Impact of Assimilating Retrieval Thermodynamic Fields and Radar Data

To investigate the potential benefits of assimilating 3D temperature and water-vapor information in addition to radar observations in multiscale weather systems, a thermodynamics retrieval algorithm was employed, and three sets of observing system simulation experiments (OSSEs) were conducted by using the WRF–LETKF Radar Assimilation System (WLRAS). With one frontal system accompanied heavy rainfall case study, results showed that assimilating temperature data had a significant impact on the final analysis at the mid-level of stratiform areas despite the warm and wet bias of the retrieved fields, resulting in improved forecasts of heavy rainfall. On the other hand, assimilating water vapor information only helped to reconstruct the range and intensity of the cold pool near the surface, but the improvement in 3-hour rainfall forecast was limited. The optimal results of analysis and short-term forecast were achieved when both the retrieved temperature and water vapor fields were assimilated. Overall, this study illustrated the benefits of assimilating three-dimensional thermal variables in multiscale precipitation systems.