1. Introduction
Warm-sector heavy rainfall (WR) characteristically emerges within the warm sector located approximately 200–300 km south of a cold or quasi-stationary front, or manifests within the confluence zone of southwesterly and southeasterly winds, or in the southwesterly airflow that is free from wind shear, remarkably uninfluenced by tropical systems, cold fronts, or cold air (Huang 1986). WR events are characterized by intense and localized rainfall and often occur rapidly, resulting in extensive economic damage and high casualty numbers (Wu and Luo 2016; Luo et al. 2017; Sun et al. 2019).
Previous research has indicated that WR is often associated with a variety of synoptic systems and environmental conditions. Expanding on this, Du and Chen (2019a) shed light on a coupling between two low-level jets (LLJs). The synoptic-system LLJ results in divergence around 700 hPa close to its entry zone. Meanwhile, when the boundary layer jet (BLJ) interacts with topography, it intensifies convergence approximately at 950 hPa close to its exit region along the coast. This leads to significant mesoscale uplift near coastal regions. Moreover, the LLJ plays a crucial role in transporting warm and moist air (Dong et al. 2021; Liu et al. 2020; Du and Chen 2019a,b; Zhang et al. 2018). Zhang et al. (2022) discovered that the warm and wet tongue, represented by pseudoequivalent potential temperature θse, situated over the northern section of the South China Sea, closely correlates with WR occurrences. Thermodynamic analyses suggest that potential early indicators for WR events in the coastal regions of Guangdong can be discerned through factors such as the LLJ, the presence of the warm and wet tongue, and convergence in the lower troposphere. Further investigation by Du et al. (2020) illuminates the roles of terrain, coastline, and cold pools in the generation of convection. Coastal convergence, orographic lifting, and mesoscale upward movement triggered by the end of a marine BLJ (MBLJ) all contributed to convection initiation (CI) occurring at the vertex of the coastal concave mountain geometry. In numerical simulations, coastal CI either disappeared or weakened when South China’s coastline or topography was removed, with the MBLJ shifted farther north. This implies the significant role of coastlines and terrain in the process of CI.
While WR research in China has largely been concentrated on South China (Zhang et al. 2022; Du et al. 2022; Dong et al. 2021; Liu et al. 2020; Du and Chen 2019a,b), WR events in East China have received considerably less attention (Zhang et al. 2022). Based on synoptic weather evaluations, three distinct categories have been identified for WR events in the middle and lower reaches of the Yangtze River: cold fronts, warm-type shear lines, and the periphery of the subtropical high. Significantly, Chen et al. (2016) noted that the WR associated with the warm-type shear line (WSWR) account for 68% of the overall occurrences, representing a significant segment of WR in the Yangtze–Huaihe River (YHR) basin in China. Given these statistics, a comprehensive analysis of WR in the YHR basin would greatly benefit from a more focused study of WSWR characteristics to uncover and understand the underlying mechanisms.
Song et al. (2022) established an objective identification method for WSWR along the YHR basin. In this method, a mesoscale rainstorm is identified as a sustained rainfall zone where the mean rainfall surpasses 5 mm h−1, reaching highs above 20 mm h−1, and with its long axis extending over 100 km. Rainfall occurring within a 500-km radius of the typhoon’s center, according to the JTWC typhoon path, is excluded. If a front at 850 hPa is present in the Yangtze–Huaihe basin, the center of the WR rainstorm should be at least 200 km away, and no northerlies (υ < 0) should be found within a northern semicircle with a 100-km radius. This condition also applies in the absence of a front. Last, if the center of the WR rainstorm is located between 100 and 300 km south of the warm shear line, it is defined as the center of the WSWR rainstorm. Based on the above objective criteria, this study builds upon the statistical analysis of WSWR events along the Yangtze–Huaihe coastal region (YHCR) from 2010 to 2017 (Table 1). This paper is structured as follows: the data and methods are described in section 2. The objective classification of WSWR over YHCR is described in section 3. The multiscale dynamic and thermodynamic characteristics of WSWR over YHCR are investigated in section 4. A summary and discussion are then provided in section 5.
