Kolassa, J., R. H. Reichle, M. Ganeshan, E. L. McGrath-Spangler, and O. Reale:
"Assessing the Impact of SMAP Soil Moisture Data Assimilation on the Simulation and Prediction of Tropical Cyclone Idai"
Presentation at the AGU Fall Meeting, Chicago, IL, USA, 2022.

Abstract:
This work is focused on the role of soil moisture in the evolution of tropical cyclones (TCs) approaching land and after landfall. The underlying working hypothesis is that dry land surface conditions can lead to faster dissipation of a TC over land (often associated with changes in precipitation structure), whereas very wet conditions can help sustain or even re-intensify a TC. The ability to forecast post-landfall TC evolution and mitigate the associated socio-economic impact thus hinges on accurate knowledge of land surface conditions prior to landfall. The NASA Soil Moisture Active Passive (SMAP) mission provides accurate observations of soil moisture globally and at high revisit times of 2-3 days. It has been shown that the assimilation of SMAP brightness temperatures (Tb) significantly improves modeled land surface states and thus has the potential to constrain land surface initial conditions in TC forecasts. In this presentation, we investigate this potential through an extensive set of Observing System Experiments that systematically assess the impact of assimilating SMAP Tbs on TC forecast skill in the Goddard Earth Observing System. We focus here on the case of TC Idai, which made landfall twice in Mozambique in March 2019. We show that the assimilation of SMAP produces an overall slight scale contraction of the storm and a slightly reduced error in the analyzed track. Consistent with the reduced scale, assimilation of SMAP produces increased soil moisture in close proximity to the track, and a drier soil moisture analysis in land areas affected by the storm’s circulation but at greater distance from the storm center. These changes are reflected in the surface fluxes, which propagate the land surface state changes to the atmosphere. This results in a TC that is slightly weaker, but with a better-defined eye and more realistic spatial scale than a simulation without SMAP data assimilation. The changes in soil moisture resulting from the assimilation of SMAP also impact the total precipitation amounts as well as the precipitation structure associated with TC Idai.


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NASA-GSFC / GMAO / Rolf Reichle