Impact of assimilating SMAP observations over land on tropical cyclone representation: A case study of Tropical Cyclone Idai

Soil moisture conditions can affect the evolution of a tropical cyclone (TC) that interacts with land. Hence, better constraining soil moisture initial conditions in a numerical weather prediction model can potentially improve forecasts of TC evolution near or over land, where TCs have strong socio‐economic impact. This study examines the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global numerical weather prediction model on the representation of southwest Indian Ocean TC Idai (2019). Two sets of model analyses of TC Idai are compared: (a) an analysis that is comparable to the operational GEOS analysis and (b) an analysis that additionally assimilates SMAP brightness temperature observations over land. Results indicate that SMAP assimilation leads to pronounced improvements in the representation of TC Idai 's three‐dimensional vorticity structure for the period when the storm is over land. Additionally, the assimilation of SMAP observations results in the storm being more compact in the analysis relative to the control experiment. These differences in representation are traced to wetter soil moisture conditions from SMAP assimilation over key land areas that interact with the TC circulation and result in an increased moisture flux to the atmosphere. A preliminary investigation of 5‐day forecasts initialized from both analyses further shows reduced errors in intensity and along‐track position for forecasts generated from initial conditions constrained with SMAP observations. The results demonstrate the benefit of SMAP assimilation for an example case study of TC Idai and provide motivation to further investigate the impact of SMAP assimilation on TC analyses and forecasts more generally.