Classification results of the geometric center of the rain cluster at the peak moment (UTC) of WSWR events along the YHCR.
2. Data and method
a. Data
The 0.1° × 0.1° resolution hourly precipitation data from gauge–satellite sources in China are obtained from the China Meteorological Administration’s National Meteorology Information Center. By utilizing a two-step process that includes the application of the probability density function and optimal interpolation, these data are combined using over 30 000 automatic weather stations throughout China along with the Climate Precipitation Center morphing technique (CMORPH) precipitation product (Shen et al. 2014). The 0.25° × 0.25° resolution hourly atmospheric circulation fields are taken from the European Centre for Medium-Range Weather Forecasts’ ERA5 reanalysis datasets (Hersbach et al. 2020).
b. Method
To track potential moisture transport routes for WSWR events over YHCR, the online Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-WEB V4) model (http://www.arl.noaa.gov/hysplit) is utilized, originating from NOAA’s Air Resources Laboratory (ARL) (Draxler et al. 2009; Draxler and Hess 1998), the model operates under the assumption that air packets are in line with the wind vector V. The Global Data Assimilation System (GDAS) data have been selected as the input to serve as the input for the HYSPLIT-WEB V4 model. The chosen data span elements like geopotential levels, specific humidity, and a 3D wind grid, provided at intervals of 3 h and a spatial scale of 1° × 1°. More detailed information can be accessed in the HYSPLIT-WEB V4 User’s Guide (Draxler et al. 2009).
To investigate the multiscale dynamic and thermodynamic characteristics of WSWR, the Barnes bandpass filter is used in this study. It is developed based on the Barnes objective analysis method (Barnes 1973). This approach is designed to produce gridded data F(x, y) from observations Fk, where k represents the index of observational stations, running from 1, 2, up to N. Barnes filtering, applied to uniformly gridded data, is utilized in this study for the filtering of ERA5 reanalysis data with a grid resolution of 0.25° × 0.25°. This process serves to isolate fluctuations across three scales: large scale (above 2000 km), meso-α scale (200–2000 km), and meso-β scale (below 200 km). The specific filtering scheme unfolds as follows: with parameters c1 = 150 000 km2, c2 = 150 000 km2, and G = 0.3, the large-scale background field above 2000 km and fluctuations below 2000 km are computed. Employing c1 = 3000 km2, c2 = 3000 km2, and G = 0.3, the fluctuations above 200 km and the meso-β-scale fluctuations below 200 km are computed. The meso-α-scale fluctuations in the 200–2000-km range are then obtained by subtracting the fluctuations above 2000 km from those above 200 km.
A comprehensive analysis is conducted to explore the physical variables associated with WSWR, with both composite analysis and dynamic composite analysis being deployed. This is further enhanced by statistical validation using Student’s two-tailed t test.
3. Objective classifications
Previous research reveals that the western Pacific subtropical high (WPSH) (Zhan et al. 2008; Chen and Zhai 2014; Zhao et al. 2022) and water vapor (Zhou and Yu 2005; Shi et al. 2020; Zhang et al. 2021) both have a close relationship with precipitation in eastern China. Thus, the WPSH and water vapor transport are fundamentally taken into account for categorizing the WSWR events. For each WSWR event occurring over YHCR, the center of the WSWR rainstorm that records the highest precipitation is chosen as the initial position (Song et al. 2022), and the moment of peak precipitation is selected as the starting time. Thirty-two air particle trajectories arriving at YHCR at altitudes of 1500 and 700 m are traced, with updates every 3 h, for a total backward duration of 3 days. Alongside this, the locations of the westward extension ridge point of the WPSH are considered at the peak moments, and the K-means unsupervised learning method for clustering is then applied. As the number of clusters increases, SSE quickly drops until it reaches an optimal level at K = 3, beyond which the decrease in SSE becomes significantly slower. Additionally, the silhouette score peaks at K = 3 (as shown in Fig. 1). Considering both empirical tests, K = 3 is the optimal count for clusters.
