Open AccessThe Efficiency of Assimilating Satellite Soil Moisture Retrievals in a Land Data Assimilation System Using Different Rainfall Error Models
Author(s) -
Viviana Maggioni,
Rolf H. Reichle,
Emmanouil N. Anagnostou
Publication year - 2013
Publication title -
journal of hydrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.733
H-Index - 123
eISSN - 1525-755X
pISSN - 1525-7541
DOI - 10.1175/jhm-d-12-0105.1
Subject(s) - environmental science , data assimilation , radiometer , satellite , water content , forcing (mathematics) , precipitation , anomaly (physics) , meteorology , climatology , atmospheric sciences , remote sensing , geology , physics , geotechnical engineering , condensed matter physics , engineering , aerospace engineering
The efficiency of assimilating near-surface soil moisture retrievals from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations in a Land Data Assimilation System (LDAS) is assessed using satellite rainfall forcing and two different satellite rainfall error models: a complex, multidimensional satellite rainfall error model (SREM2D) and the simpler (control) model (CTRL) used in the NASA Goddard Earth Observing System Model, version 5 LDAS. For the study domain of Oklahoma, LDAS soil moisture estimates improve over the satellite retrievals and the open-loop (no assimilation) land surface model estimates, exhibiting higher daily anomaly correlation coefficients (e.g., 0.36 in the open loop, 0.38 in the AMSR-E, and 0.50 in LDAS for surface soil moisture). The LDAS soil moisture estimates also match the performance of a benchmark model simulation forced with high-quality radar precipitation. Compared to using the CTRL rainfall error model in LDAS, using the more complex SREM2D exhibits only slight improvements in soil moisture estimates.