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Abstract:
The NASA Goddard Earth Observing System (GEOS) employs a hybrid four-dimensional ensemble-variational (Hybrid-4DEnVar) atmospheric data assimilation system to provide global, near-real time weather analysis and forecast products. This study introduces a land analysis into the GEOS Hybrid-4DEnVar system to additionally assimilate L-band (1.4 GHz) brightness temperature observations over land from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which are highly sensitive to surface (~0-5 cm) soil moisture. This weakly-coupled land analysis impacts the simulated and forecast atmosphere through the land-atmosphere exchange processes encoded in the atmospheric model. Retrospective assimilation experiments for boreal summer 2017 were conducted with the system at 50 km horizontal resolution. The SMAP assimilation is shown to mitigate errors in screen-level (2-m) specific humidity (q2m) and temperature (T2m), with regional reductions in the root mean square error (RMSE) of q2m and maximum daily T2m by up to 0.4 g kg-1 and 0.3 K, respectively. These improvements are somewhat smaller than those found in a precursor study that used the atmospheric analysis in the simpler, 3-dimensional variational (3DVar) atmospheric assimilation configuration of the current GEOS reanalysis; Hybrid-4DEnVar provides overall better near-surface background estimates than 3DVar and therefore leaves less room for improvement. Finally, in the Hybrid-4DEnVar system with SMAP assimilation, forecasts of q2m and T2m have significantly improved anomaly correlation and RMSE (99% confidence) at lead times out to 5 days compared to the Hybrid-4DEnVar system without SMAP assimilation, with medium-range lead times extended by ~3 h for q2m and ~2 h for T2m. Slight but nevertheless significant improvements are also seen for temperature forecasts at the 925 and 850 mb levels and lead times out to 4 days. Humidity forecasts at 925 and 850 mb are improved out to 1.5-day lead time with 90% confidence.