Abstract
In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.
Highlights
The numerical weather prediction (NWP) models are largely related to initial conditions and the physical parameterization schemes applied in the model [1]
The observed track, maximum wind speed (MWS), and minimum sea level pressure (MSLP) data of typhysical parameterization were selected for the sensitivity experiments
The maximum wind at 10 m and the minimum surface pressure were directly determined as MWS and MSLP, respectively, in the numerical experiments with an appropriate search size to exclude other typhoons in the domain
Summary
The numerical weather prediction (NWP) models are largely related to initial conditions and the physical parameterization schemes applied in the model [1]. Different schemes are introduced into the dynamic framework of the numerical model to describe the weather process at various scales reasonably. A model’s initial field is affected by the quality of observation data [3,4,5,6,7,8,9,10,11,12] and the data assimilation approaches. It was mainly provided with conventional data, which have low resolution, large errors, and other problems.
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