Variation of SSE (blue) and silhouette score (red) with the number of clusters.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
Upon analyzing the environmental characteristics of the three clusters (Fig. 2), significant similarities between clusters 1 and 3 are identified. Specifically, the WPSH (indicated by the red line) retreats into the ocean and the BLJ at 950 hPa and LLJ at 850 hPa run along the coast to the YHCR. This is different from cluster 2, where the WPSH is located over the mainland, and there is significant LLJ at 850 hPa on the southwest side of YHCR. Consequently, cluster 1 and cluster 3 are grouped into a single category, which are termed as the south-biased type (S-type). Cluster 2, on the other hand, is distinct and labeled as the southwest type (SW-type). The results, as displayed in Table 1, show 15 cases classified as the SW-type and 17 cases as the S-type.
The composite horizontal wind at (a)–(c) 850 and (d)–(f) 950 hPa (arrows; m s−1; arrow fields are statistically significant at a 95% confidence level) and its velocity (shading; m s−1; dotted points are statistically significant at a 95% confidence level) for cluster 1, cluster 2, and cluster 3, respectively. The red solid line refers to 5880-gpm contours of geopotential height at 500 hPa at WSWR peak moments. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
The moisture conditions of two types of WSWR are subsequently examined by composite analysis. The vertical distributions of water vapor suggest that for SW-type WSWR (Fig. 3a), the majority of the moisture is mainly accumulated near the lower troposphere with the maximum value at ∼850 hPa. The equivalent potential temperature decreases with height, satisfying the condition for convective instability. This implies that even minor disturbances can easily trigger convection. The maximum value of moisture flux in the S-type WSWR (Fig. 3b) is primarily concentrated in the boundary layer, and the vertical structure of θse indicates that warm moist air is restricted inside the boundary layer. While the peak water vapor flux distribution varies between the two WSWR types, the water vapor is mainly confined in the lower troposphere. Therefore, a vertical integration of the water vapor flux at 1000–700 hPa is performed to better understand the moist pathways. For SW-type WSWR (Fig. 4a), moisture pathways from the northern South China Sea and the western Pacific Sea converge near Guangdong province, then shift southwestward, reaching the YHCR. For S-type WSWR (Fig. 4b), one pathway originates in the Bay of Bengal, traverses the South China Sea, and advances into mainland China with southernly airflow, reaching the YHCR along the southeastern coastal areas. Simultaneously, another pathway follows a southern airflow along the western Pacific through the East China Sea, also reaching the YHCR.
Zonal–height section averaged along (30°–34°N) from the surface to 600 hPa for (a) SW-type and (b) S-type at WSWR occurrence moments. Composite of water vapor flux (shading; 10−4 kg Pa−1 m−1 s−1; dotted points are statistically significant at a 95% confidence level) and pseudoequivalent potential temperature (θse; K; black contours). The yellow line indicates the longitude of YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
Composite of integrals of water vapor flux from the surface to 700 hPa at the moments of (a) SW-type and (b) S-type WSWR occurrence (shading; kg m−1 s−1; dotted points and arrow fields are statistically significant at a 95% confidence level). The red solid line is the 5880-gpm contour of geopotential height at 500 hPa. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
These results confirm the objectivity of our classification. Two types of WSWR events are distinguished by analyzing the moisture transport trajectories and the western extension ridge point of the WPSH. Fifteen cases of the SW-type WSWR are identified, where moisture pathways predominantly lie southwest near YHCR, and 17 cases of S-type WSWR are found, where pathways mainly originate from the south near YHCR. In the SW-type cases, it is found that the water vapor primarily concentrates in the lower troposphere, while in the S-type events, it predominantly resides in the boundary layer. Accordingly, a comprehensive analysis of the multiscale dynamic and thermodynamic characteristics for both SW-type and S-type WSWR will be conducted in the subsequent sections.
