  Soil moisture conditions can influence the evolution of a tropical cyclone
(TC) that is partially or completely over land. Hence, better constraining soil
moisture initial conditions in a numerical weather prediction model can
potentially improve predictions of TC evolution near or over land. 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 weather model on the prediction of South-West Indian Ocean TC
Idai (2019). Two sets of retrospective forecasts of TC Idai are compared in an
Observing System Experiment framework: (i) forecasts initialized from an
analysis that is comparable to the GEOS operational analysis and (ii) forecasts
initialized from 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 structure and
prediction of its intensity and track. The wind speed radius (a measure for TC
compactness) is reduced by up to 18% in the analysis with SMAP assimilation
relative to the control experiment without SMAP assimilation. The forecast
intensity error, measured against the observed intensity, is reduced by up to
23%. The forecast along-track error is reduced by up to 34%, indicating a more
accurate propagation speed, while the impact of SMAP assimilation on the
forecast cross-track error is neutral. These results provide a valuable
demonstration that SMAP assimilation can have a highly beneficial impact on TC
prediction in global weather forecast models.
