Abstract
The Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite provides continuous observations every 10 min. This study investigates the assimilation of every-10-min radiance from the AHI with the POD-4DEnVar method. Cloud detection is conducted in the AHI quality control procedure to remove cloudy and precipitation-affected observations. Historical samples and physical ensembles are combined to construct four-dimensional ensembles according to the observed frequency of the Himawari-8 satellite. The purpose of this study was to test the potential impacts of assimilating high temporal resolution observations with POD-4DEnVar in a numerical weather prediction (NWP) system. Two parallel experiments were performed with and without Himawari-8 radiance assimilation during the entire month of July 2020. The results of the experiment with radiance assimilation show that it improves the analysis and forecast accuracy of geopotential, horizontal wind field and relative humidity compared to the experiment without radiance assimilation. Moreover, the equitable threat score (ETS) of 24-h accumulated precipitation shows that assimilating Himawari-8 radiance improves the rainfall forecast accuracy. Improvements were found in the structure, amplitude and location of the precipitation. In addition, the ETS of hourly accumulated precipitation indicates that assimilating high temporal resolution Himawari-8 radiance can improve the prediction of rapidly developed rainfall. Overall, assimilating every-10-min AHI radiance from Himawari-8 with POD-4DEnVar has positive impacts on NWP.
Highlights
The accuracy of weather forecasts has been greatly improved in recent decades, which can be credited mostly to the development of numerical weather prediction (NWP) [1].With the development of numerical models, the accuracy of the initial conditions is extremely critical for the quality of NWP
The Proper Orthogonal Decomposition (POD)-4DEnVar method has been evaluated by the Lorenz96 model, observing system simulation experiments (OSSEs) and shallow water wave equation, and the results show that this method outperforms the 4DVar and ensemble Kalman filter (EnKF) methods with lower computational costs [25,26,27]
The potential impacts of assimilating every-10-min Himawari-8 radiance data were investigated based on a four-dimensional ensemble variation assimilation method
Summary
With the development of numerical models, the accuracy of the initial conditions is extremely critical for the quality of NWP. With the growing number of observations, data assimilation has become an effective way to provide accurate initial conditions by combining the information between a numerical model and observations [2,3]. The 4DVar can assimilate observations at multiple analysis times with the background error covariance evolving over the assimilation window with the tangent linear and adjoint model [7,8,9,10]. The background error covariance at the beginning of the assimilation window is still static, and the coding, maintenance and updating of the tangent linear and adjoint model are Remote Sens.
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