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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Cloud-Resolving Model Applied to Nowcasting: An Evaluation of Radar Data Assimilation and Microphysics Parameterization

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Author(s):
Vendrasco, Eder P. [1] ; Machado, Luiz A. T. [2, 1] ; Ribeiro, Bruno Z. [3] ; Freitas, Edmilson D. [4] ; Ferreira, Rute C. [1] ; Negri, Renato G. [1]
Total Authors: 6
Affiliation:
[1] Natl Inst Space Res CPTEC INPE, Ctr Weather Forecast & Climate Studies, Cachoeira Paulista, SP - Brazil
[2] Max Planck Inst Chem, Multiphase Chem Dept, Mainz - Germany
[3] SUNY Albany, Dept Atmospher & Environm Sci, Albany, NY 12222 - USA
[4] Univ Sao Paulo IAG USP, Inst Astron Geophys & Atmospher Sci, Sao Paulo - Brazil
Total Affiliations: 4
Document type: Journal article
Source: WEATHER AND FORECASTING; v. 35, n. 6, p. 2345-2365, DEC 2020.
Web of Science Citations: 0
Abstract

This research explores the benefits of radar data assimilation for short-range weather forecasts in southeastern Brazil using the Weather Research and Forecasting (WRF) Model's three-dimensional variational data assimilation (3DVAR) system. Different data assimilation options are explored, including the cycling frequency, the number of outer loops, and the use of null-echo assimilation. Initially, four microphysics parameterization are evaluated (Thompson, Morrison, WSM6, and WDM6). The Thompson parameterization produces the best results, while the other parameterization generally overestimate the precipitation forecast, especially WDSM6. Additionally, the Thompson scheme tends to overestimate snow, while the Morrison scheme overestimates graupel. Regarding the data assimilation options, the results deteriorate and more spurious convection occurs when using a higher cycling frequency (i.e., 30 min instead of 60 min). The use of two outer loops produces worse precipitation forecasts than the use of one outer loop, and the null-echo assimilation is shown to be an effective way to suppress spurious convection. However, in some cases, the null-echo assimilation also removes convective clouds that are not observed by the radar and/or are still not producing rain, but have the potential to grow into an intense convective cloud with heavy rainfall. Finally, a cloud convective mask was implemented using ancillary satellite data to prevent null-echo assimilation from removing potential convective clouds. The mask was demonstrated to be beneficial in some circumstances, but it needs to be carefully evaluated in more cases to have a more robust conclusion regarding its use. (AU)

FAPESP's process: 15/14497-0 - Nowcasting of intense thunderstorms and understanding of the physical processes inside clouds: the SOS-CHUVA (Severe Weather Observation System)
Grantee:Luiz Augusto Toledo Machado
Support Opportunities: Research Program on Global Climate Change - Thematic Grants