4. Characteristics of the WSWR over YHCR
a. The dynamic characteristics of SW-type WSWR
To uncover the underlying mechanism linked to SW-type WSWR over YHCR, the synoptic features within the troposphere are investigated (Figs. 5a–c). The BLJ is present at 950 hPa, following a northeast–southwest trajectory along the coastline. The wind direction undergoes a change from southeast to southwest at YHCR as it moves from the boundary layer to the lower troposphere. This shift is indicative of warm advection, consistent with the principles of thermal wind. In the lower troposphere at 850 hPa, an LLJ is observed, where the maximum core value surpasses 12 m s−1. The YHCR is positioned in the exit region of the LLJ and is characterized by a robust southwest airflow. Conversely, at 600 hPa, the YHCR is found at the entrance of the LLJ. The low-level convergence and midlevel divergence, referred to as suction effects, are pivotal in triggering and sustaining convection for SW-type WSWR.
Composite of horizontal winds (arrows; m s−1; arrow fields are statistically significant at a 95% confidence level) and its velocity (shading; m s−1; dotted points are statistically significant at a 95% confidence level) of SW-types at (a) 600 , (b) 850, and (c) 950 hPa and S-types at (d) 700, (e) 850, and (f) 975 hPa of WSWR events over YHCR at the occurrence moments. The red lines define the cross section in Fig. 11. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
Dynamic composite of horizontal wind fields (arrows; m s−1) at (left) large, (center) meso-α, and (right) meso-β scales, centered at 32°N, 121°E (red spot), at the moments of SW-type WSWR occurrence. These are depicted at the (a)–(c) 600-, (d)–(f) 850-, and (g)–(i) 950-hPa levels. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
To further illustrate the vertical profile of divergence fields, an average calculation along the longitude and latitude for the range 120°–122°E and 30°–34°N are conducted, respectively. As clearly demonstrated in Fig. 7, strong convergence is shown in the lower troposphere for both zonal–height section (Fig. 7a) and meridional–height section (Fig. 7b). Particularly for Fig. 7b, an entire atmospheric layer between 900 and 700 hPa exhibits convergence, while at 600 hPa, there is a prominent center of divergence. This observation reaffirms the configuration of lower tropospheric convergence and midtropospheric divergence depicted in Figs. 5a and 5b.
Composite of the (a) meridional–height section and (b) zonal–height section of divergence averaged along 120°–122°E and 30°–34°N (shading; 10−6 s−1; dotted points are statistically significant at a 95% confidence level) at the moments of SW-type WSWR occurrence, respectively. The wind (black vectors) along the y direction in (a) and the x direction in (b) is displayed, with the vertical velocity exaggerated by 100 times. Arrow fields are statistically significant at a 95% confidence level.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
The dynamic characteristics analyzed provide insights into the triggering mechanism of the SW-type WSWR. Notably, YHCR is positioned on the left side of the BLJ, where a cyclonic vortex is observed at the meso-α scale within the boundary layer. At 850 hPa, YHCR is located in the exit region of LLJ, whereas at 600 hPa, it is located near the LLJ’s entrance. At the meso-β scale, there is wind field convergence at 850 hPa and a divergence at 600 hPa. This low-level convergence and midlevel divergence fosters the initiation and sustenance of convection.
