Open AccessThe Impact of Rainfall Error Characterization on the Estimation of Soil Moisture Fields in a Land Data Assimilation System
Author(s) -
Viviana Maggioni,
Rolf H. Reichle,
Emmanouil N. Anagnostou
Publication year - 2012
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-11-0115.1
Subject(s) - data assimilation , environmental science , water content , forcing (mathematics) , satellite , meteorology , land cover , mean squared error , ensemble kalman filter , assimilation (phonology) , radar , precipitation , rain gauge , climatology , mathematics , geology , land use , kalman filter , statistics , computer science , geography , telecommunications , linguistics , civil engineering , philosophy , geotechnical engineering , aerospace engineering , engineering , extended kalman filter
This study presents a numerical experiment to assess the impact of satellite rainfall error structure on the efficiency of assimilating near-surface soil moisture observations. Specifically, the study contrasts a multidimensional satellite rainfall error model (SREM2D) to a simpler rainfall error model (CTRL) currently used to generate rainfall ensembles as part of the ensemble-based land data assimilation system developed at the NASA Global Modeling and Assimilation Office. The study is conducted in the Oklahoma region using rainfall data from a NOAA multisatellite global rainfall product [the Climate Prediction Center (CPC) morphing technique (CMORPH)] and the National Weather Service rain gauge–calibrated radar rainfall product [Weather Surveillance Radar-1988 Doppler (WSR-88D)] representing the “uncertain” and “reference” model rainfall forcing, respectively. Soil moisture simulations using the Catchment land surface model (CLSM), obtained by forcing the model with reference rainfall, are randomly perturbed to represent satellite retrieval uncertainty, and assimilated into CLSM as synthetic near-surface soil moisture observations. The assimilation estimates show improved performance metrics, exhibiting higher anomaly correlation coefficients (e.g., ~0.79 and ~0.90 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively) and lower root-mean-square errors (e.g., ~0.034 m3 m−3 and ~0.024 m3 m−3 in the SREM2D nonassimilation and assimilation experiments for root zone soil moisture, respectively). The more elaborate rainfall error model in the assimilation system leads to slightly improved assimilation estimates. In particular, the relative enhancement due to SREM2D over CTRL is larger for root zone soil moisture and in wetter rainfall conditions.