Error Propagation of Remote Sensing Rainfall Estimates in Soil Moisture Prediction from a Land Surface Model

Abstract

The study presents an in-depth investigation of the properties of remotely sensed rainfall error propagation in the prediction of near-surface soil moisture from a land surface model (LSM). Specifically, two error sources are compared: rainfall forcing due to estimation error by remote sensing techniques and the representation of land?atmospheric processes due to LSM uncertainty [the Community Land Model, version 3.5 (CLM3.5), was used in this particular study]. CLM3.5 is forced by three remotely sensed precipitation products, namely, two satellite-based estimates?NASA?s Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis and NOAA?s Climate Prediction Center morphing technique (CMORPH)?and a rain gauge-adjusted radar?rainfall product from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The error analysis is performed for the warm seasons of 2004 and 2006 and is facilitated through the use of in situ measurements of soil moisture, rainfall, and other meteorological variables from a network of stations capturing the state of Oklahoma (Oklahoma Mesonet). The study also presents a rigorous benchmarking of the Mesonet network as to its accuracy in deriving area rainfall estimates at the resolution of satellite products (0.25° and 3 h) through comparisons against the most definitive measurements of a smaller-yet-denser network of rain gauges in southwestern Oklahoma (Micronet). The study compares error statistics between modeling and precipitation error sources and between the various remote sensing techniques. Results are contrasted between the relatively moist summer period of 2004 to the drier summer period of 2006, indicating model and error propagation dependencies. An intercomparison between rainfall and modeling error shows that the two error sources are of similar magnitudes in the case of high modeling accuracy (i.e., 2004), whereas the contribution of rainfall forcing error to the uncertainty of soil moisture prediction can be lower when the model?s efficiency skill is relatively low (i.e., 2006).