b. The thermodynamic characteristics of SW-type WSWR
As the distribution of humidity fields is mostly concentrated in the lower troposphere (Fig. 3a), the scale distribution of orizontal moisture advection at 850 hPa (Figs. 8a,b) is computed. Figure 8a represents moisture advection calculated from large-scale wind fields and large-scale humidity gradients. It displays a strong moisture advection field centered in northern Jiangsu with the background of strong southwest winds. Figure 8b shows moisture advection calculated from meso-α-scale wind fields and large-scale humidity gradients. Specifically, the meso-α-scale wind field features an almost east–west-oriented low pressure vortex. The southwestern winds carry moist air, while the northeastern winds transport dry air, with a clear moist center and dry center on the vortex’s south and north sides, respectively. To investigate the impact of temperature advection at different scales, a scale separation of the temperature field and the wind field is carried out. The temperature advection computed from the large-scale horizontal wind field and the large-scale temperature field reveals strong warm advection across the entire area of YHCR and its surrounding regions (Fig. 9a). The temperature advection involving the meso-α horizontal wind field and the large-scale temperature field also plays a significant role. The southwest wind delivers warm advection, while the northeast wind carries cold advection (Fig. 9b). These results demonstrate that the large-scale field provides a robust backdrop of warm, moist airflow, and it alone is insufficient to trigger convection. The key component is the cyclonic vortex at the meso-α scale, which plays a crucial role in inducing airflow divergence. The southwestern winds on the vortex’s southern side, which align with the LLJ depicted in Fig. 5b, facilitate the transport of warm, moist air. When coupled with large-scale humidity and temperature gradients, this leads to a pronounced warm and moist advection.
Dynamic composite of horizontal wind fields (arrows; m s−1) and humidity advection (shading; 10−8 kg kg−1 s−1) for (a),(b) SW-type at 850 hPa and (c),(d) S-type at 975 hPa centered at 32°N, 121°E at the moment of WSWR occurrence. (left) Large-scale horizontal wind combined with large-scale specific humidity and (right) meso-α horizontal wind combined with large-scale specific humidity. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
As in Fig. 8, but for temperature advection (shading; 10−5 K s−1).
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
c. The dynamic characteristics of S-type WSWR
The same dynamic analysis is performed for the S-type WSWR, yielding results that are different from those observed for the SW-type WSWR. A BLJ is observed at 975 hPa (Fig. 5f), with the maximum velocity of the jet core exceeding 9 m s−1 and the prevalent wind direction is southeast. However, at the lower tropospheric level of 850 hPa (Fig. 5e), the wind direction turns to the south, and at 700 hPa, it shifts to the southwest (Fig. 5d). From the lower to higher levels, the wind field transitions from southeast to southwest, forming a clockwise circulation. Scale analysis confirms this observation, with both large-scale wind fields (Figs. 10a,d,g) and the meso-α wind field (Figs. 10b,e,h) demonstrating a shift in the wind field from southeast to southwest, following a clockwise rotation, which indicates strong warm advection according to principles of thermal wind. In the lower levels of the troposphere of the meso-α wind field (Figs. 10e,h), a cyclonic vortex situated above the YHCR is observed. This cyclonic circulation favors the transfer of warm, moist energy toward the YHCR. Additionally, the cyclonic vortex promotes the convergence of airflows, thereby triggering the onset of convection.
Dynamic composite of horizontal wind fields (arrows; m s−1) at (left) large, (center) meso-α, and (right) meso-β scales, centered at 33°N, 121°E (red spot), at the moment of S-type WSWR occurrence. These are depicted at the (a)–(c) 600-, (d)–(f) 850-, and (g)–(i) 975-hPa levels. The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
To explore the triggering mechanism on the S-type WSWR, a cross section along the red line in Fig. 5f is taken. Figure 11a clearly shows the vertical structure of the BLJ. The BLJ primarily occurs over the ocean below 900 hPa with a maximal core at ∼975 hPa, where the velocity surpasses 9 m s−1, together with the coastline convergence (Fig. 11b), low-level convergence increases, and therefore triggering convection.
