The Impact of Ground-based Microwave Radiometer Data Assimilation: A Case Study

Atmospheric profiles are indispensable for operational weather forecasting across a wide range of scales and latitudes. Despite their importance, the assimilation of tropospheric wind and temperature profiles remains a complex task with considerable potential to markedly improve weather predictions. This research investigates the impact of Ground-based Microwave Radiometer profile measurements on Numerical Weather Prediction (NWP) using a real rainfall case study. Employing the Local Error-Subspace Transform Kalman filter (LESTKF), we assimilate temperature and wind profiles derived from the Ground-based Microwave Radiometer observation network. The coupled WRF-PDAF (Parallel Data Assimilation Framework) system is utilized to conduct twin experiments. These experiments, which vary observation variables and localization distances, offer valuable insights into the assimilation process. The study evaluates potential configurations for future profile measurements and discusses recommended localization distances. The results demonstrate that incorporating multiple observation variables leads to substantial forecast improvements compared to using individual variables alone. The research culminates in a recommendation for an optimal localization distance, which has the potential to enhance the accuracy and reliability of weather forecasting.