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Soil moisture conditions can influence the evolution of a tropical cyclone(TC) that is partially or completely over land. Hence, better constraining soilmoisture initial conditions in a numerical weather prediction model canpotentially improve predictions of TC evolution near or over land. This studyexamines the impact of assimilating observations from the NASA Soil MoistureActive Passive (SMAP) mission into the NASA Goddard Earth Observing System(GEOS) global weather model on the prediction of South-West Indian Ocean TCIdai (2019). Two sets of retrospective forecasts of TC Idai are compared in anObserving System Experiment framework: (i) forecasts initialized from ananalysis that is comparable to the GEOS operational analysis and (ii) forecastsinitialized from an analysis that additionally assimilates SMAP brightnesstemperature observations over land. Results indicate that SMAP assimilationleads to pronounced improvements in the representation of TC Idai structure andprediction of its intensity and track. The wind speed radius (a measure for TCcompactness) is reduced by up to 18% in the analysis with SMAP assimilationrelative to the control experiment without SMAP assimilation. The forecastintensity error, measured against the observed intensity, is reduced by up to23%. The forecast along-track error is reduced by up to 34%, indicating a moreaccurate propagation speed, while the impact of SMAP assimilation on theforecast cross-track error is neutral. These results provide a valuabledemonstration that SMAP assimilation can have a highly beneficial impact on TCprediction in global weather forecast models.

Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai