The Prestorm Environment and Prediction for Local‐ and Nonlocal‐Scale Precipitation: Insights Gained From High‐Resolution Radiosonde Measurements Across China

Abstract

AbstractDifferentiating local‐scale precipitation (LP) and nonlocal‐scale precipitation (NLP) and understanding their corresponding prestorm environment are of importance for accurate severe weather analysis and forecasting. However, the difference in the transient prestorm environments of LP and NLP is largely undetermined, thereby fundamentally limiting the predictability of convective storms. The present study focuses on the precursor signals of LP and their differences from those of NLP based on explicit observational analyses using high‐resolution radiosonde measurements from the China radiosonde network with a vertical resolution of 5‐m, combined with 1‐min rain gauge data from the China rain gauge network during 2013–2020. LP and NLP can be recognized by most proximity sounding parameters. For example, the upcoming LP is characterized by larger most‐unstable convective available potential energy (MUCAPE), K index, and total precipitable water (TPW), and a lower lifting condensation level (LCL), compared to the NLP scenario. By taking proximity sounding parameters as inputs and precipitation types as learning targets, a nu‐support vector machine algorithm can effectively predict LP or NLP events, achieving an overall precision of 97%. However, the precision significantly drops by approximately 20% after removing the variable of low‐level wind shear, indicating the crucial role of wind observation when predicting the LP event. Furthermore, more intensive LPs are characterized by larger MUCAPE, K index, TPW, and moist static energy, and lower LCL. Among others, MUCAPE is the most important feature, according to the gradient boosting machine algorithm.