Abstract:
L-band (1.4 GHz) brightness temperature observations from the NASA Soil Moisture Active Passive (SMAP) mission are routinely used in the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system to generate the SMAP Level 4 Soil Moisture (L4_SM) product. The L4_SM product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture as well as related land surface states and fluxes from 31 March 2015 to present with a latency of ~2.5 days. The L4_SM algorithm (i) uses an ensemble approach, (ii) is spatially distributed, (iii) performs downscaling from the resolution of the observations to that of the model, and (iv) respects the relative uncertainties of the modeled and observed brightness temperatures. The resulting soil moisture estimates meet the root-mean-square error target of 0.04 m3/m3 (after removal of the long-term mean differences). This presentation investigates the data assimilation diagnostics from the L4_SM algorithm, including the statistics of the differences between model-predicted brightness temperatures and SMAP observations and the statistics of the adjustments (or increments) to the modeled soil moisture state. We demonstrate that there is little bias in the soil moisture analysis due to the use of the L-band climatology from the Soil Moisture Ocean Salinity (SMOS) mission in the L4_SM system. Furthermore, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system. Finally, we explore the possibility of using SMAP observations in areas where SMOS does not provide an L-band climatology because of radio-frequency interference.