9 hPa\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Especially in the northern-path type, more upward kinetic energy is converted due to the strong warm advection, allowing the upward extension of strong vertical motion to the middle troposphere.\u003c/p\u003e \u003cp\u003eBesides favorable moisture and vertical motion conditions, the frontal process caused by the intersection of cold and warm advection also plays a vital role in forming large-scale precipitation. In the southern-path type, significant temperature difference is observed between the cold and warm air masses, with the cold air mass being particularly strong. The area west of the snowfall region is controlled by cold advection from the surface to the middle troposphere, causing the frontal zone to tilt westward with altitude. As the cold vortex moves eastward, a cold cushion forms near the surface, which produces heavy snow by forcing the warm, moist air to climb along this cold cushion. As a result, the heavy snowfall area is at the rear of the frontal zone. In contrast, the northern-path type has a steep frontal surface, where the cold air mass is comparable to the warm air masses in the intensity, or the warm air mass is even stronger. This results in a slow-moving frontal surface and warm advection dominating the lower to middle troposphere. The strong upward motion lifts the warm-moist air and causes snowfall at the front of the frontal zone. This difference in the falling areas of snow is caused by the varying positions of the main body of cold vortex. For the southern-path type, as the main body is located farther south, the cold advection at its rear affects Northeast China. While for the northern-path type, the main body affects Northeast China by transporting cold air eastward and southward. The vortex or deep trough detached from the cold vortex may remain a certain distance from Northeast China, leading to relatively weak cold air penetrating into this region. As for the northwestern-path type, the main body of cold vortex is located between those of the northern-path and southern-path types, and the cold air carried exerts a moderate impact on Northeast China, resulting in a snowstorm area near the frontal zone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb shows that the southern-path type is influenced by the cold advection at the rear of the cold vortex. The cold-dry air at upper levels penetrates into the middle levels, with a noticeable dry tongue extending to the mid-level. The notable dry intrusion promotes the persistence of the cold vortex system. There is evident moisture lifting on both sides of the low-level snowstorm center. This is because the mid-level dry-cold air intrusion forces the low-level moisture to rise. As moisture on the west side of the snowstorm center ascends to a certain height along the frontal surface, it encounters the dry layer in the middle troposphere, which impedes the vertical transport and diffusion of moisture and energy, leading to the accumulation of a large amount of unstable energy with high temperature and high humidity. Simultaneously, the presence of dry layer enhances the convective instability, which facilitates the occurrence, development and enhancement of heavy precipitation events. From the perspective of potential vorticity (PV), the high-level PV core extends downward into the middle levels, with PV values reaching 2\u0026ndash;4 PVU at 300\u0026ndash;400 hPa on the west side of the snowfall area. The PV core rapidly develops downward, extending to the east of the surface snowfall area and inducing the initiation and development of surface systems. In summary, the dry intrusion process is essential for the persistence of cold vortex and the enhancement of snowfall.\u003c/p\u003e \u003cp\u003eThe northwestern-path type also has dry intrusion and downward PV transportation, but the intensity is weaker than that of the southern-path type. The minimum relative humidity of the dry layer is 45%. While the dry layer inhibits the vertical transportation of moisture and energy, it does not prevent the horizontal diffusion. Consequently, due to the sustained intense moisture flux transport, the northwestern-path type causes a broader area of heavy snowfall in Northeast China. In contrast, the dry intrusion is less pronounced for the northern-path type, and there is no notable PV core at upper levels. This is because the deep trough or low vortex which splits from the cold vortex has not yet fully entered Northeast China. Nevertheless, the warm-moist southwesterly condensates in front of the low-level cyclonic shear. As a result, the PV core beneath the heating zone is substantially enhanced, which intensifies the surface cyclone in front of the snowstorm center, thereby promoting the development of CVS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe cold vortex is a large-scale system. To explore its evolution, we analyze the cross-sectional characteristics of vorticity by extending the latitudinal range to 10 longitudes around the snowstorm center, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea. As for the northwestern-path type, the vorticity and positive vorticity advection in front of the vortex are notably weaker than other