A preliminary evaluation of FY-4A visible radiance data assimilation by the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for a tropical storm case

Abstract

Satellite visible radiance data that contain rich cloud and precipitation information are increasingly assimilated to improve the forecasts of numerical weather prediction models. This study evaluates the Data Assimilation Research Testbed (DART, Manhattan release v9.8.0), coupled with the Weather Research and Forecasting (WRF) model (ARW v4.1.1) and the Radiative Transfer for TOVS (RTTOV, v12.3) package, for assimilating the simulated visible imagery of the FY-4A geostationary satellite located over Asia in an Observing System Simulation Experiment (OSSE) framework. The OSSE was performed for the tropical storm Higos that occurred in 2020 and contains multi-layer mixed-phase cloud and precipitation processes. The advantages and limitations of DART for assimilating FY-4A visible imagery were evaluated. Both single-observation experiments and cycled data assimilation (DA) experiments were performed to study the impact of different filter algorithms available in DART, variables being cycled, observation outlier thresholds, observation errors, and observation thinning. The results show that assimilating visible radiance data significantly improves the analysis of the cloud water path (CWP) and cloud coverage (CFC) from first-guess forecasts. The rank histogram filter (RHF) allows WRF to more accurately simulate CWP and CFC compared with the ensemble adjustment Kalman filter (EAKF) although it took roughly twice as long as the latter. By cycling both cloud and non-cloud variables, specifying large outlier threshold values, or setting smaller observation errors without thinning of observations, WRF achieved a better simulation of CWP and CFC. With model integration, DA of the visible radiance data also generated a slightly positive impact on non-cloud variables as they were adjusted through the model dynamics and physics related to cloud processes. In addition, the DA improved the representation of precipitation. However, the impact on the rain rate is limited by the inabilities of the DA to improve cloud vertical structures and cloud phases. A negative impact of the DA on cloud variables was found due to the nature of the non-linear forward operator and the non-Gaussian distribution of the prior. Future works should explore faster and more accurate forward operators suitable for assimilating FY-4A visible imagery, techniques to reduce the non-linear and non-Gaussian errors, and methods to correct the location errors which correspond to the clouds underestimated by the first guess.