Login / Signup

Assimilation of SMAP Brightness Temperature Observations in the GEOS Land-Atmosphere Data Assimilation System.

Rolf H ReichleSara Q ZhangQing LiuClara S DraperJana KolassaRicardo Todling
Published in: IEEE journal of selected topics in applied earth observations and remote sensing (2021)
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 article 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 versus in situ measurements by ~0.1-0.26 over that of ADAS estimates; the unbiased root-mean-square error of LADAS soil moisture is reduced by 0.002-0.008 m3/m3 from that of ADAS. Furthermore, the global land average RMSE versus in situ measurements of screen-level air specific humidity (q2m) and daily maximum temperature (T2mmax) 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 T2mmax 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.
Keyphrases
  • climate change
  • mycobacterium tuberculosis
  • water quality
  • electronic health record
  • big data
  • physical activity
  • particulate matter
  • machine learning
  • deep learning
  • adverse drug
  • single cell