Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System

Advantage of 30‐s‐Updating Numerical Weather Prediction With a Phased‐Array Weather Radar Over Operational Nowcast for a Convective Precipitation System
复制标题

DOI:
10.1029/2021gl096927
复制
发表时间:
2022
影响因子:
5.2
通讯作者:
T. Honda;A. Amemiya;S. Otsuka;J. Taylor;Y. Maejima;S. Nishizawa;T. Yamaura;K. Sueki;H. Tomita;T. Miyoshi
T. Honda;A. Amemiya;S. Otsuka;J. Taylor;Y. Maejima;S. Nishizawa;T. Yamaura;K. Sueki;H. Tomita;T. Miyoshi
中科院分区:
地球科学1区
文献类型:
--
作者:
T. Honda;A. Amemiya;S. Otsuka;J. Taylor;Y. Maejima;S. Nishizawa;T. Yamaura;K. Sueki;H. Tomita;T. Miyoshi

文献摘要

相似文献

夏季对流性降水系统常引起突发性强降水,对人类活动产生较大影响,但其快速演变限制了预报能力。具有高时空分辨率的相控阵天气雷达(PAWR)可用于观测此类降水系统。最近开发的数值天气预报(NWP)系统将PAWR观测与500 m网格NWP模式同化。它每30秒启动30分钟的扩展预报,比业务NWP和临近预报系统更频繁。本研究调查了30 s更新的NWP系统在一个单一但具有代表性的对流降水事件中的好处,其中对流云在10分钟内发展,并且其演变不能通过业务降水临近预报很好地预测。快速更新的数值预报系统成功地预报了对流云的演变。每30秒同化一次PAWR观测,可以不断修改水汽场和动力场,并不断提高预报精度。
Convective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.