The Short-Duration Heavy Rainfall in Different Quadrants of Northeast China Cold Vortices

Abstract

The Northeast China cold vortex (NCCV) is one of the main synoptic-scale systems causing short-duration heavy rainfall (SDHR) in Northeast China. Environmental conditions (e.g., water vapor, instability, and vertical wind shear) are known to be distinctly different over the four quadrants of NCCVs, rendering prediction of the SDHR related to NCCVs (NCCV_SDHR) more challenging. Based on 5-yr hourly rainfall observations from 3196 automatic weather stations and ERA5 reanalysis data, 10,232 NCCV_SDHR events were identified and divided into four quadrant groups according to their relative position to the center of the NCCV (CVC). The results show that the southeast quadrant features the highest frequency of SDHR, with stronger intensity, longer duration, and wider coverage; and the SDHR in different quadrants presents different formation mechanisms and varied temporal evolution. A new coordinate system is established relative to the CVC that uses the CVC as the origin and the radius of the NCCV (r_CV) as the unit distance. In this new coordinate system, all of the NCCV_SDHR events in the 5-yr study period are synthesized. It is found that the occurrence frequency of NCCV_SDHR initially increases and then decreases with increasing distance from the CVC. The highest frequency occurs mainly between 0.8 and 2.5 times r_CV from the CVC in the southeast quadrant. This can be attributed to the favorable conditions, such as convergence of the low-level shear line and abundant water vapor, which are concentrated in this region. Furthermore, high-frequency NCCV_SDHR larger than 50 mm (NCCV_SDHR50) is observed to be closer to the CVC. When NCCV_SDHR50 occurs, the NCCV is in closer proximity to the subtropical high, resulting in stronger low-level convergence and more abundant water vapor. Additionally, there are lower lifting condensation levels and stronger 0–6- and 0–1-km vertical wind shears in these environments. These findings provide a valuable reference for more accurate prediction of NCCV_SDHR.