types, indicating that the cold vortex is still in its developing stage. The vorticity of the southern-path type is the strongest among the three types, with a well-developed deep vertical structure throughout the troposphere at the snowstorm center. Additionally, the positive vorticity advection in front of the cold vortex increases with height, indicating that this vortex is in the mature stage. Although the vorticity of the northern-path type is larger than that of the northwestern-path type, there is no obvious positive vorticity above the snowstorm center, which is a certain distance away from the snowstorm center. The positive vorticity advection in front of the vortex, in conjunction with the warm advection in the lower layer, further enhances the ascending motion. The intensified ascending motion accelerates the convergence at low levels, strengthening the low-level cyclonic circulation. As a result of the combined effect of strong warm advection and positive vorticity advection, the low vortex that splits from the main body of the cold vortex gradually evolves into a deep and nearly stable system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study objectively identifies the cold vortex systems in Northeast China and defines the CVS events in this region. Based on the intensity of cold vortexes, the K-means clustering method is used to classify the CVS events into three types. Then, the atmospheric circulation system and the vertical configurations of CVS events in winter in Northeast China are discussed. The main conclusions are as follows.\u003c/p\u003e \u003cp\u003eCVS events account for over 40% of all snowstorm events in Northeast China. According to the intensity of cold vortexes, the K-means clustering method is used to classify the CVS events into three types: northwestern-path type, southern-path type, and northern-path type.\u003c/p\u003e \u003cp\u003eThe main body of cold vortex of the northwestern-path type is located near the Lake Baikal, with strong baroclinicity. An abnormal cyclone is in front of the cold vortex at 850 hPa. The moisture flux path is mainly westerly and southwesterly routes, and the moisture convergence area is located over the southern part of Northeast China.\u003c/p\u003e \u003cp\u003eThe main body of cold vortex of the southern-path type is the southernmost, which locates over the region of North China-Northeast China. At 500 hPa, there is an \u0026ldquo;east high, west low\u0026rdquo; blocking circulation pattern. The center of 850 hpa abnormal cyclone coincides with that at 500 hPa. A cyclone initiates near the surface, and in the downstream areas there is anticyclone high pressure. The moisture path is southward\u003c/p\u003e \u003cp\u003eThe main body of cold vortex of the northern-path type old vortex is located in the northwest of the Lake Baikal, with a more northern position than other types. At 500 hPa, the cold vortex and the Ural Mountains high pressure constitute a \u0026ldquo;west high, east low\u0026rdquo; circulation pattern. At 850 hPa, there are two abnormal cyclone centers, one near the main body of the cold vortex and the other in Northeast China. The main body of the cold vortex in the north continuously transports cold air southward, which deepens the southward-moving short-wave trough into a low vortex or deep trough. A cyclone initiates near the surface, and the moisture path is southwestward. Compared with other types, the moisture convergence, which is located in southern part of Northeast China, is the most remarkable among the three types.\u003c/p\u003e \u003cp\u003eSince the process of CVS of the southern-path type is relatively complex, we have made a schematic diagram, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. It can be seen that the cold vortex of the southern-path type is a deep system, with a significant temperature difference between the cold and warm air masses on both sides of the snowfall area. The warm-moist air masses climb up along the cold wedge and produce heavy snowfall, with the intense snowfall located at the rear of the frontal zone. The penetration of high-level cold-dry air into the middle atmosphere inhibits the vertical movement of moisture, allowing the accumulation of unstable energy and increasing convective instability. The high-level PV core extends downward to the middle level, in conjunction with the increasing of positive vorticity advection in front of the cold vortex with height, inducing the development of surface systems, which supports the persistence of the cold vortex and the enhancing of snowfall.\u003c/p\u003e \u003cp\u003eFor the northwestern-path type, the coupling characteristics of high levels and low levels are between those of the northern-path type and southern-path type. Although the vorticity of the northern-path type is larger than the northwestern-path type, there is no obvious positive vorticity above the snowstorm center. The snowstorm of the northern-path type is mainly caused by strong upward motion lifting the warm, moist air, and the snowfall area is in front of the frontal zone. In the front part of the low-level cyclonic shear zone, the warm-moist southwesterly air flow undergoes a significant condensation process, causing a notable enhancement of the PV core beneath the heating zone, promoting the deepening of the surface cyclone in front of the snowstorm center.