期刊
ADVANCES IN ATMOSPHERIC SCIENCES
卷 40, 期 5, 页码 920-936出版社
SCIENCE PRESS
DOI: 10.1007/s00376-022-1380-3
关键词
FY-4A; AGRI; clear-sky radiance; satellite data assimilation; 21; 7 Henan extremely persistent heavy rainfall
Assimilation of AGRI clear-sky radiance, along with conventional observations, improves the forecast accuracy of extreme rainfall events in Henan, especially for short-duration intense precipitation processes.
Assimilation of the Advanced Geostationary Radiance Imager (AGRI) clear-sky radiance in a regional model is performed. The forecasting effectiveness of the assimilation of two water vapor (WV) channels with conventional observations for the 21 center dot 7 Henan extremely heavy rainfall is analyzed and compared with a baseline test that assimilates only conventional observations in this study. The results show that the 24-h cumulative precipitation forecast by the assimilation experiment with the addition of the AGRI exceeds 500 mm, compared to a maximum value of 532.6 mm measured by the national meteorological stations, and that the location of the maximum precipitation is consistent with the observations. The results for the short periods of intense precipitation processes are that the simulation of the location and intensity of the 3-h cumulative precipitation is also relatively accurate. The analysis increment shows that the main difference between the two sets of assimilation experiments is over the ocean due to the additional ocean observations provided by FY-4A, which compensates for the lack of ocean observations. The assimilation of satellite data adjusts the vertical and horizontal wind fields over the ocean by adjusting the atmospheric temperature and humidity, which ultimately results in a narrower and stronger WV transport path to the center of heavy precipitation in Zhengzhou in the lower troposphere. Conversely, the WV convergence and upward motion in the control experiment are more dispersed; therefore, the precipitation centers are also correspondingly more dispersed.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据