ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 47 Issue 8 > 2021,47(8):932-952. DOI:10.7519/j.issn.1000-0526.2021.08.003 Prev Next
A Squall Line Case Study of Assimilating the Radar Data, Retrieval of Water Vapor and InCloud Potential Temperature from Reflectivity in a 3DVAR Framework
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Abstract:
This paper investigates the influence of assimilating radar radial wind and reflectivity, and pseudo observation on the precipitation, gale and hail and other disastrous weather. At the same time, this paper also discusses the effect of different cold start time on precipitation forecast for a squall line event that occurred in the south of the Yangtze River on 4 May 2020. The results indicate that after assimilating the pseudo- water vapor and pseudo- in-cloud potential temperature at the cold start time, the water vapor and potential temperature are increased in the observed strong echo area. As the number of cycle assimilation increases, the negative water vapor increment mainly appears in the spurious convection of background, and the convection is inhibited. The increase in potential temperature mainly concentrates in the area where the observed echo is larger than the background simulated echo. Compared with only assimilating radar data, the assimilation of pseudo-water vapor and pseudo-in-cloud potential temperature can obviously improve the 0-3 h radar composite reflectivity and precipitation forecast. The simulated 2-5 km updraft helicity path is more consistent with the location of damage wind and hail disaster. The FSS scores at 1, 5, 10 mm threshold of 1 h accumulated precipitation in 0-6 h is significantly improved. With the increase of cycle assimilation times, the FSS score of pseudo observation experiments compared to radar experiments rises significantly at first, reaching the peak after 6-8 h cycle, and then the FSS score declines. In addition, different cold start time experiments show that after the background field is updated at 12:00 UTC, assimilating pseudo observation data has a positive contribution to improving precipitation prediction.
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