Improving the WRF Forecast of Landfalling Tropical Cyclones Over the Asia‐Pacific Region by Constraining the Cloud Microphysics Model With GPM Observations

We proposed a method to improve the forecasts of landfalling tropical cyclones (LTCs) by constraining the “cloud physics” with Global Precipitation Measurement (GPM) satellite observations. Eight typical LTCs that are well observed by GPM satellite in the Asia‐Pacific region from 2015 to 2021 are selected to verify the feasibility of this method. Using a cloud‐resolving model, the LTCs are simulated for 3 days with both the original and modified microphysics scheme for comparison. The improvement of LTC forecasts is evaluated in terms of hydrometeor structure, amplitude, and location. Most notably, the structure forecast of condensed water improved up to 32% on average for all LTCs. The location forecast and amplitude forecast of condensed water also improved to varying degrees. Moreover, it is found that the error of LTC forecasts was reduced even more by using microphysics constraints from GPM observation than that by assimilating GPM data directly in other research.

[1]  Zuhang Wu,et al.  A Comparison of Spectral Bin Microphysics versus Bulk Parameterization in Forecasting Typhoon In-Fa (2021) before, during, and after Its Landfall , 2022, Remote. Sens..

[2]  Zuhang Wu,et al.  A Comparison of Convective and Stratiform Precipitation Microphysics of the Record-breaking Typhoon In-Fa (2021) , 2022, Remote. Sens..

[3]  Hengchi Lei,et al.  Precipitation characteristics of typhoon Lekima (2019) at landfall revealed by joint observations from GPM satellite and S-band radar , 2021 .

[4]  T. Kubota,et al.  Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency using Space-borne Radar and Microwave Imager of the Global Precipitation Measurement as Reference , 2021, Monthly Weather Review.

[5]  Liguang Wu,et al.  Landfalling Tropical Cyclone Research Project (LTCRP) in China , 2019 .

[6]  Soo Ya Bae,et al.  Development of a Single-Moment Cloud Microphysics Scheme with Prognostic Hail for the Weather Research and Forecasting (WRF) Model , 2018, Asia-Pacific Journal of Atmospheric Sciences.

[7]  Riko Oki,et al.  THE GLOBAL PRECIPITATION MEASUREMENT (GPM) MISSION FOR SCIENCE AND SOCIETY. , 2017, Bulletin of the American Meteorological Society.

[8]  G. Thompson,et al.  Improvements to the snow melting process in a partially double moment microphysics parameterization , 2017 .

[9]  Takuji Kubota,et al.  Experimental Assimilation of the GPM Core Observatory DPR Reflectivity Profiles for Typhoon Halong (2014) , 2016 .

[10]  Peter Bauer,et al.  The quiet revolution of numerical weather prediction , 2015, Nature.

[11]  Filipe Aires,et al.  Introducing multisensor satellite radiance‐based evaluation for regional Earth System modeling , 2014 .

[12]  A. Hou,et al.  The Global Precipitation Measurement Mission , 2014 .

[13]  Y. Rahmat-Samii,et al.  NEXRAD-In-Space: A Geostationary Orbiting Doppler Radar for Hurricane Monitoring and Studies , 2011 .

[14]  R. Fovell,et al.  Impact of cloud microphysics on hurricane track forecasts , 2007 .

[15]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[16]  F. Tapiador,et al.  Predicting Tropical Cyclone Rapid Intensification From Satellite Microwave Data and Neural Networks , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Yanqiong Xie,et al.  Radiance-based assessment of bulk microphysics models with seven hydrometeor species in forecasting Super-typhoon Lekima (2019) near landfall , 2022, Atmospheric Research.

[18]  Song-You Hong,et al.  The impact of microphysical schemes on hurricane intensity and track , 2011 .