The radar data assimilation is practical for enhancingthe accuracy of numerical weather prediction in precipitation, yet few systems have correct and practical results.This research used a Space and Time Multiscale Analysis System (STMAS) in assimilating radar radical velocity and reflectivity factor, to compare with the previous Local Analysis and Prediction System (LAPS) results in the initial wind field, water vapor condition, weather prediction, precipitation, and radar reflection.The case study in the Mei-Yu event, in Wuhan on 5 July 2016, showed that the STMAS method provides a better initial condition in the wind field, water vapor condition and hence improved precipitation prediction.First, when assimilating the radar radical wind velocity, STMAS added the continuity equation as a strong constraint, which significantly improves the dynamic field in the initial condition.Second of all, STMAS saturated the water vapor when assimilating the radar reflection higher than the threshold, which provided more water vapor to the initial condition.This process can increase the instability, and trigger the convection.The last and the most important part is that, since the STMAS method helped to improve the initial condition, the patterns of the high altitude in the forecast is more realistic, which lower the bias of the prediction of precipitation in location and strength, especially in the prediction of the strong event which is larger than 100 mm.
Juxiang PENG
,
Yuanfu XIE
,
Zhaoping KANG
,
Hongli LI
. Application of an Improved Radar Data Assimilation Scheme in Heavy Rain Forecast in Meiyu Period[J]. Plateau Meteorology, 2020
, 39(5)
: 1007
-1022
.
DOI: 10.7522/j.issn.1000-0534.2019.00091
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