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

TL;DR

Assimilating SMAP soil moisture observations into a global weather model significantly improves the prediction accuracy of Tropical Cyclone Idai's structure, intensity, and track near land.

Contribution

This study demonstrates the positive impact of SMAP data assimilation on tropical cyclone prediction in a global weather model, a novel application for TC forecasting.

Findings

Up to 18% reduction in wind speed radius.

Up to 23% reduction in forecast intensity error.

Up to 34% reduction in along-track error.

Abstract

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.