\u003c/p\u003e \u003cp\u003eThis study establishes a database of CVS events and explores the influencing mechanisms for different types of CVS events. In the future, we will analyze the possible precursor signals triggering the CVSs, such as the SST anomalies preceding the occurrence of extreme snowstorms, the formation of the cold vortex associated with the splitting of polar vortex, and the relationships of CVSs with the elements like polar sea ice, sea surface temperature, and so on.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors disclosed no relevant relationship.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor 1 - Yue Wang:Methodology, Software, Investigation, Writing - Original Draft;Author 2(Correspondence) - Chenghan Liu: Data Curation, Writing - Original Draft;Formal Analysis.Author 3- Yihe Fang: Visualization, Investigation;Author 4-Haoran Jiao:Resources, Supervision;Author 5-Jing Liu:Software, ValidationAuthor 6-Feng Zhou: EditingAll authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e \u003cp\u003eThis work was jointly supported by the Research Project of the Institute of Shenyang Atmospheric Environment, CMA (Grant No. 2022SYIAEKFMS10); the National Key Research and Development Project (Grant No. 2018YFC1505601); the National Natural Science Foundation of China (Grant No. 42005037); and Liaoning Provincial Meteorological Bureau Annual Foundation Project (Grant No. 202302).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgel L, Barlow M, Qian JH et al (2015) Climatology of daily precipitation and extreme precipitation events in the northeast United States. 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Meteorological Sci Technol 46(5):971\u0026ndash;978\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":"Northeast China, Northeast cold vortex, Cold vortex snowstorm, K-means clustering, Circulation Features, Thermodynamic mechanisms","lastPublishedDoi":"10.21203/rs.3.rs-4907967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4907967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBased on the fifth-generation atmospheric reanalysis of the global climate released by the European Centre for Medium-Range Weather Forecasts (ECMWF), this study firstly objectively identifies the Northeast cold vortex (NECV) processes during the winters from 1951 to 2021. Subsequently, using the observational data from 245 meteorological stations in Northeast China, cold vortex snowstorm (CVS) events in Northeast China are defined and further classified into three types based on the NECV intensity, which are named as northwestern-path, southern-path and northern-path types according to the movement paths of the associated NECVs. The results show that for CVSs of the southern-path type, the NECVs feature the southernmost position, with the whole Northeast China dominated by abnormal cyclone at 850 hPa. For CVSs of the northern-path type, there are two abnormal cyclones in a stepped distribution. However, at the sea level, the surface systems of the southern-path and northern-path types develop into cyclones, leading to distinct moisture transport paths among the three types. The high moisture flux regions correspond well with the snowfall area across all three types, as do the high moisture divergence regions with the extreme snowfall regions. The upper-level jet core is located near 30\u0026deg;N. As the Northeast China region is situated north of the upper-level jet exit, the upward motions in the southern-path and northern-path types are stronger than that in the northwestern-path type. For CVSs of the northern type, significant positive vorticity is located away from the snowstorm center. Warm advection dominates the region from the lower to middle troposphere, and strong upward motion lifts warm and humid air, thereby resulting in the snowstorm. CVSs of the northwestern-path type intermediate in upper-lower level configurations between the other two types. For CVSs of the southern-path type, there is a deep vertical system. Cold advection prevails the mid-to-low levels over the snowfall area, which provides a cold cushion near the ground that forces warm and humid air to ascend, thereby producing heavy snow. Furthermore, the downward extension of the high-level potential vorticity to the mid-to-low levels, along with the intrusion of dry-cold air from the upper levels into the mid-to-low levels facilitate the persistence of the cold vortex and the intensification of snowfall.\u003c/p\u003e","manuscriptTitle":"Classification of Cold Vortex Snowstorms in Northeast China Based on K- means Clustering Algorithm and their Features","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 08:10:13","doi":"10.21203/rs.3.rs-4907967/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":"8a2b2181-49ad-43d3-84b5-c161c7eb6c97","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-25T19:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-12 08:10:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4907967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4907967","identity":"rs-4907967","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}