A Precipitation Detection Method for MWTS-Ⅱ Radiance Assimilation in Typhoon Simulation

FY-3C Microwave Temperature Sounder Ⅱ(MWTS-Ⅱ) lacks observations at 23.8 GHz, 31 GHz and 89 GHz,making it difficult to remove the data contaminated by precipitation in assimilation. In this paper, a fast forward operator based on the Community Radiative Transfer Model(CRTM) was used to analyze the relationship between the observation minus background simulation(O-B) and the cloud fractions in different MWTS-Ⅱ channels. In addition,based on the community Gridpoint Statistical Interpolation(GSI) system, the radiation brightness temperature of the MWTS-Ⅱ was assimilated in the regional Numerical Weather Prediction(NWP) model. In the process of assimilation,Visible and Infrared Radiometer(VIRR) cloud detection products were matched to MWTS-Ⅱ pixels for precipitation detection. For typhoon No. 18 in 2014, impact tests of MWTS-Ⅱ data assimilation was carried out. The results show that,though the bias observation minus analysis(O-A) of assimilated data can be reduced by quality control only with | O-B| 3 K; however, the O-A becomes much smaller while the precipitation detection is performed with F_(virr) 0.9(VIRR cloud fraction threshold of 0.9). Besides, the change of the environmental field around the typhoon is more conducive to make the simulated track closer to the observation. The 72-hour typhoon track simulation error also shows that, after the precipitation detection, the error of simulated typhoon track is significantly reduced, which reflects the validity of a precipitation detection method based on a double criterion of | O-B | 3 K and F_(virr) 0.9.

A Precipitation Detection Method for MWTS-II Radiance Assimilation in Typhoon Simulation

FY-3c Microwave Temperature Sounder II (MWTS-II) lacks observations at 23.8 GHz, 31 GHz and 89 GHz, which makes it difficult to remove the data Ccontaminated by precipitation in assimilation. In this paper, a fast forward operator based on Community Radiative Transfer Model (CRTM) was used to analyze the relationship between the observation minus background simulation (O-B) and the cloud fractions in different MWTS-II channels. And, based on the community Grid point Statistical Interpolation (GSI) system, the radiation brightness temperature of MWTS-II was assimilated to the regional Numerical Weather Prediction (NWP) model. In the process of assimilation, Visible and Infrared Radiometer (VIRR) cloud detection products were matched to MWTS-II pixels for precipitation detection. For typhoon No. 18 in 2014, impact tests of MWTS-II data assimilation was carried out. The results show that, though the bias observation minus analysis (O-A) of assimilated data can be reduced by quality control only with | O-B | < 3K, however, the O-A become much smaller while the precipitation detection was performed with Fvirr < 0.9 (VIRR cloud fraction threshold of 0.9). And also the variation of typhoon''s peripheral environment field is more conducive to force the simulated track to approach the real observation. The 72-hour typhoon track simulation error also shows that, after the precipitation detection, the error of simulated typhoon track is significantly reduced, which reflects the validity of a precipitation detection method based on a double criterion of | O-B | < 3K and Fvirr < 0.9.