Composite vertical cross section of (a) velocity (shading; m s−1; dotted points are statistically significant at a 95% confidence level) and (b) divergence (shading; 10−6 s−1; dotted points are statistically significant at a 95% confidence level). The wind (black vectors) along the line CD in Fig. 5f, with the vertical velocity exaggerated by 100 times. The yellow line represents the coastline of YHCR. Arrow fields are statistically significant at a 95% confidence level.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
d. The thermodynamic characteristics of S-type WSWR
The thermal fields are depicted using the pseudoequivalent potential temperature θse, effectively capturing the two thermodynamic factors: water vapor and temperature. The composite of θse shows the presence of a warm, moist tongue, transported along the BLJ as depicted in Fig. 12a. To clearly depict the anomaly in θse, the data are adjusted by subtracting the climatological mean from 1989 to 2017. Figure 12b exhibits a positive anomaly center exceeding 9 K, with the area of highest value located near Hangzhou Bay and the southern part of Jiangsu Province. These results show that for the S-type of WSWR occurs under conditions of high moisture and warmth in the boundary layer. Additionally, the BLJ aids in channeling this moist and warm air to YHCR.
(a) Composite of θse (shading; K) at 950 hPa. (b) Composite of θse (shading; K) anomalies and horizontal wind field (arrows; m s−1) anomalies at 950 hPa for S-type WSWR. Arrow fields (dotted regions) are statistically significant at a 95% confidence level for horizontal wind (θse). The purple rectangle refers to the YHCR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
To understand the roles of different scales of humidity and temperature advection, a scale analysis is conducted. Given that the humidity fields’ distribution is predominantly concentrated in the boundary layer as shown in Fig. 3b, the scale distribution of horizontal moisture advection (Figs. 8c,d) and horizontal temperature advection (Figs. 9c,d) at 975 hPa is computed. Strong moisture (warm) advection, centered near the YHCR coastline, is facilitated by the combined action of the large-scale horizontal winds, marked by robust southeast winds, and the large-scale specific humidity (the large-scale temperature) as depicted in Fig. 8c (Fig. 9c). The meso-α wind field and the large-scale specific humidity field (large-scale temperature field) play important roles in Fig. 8d (Fig. 9d). Specifically, the meso-α-scale wind field is distinguished by a nearly north–south-oriented cyclonic vortex. The existence of the vortex is favorable to the convergence of circulation. The southerly winds on the vortex’s eastern side, which align with the BLJ depicted in Fig. 5f, enable the transport of warm, humid air. Furthermore, a prominent center of warm and moist advection is present near the YHCR coastline. Scale analysis results demonstrate that while large-scale wind fields provide a backdrop of strong southeast winds and a pronounced center of warm and moist advection, the interaction of meso-α horizontal winds with large-scale specific humidity (and temperature) plays a significant role.
e. Case study of WSWR
To further verify the accuracy of the results, typical cases of the SW-type at 1400 UTC 21 June 2016 and S-type at 0400 UTC 11 July 2016 are selected. For SW-type (Fig. 13a), the WPSH indicated by the red contour extends into the mainland of China, and the geometric center of the WSWR cluster is located at the exit area of LLJ at 850 hPa and the entrance area of the 600-hPa jet, where the wind speed of the jet core is above 20 m s−1. The suction effect caused by low-level convergence and midlevel divergence initiates the convection. In the case of the S-type (Fig. 13b), the WPSH retreats eastward to the East China Sea, with a BLJ present. The BLJ carries moisture along the coastlines of Fujian and Zhejiang Provinces toward the YHCR. Precipitation is observed at the BLJ’s exit zone, characterized by core wind speeds exceeding 10 m s−1. Here, the convergence at the exit of BLJ and southeastern coastal winds at the coastline initiates convection.
