Nocturnal-to-morning rains during the warm season in South China: characteristics and predictability

ABSTRACT

The characteristics of nocturnal-to-morning rainfall (NMR) during the warm season in South China are examined using hourly surface observations from 2015–2019. Results show that strong NMR is mainly located in coastal regions and mountainous areas. NMR mainly occurs during 0200–0800 LST. The distribution of NMR can be divided into two types. The first pattern is a coastal type where NMR is mainly located at windward sites, such as southern coastal mountain areas in Guangdong (GD) and in Guangxi (GX). The second type is an inland type where NMR is mainly located at windward sites, such as the northern mountainous areas in GX and the Pearl River Delta regions in GD. The strong convergence between the northerly wind over the mountainous regions and southerly wind, as well as the strong downhill winds strengthened by the narrow pipe effect over the valleys in mountainous areas, together contribute to the high frequency of inland NMR. The strong southeasterly onshore wind and the cyclonic circulations strengthened by the northeasterly wind over the northern mountainous areas contribute to the high frequency of coastal NMR. Though the GRAPES (Global/Regional Assimilation and Prediction System) model can capture the intensity and distribution of large-scale NMR, it exhibits low predictability of small-scale NMR, especially that in the warm sector.

GRAPHICAL ABSTRACT

摘要

本文研究了华南暖季夜间到早晨降水 (NMR) 主要特征和可预报性。指出NMR主要发生在凌晨02–08时, 可分为两类:第一类是海岸型, 主要发生于山脉迎风坡, 如广东南部沿海山区和广西沿海山区。第二类是内陆型, 如广西北部山区和珠江三角洲中北部地区。山区北风与南侧强南风的对流辐合使得内陆型NMR频繁发生, 而东南向海风和北侧山区东北风形成的气旋性环流是造成海岸型NMR频发。尽管GRAPES模式可以预报出大尺度NMR, 但对暖区小尺度NMR预报能力较低。

KEYWORDS:

• South China
• warm season
• nocturnal-to-morning rainfall
• GRAPES

关键词:

• 华南
• 暖季
• 夜间到早晨降水
• GRAPES

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was jointly supported by the National Key Research and Development Program of China [grant number 2018YFC1506901]; the National Natural Science Foundation of China [grant numbers 41505084 and 41875079]; and the Project of Guangzhou Science and Technology [grant number 201804020038].