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Reichle, R. H., S. Q. Zhang, Q. Liu, C. S. Draper, J. Kolassa, and R. Todling:
"Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System"
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 10628-10643, doi:10.1109/JSTARS.2021.3118595, 2021.

Abstract:
Errors in soil moisture adversely impact the modeling of land-atmosphere water and energy fluxes and, consequently, near-surface atmospheric conditions in atmospheric data assimilation systems (ADAS). To mitigate such errors, a land surface analysis is included in many such systems, although not yet in the currently operational NASA Goddard Earth Observing System (GEOS) ADAS. This study investigates the assimilation of L-band brightness temperature (Tb) observations from the Soil Moisture Active Passive (SMAP) mission in the GEOS weakly-coupled land-atmosphere data assimilation system (LADAS) during boreal summer 2017. The SMAP Tb analysis improves the correlation of LADAS surface and root-zone soil moisture vs. in situ measurements by ~0.1-0.26 over that of ADAS estimates; the unbiased root-mean-square error (ubRMSE) of LADAS soil moisture is reduced by 0.002-0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE vs. in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2m_max) is reduced by 0.05 g/kg and 0.04 K, respectively, for LADAS compared to ADAS estimates. Regionally, the RMSE of LADAS q2m and T2m_max is improved by up to 0.4 g/kg and 0.3 K, respectively. Improvement in LADAS specific humidity extends into the lower troposphere (below ~700 mb), with relative improvements in bias of 15-25%, although LADAS air temperature bias slightly increases relative to that of ADAS. Finally, the root-mean-square of the LADAS Tb observation-minus-forecast residuals is smaller by up to ~0.1 K than in a land-only assimilation system, corroborating the positive impact of the Tb analysis on the modeled land-atmosphere coupling.


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NASA-GSFC / GMAO / Rolf Reichle