(a) Case of SW-type velocity (shading; m s−1) at 850 hPa; the orange solid line is the 10 m s−1 contour at 950 hPa, and the yellow solid line represents the wind speed at 600 hPa. (b) Case of the S-type velocity (shading; m s−1) at 975 hPa; the orange solid line is the 10 m s−1 contour at 975 hPa. The red solid line is the 5880-gpm contour of geopotential height at 500 hPa. The purple rectangle refers to the YHCR, and the red dot represents the position at the peak of precipitation.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
5. Conclusions and discussion
Using high-resolution hourly precipitation data and ERA5 reanalysis, the K-means machine learning unsupervised method is employed to classify 32 cases WSWR at YHCR into two types: the SW-type (15 cases) and the S-type (17 cases). By utilizing composite analysis, dynamic composite analysis, and the Barnes filter, the dynamic and thermodynamic characteristics of these two types of WSWR events along the Yangtze–Huaihe coastal regions (YHCR) are examined. The key results are as follows.
For the SW-type (Fig. 14a), the YHCR is positioned at the exit zone of the 850 hPa LLJ and the entrance region of the 600 hPa jet stream, with low-level convergence and midlevel divergence, providing optimal dynamic conditions for triggering convection. The YHCR is located on the left side of the BLJ, exhibiting cyclonic convergence. The water vapor is most concentrated around 850 hPa and the θse decreases with altitude below the midtroposphere, meeting the conditions for convective instability. As for the S-type (Fig. 14b), the influencing systems are primarily in the boundary layer. The BLJ and the warm and moist tongue in the boundary layer, in conjunction with the terrain, generate potential instability and wind speed convergence uplift. The water vapor is mainly restricted within boundary layer and the θse decreases with altitude below the midtroposphere, satisfying the conditions for convective instability. The typical case studies in Fig. 13 show similar synoptic environment background.
Schematic of the mechanisms responsible for WSHR events over YHCR. (a) SW-type WSWR and (b) S-type WSWR.
Citation: Journal of Hydrometeorology 25, 3; 10.1175/JHM-D-23-0143.1
The Barnes filtering results highlight that for SW-type WSWR, the dynamic characteristics show that there is a persistent southwest wind extending from the boundary layer to midtroposphere at the large-scale horizontal wind field, contrasted by the cyclonic vortex at the meso-α scale. Distinct wind direction convergence at 850 hPa and divergence at 600 hPa characterize the meso-β scale. In terms of thermodynamic characteristics, although the large-scale field establishes a robust warm and moist southwest airflow, this is not sufficient to trigger convection independently. The cyclonic vortex at the meso-α scale effectively promotes airflow divergence. Working in concert with the southwestern winds on the vortex’s southern side, this promotes efficient transport of warm, humid air. For the S-type WSWR, the large-scale and meso-α-scale horizontal wind field rotates clockwise from the boundary layer to midtroposphere, showing a strong warm advection. A low pressure vortex exists on the meso-α scale at the lower troposphere. The southerly winds on the eastern side of the cyclonic vortex aid in transporting warm, humid air. Along with the large-scale specific field and temperature field, this furthers pronounced warm and moist advection near the YHCR coastline. In conclusion, scale analysis reveals that the mesoscales of dynamic field are critical in the formation of both types of WSWR. Meanwhile, the large-scale and meso-α-scale wind field promote the transportation of humid airflow. When combined with large-scale specific humidity and temperature field, it results in pronounced warm and wet advection.
The aforementioned research aids in deepening our understanding of the WSWR along the YHCR, considering that there has been relatively limited research in this region. However, our study is merely statistical and focuses only on the warm shear type of heavy rainfall. More types of warm-sector heavy rainfall events should be investigated. Additionally, numerical experiments should help corroborate these statistical results by allowing us to simulate and manipulate the various factors involved in our study, furthering our understanding of the mechanisms.
Acknowledgments.
The study was jointly supported by the National Key R&D Program of China (Grant 2017YFC1502002). The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data availability statement.
The CMORPH precipitation data that support the findings of this study are available at https://doi.org/10.1002/2013JD020686. The ERA5 hourly reanalysis data on pressure level is available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels.
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