Impacts of Multiple Radiance Data Assimilation on the Simulation of Typhoon Chan-Hom

Dongmei Xu,Feifei Shen,Aiqing Shu,Hong Li,Xiaoli Xia,Jinzhong Min

doi:10.3390/atmos11090957

Abstract

With the module of assimilating AMSU-A (Advanced Microwave Sounding Unit-A) and AIRS (Atmospheric Infrared Sounder) data in the WRFDA (Weather Research and Forecasting Model Data Assimilation) system, the impacts of joint assimilation of the radiance observations from two satellites on the simulation of typhoon Chan-hom (2015) are addressed. For comparison, experiments with the assimilation of solely GTS (Global Telecommunications System) data, AMSU-A data, or AIRS data are also performed. The results show that, compared to other experiments, the analysis field after assimilating multiple radiance data is closer to the observation. The simulated steering flow in its forecast field is conductive to the northeast twist of the typhoon. In addition, the simulated rainband and the FSS (fraction skill score) calculated from the experiment with assimilating multiple radiance data are better. In the deterministic forecast, better performance is obtained from the simulation with multiple radiance data in the forecast of track, MSLP (minimum sea level pressure), and MSW (maximum surface wind).

Highlights

In the past two decades, the skill of numerical weather prediction (NWP) has been improved for many reasons [1], such as the progresses in NWP models, the improvement of advanced data assimilation (DA) techniques, and the abundance of remote sensing data, mainly including radar data and satellite data
They are over the broad ocean where conventional and radar observations are scarce in most of their life span; it is difficult to detect them by these platforms
Satellite observations account for 90–95% of the assimilated data for global NWP models [6,7,8,9], and the NWP forecast skill is greatly improved with the introduction of satellite data [10,11,12]

Summary

Introduction

In the past two decades, the skill of numerical weather prediction (NWP) has been improved for many reasons [1], such as the progresses in NWP models, the improvement of advanced data assimilation (DA) techniques, and the abundance of remote sensing data, mainly including radar data and satellite data. It is still a challenge to improve the accuracy of the initial state in the numerical model. For tropical cyclones, they are over the broad ocean where conventional and radar observations are scarce in most of their life span; it is difficult to detect them by these platforms. Satellite observations account for 90–95% of the assimilated data for global NWP models [6,7,8,9], and the NWP forecast skill is greatly improved with the introduction of satellite data [10,11,12]. In current NWP centers worldwide, satellite radiance data have become the most significant data used to improve the accuracy of short- and medium-range weather predictions

Objectives

Results

Conclusion