Correcting rainfall using satellite-based surface soil moisture retrievals : The Soil Moisture Analysis Rainfall Tool (SMART) W. T. Crow, 1 M. J. van den Berg, 2 G. J. Huffman, 3,4 and T. Pellarin 5 Received 18 February 2011 ; revised 17 June 2011 ; accepted 6 July 2011 ; published 20 August 2011. [1] Recently, Crow et al. (2009) developed an algorithm for enhancing satellite-based land rainfall products via the assimilation of remotely sensed surface soil moisture retrievals into a water balance model. As a follow-up, this paper describes the benefits of modifying their approach to incorporate more complex data assimilation and land surface modeling methodologies. Specific modifications improving rainfall estimates are assembled into the Soil Moisture Analysis Rainfall Tool (SMART), and the resulting algorithm is applied outside the contiguous United States for the first time, with an emphasis on West African sites instrumented as part of the African Monsoon Multidisciplinary Analysis experiment. Results demonstrate that the SMART algorithm is superior to the Crow et al. baseline approach and is capable of broadly improving coarse-scale rainfall accumulations measurements with low risk of degradation. Comparisons with existing multisensor, satellite-based precipitation data products suggest that the introduction of soil moisture information from the Advanced Microwave Scanning Radiometer via SMART provides as much coarse-scale (3 day, 1  ) rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly instrumented regions. Citation: Crow, W. T., M. J. van den Berg, G. J. Huffman, and T. Pellarin (2011), Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART), Water Resour. Res., 47, W08521, doi:10.1029/ 2011WR010576. 1. Introduction [2] Starting with the anticipated launch of its core satel- lite in 2013, land rainfall retrievals from the upcoming Global Precipitation Mission (GPM) constellation will con- tribute to a host of natural hazard, hydrologic, and water resource applications [Gebremichael and Hossain, 2009]. However, expectations for hydrologic applications are tem- pered by known limitations in the resolution and accuracy of satellite-based rainfall accumulation products [ Harris et al., 2007; Li et al., 2009; Tobin and Bennett, 2010; Pan et al., 2010]. Such products are known to suffer from a range of error sources, including sampling uncertainties [ Steiner et al., 2003 ; Nijssen and Lettenmaier, 2004 ; Hossain et al., 2004], beam-filling issues [Kummerow, 1998], and difficul- ties estimating the impact of solid hydrometeors [ Bennartz and Petty, 2001]. Over land, these difficulties are com- pounded by uncertainty in background emissivity values associated with variations in land surface properties [ Mor- land et al., 2001; Bytheway and Kummerow, 2010]. One potential strategy for ameliorating these problems is the use of ancillary land measurements related to precipitation [Pan and Wood, 2007; Pellarin et al., 2008; McCabe et al., 2008; Pellarin et al., 2009]. In particular, remotely sensed surface soil moisture dynamics and rainfall share an obvious physical connection. Such synergistic opportuni- ties are highly relevant given the likely temporal overlap between GPM and the NASA Soil Moisture Active Passive (SMAP) mission. Currently under development in anticipa- tion of a 2014 launch, the SMAP mission will combine L band (1.4 GHz) radar and radiometry to produce a global 10 km soil moisture product with an average repeat time of 2–3 days [Entekhabi et al., 2010]. [3] With this potential in mind, Crow et al. [2009] describe and apply a simple data assimilation approach to correct land rainfall accumulation estimates using remotely sensed surface soil moisture retrievals. Their approach is based on the time series of net additions (or subtractions) of soil water corrections calculated when sequentially assimilating surface soil moisture retrievals into a water balance model using a Kalman filter. These water volumes, or ‘‘analysis increments,’’ are then correctively applied to the satellite-based rainfall product used to force the water balance model. Results by Crow et al. [2009] demonstrate that the approach can correct a substantial fraction of root- mean-square error (RMSE) in 2–10 day accumulation esti- mates obtained from existing multisensor, satellite-based rainfall products. That is, over land, remotely sensed sur- face soil moisture retrievals provide a viable source of cor- rective information for satellite-based rainfall products. 1 Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland, USA. 2 Laboratory of Hydrology and Water Management, Ghent University, Ghent, Belgium. 3 SSAI, Greenbelt, Maryland, USA. 4 NASA GSFC, Greenbelt, Maryland, USA. 5 Laboratoire d’  Etude des Transferts en Hydrologie et Environnement, Grenoble, France. Copyright 2011 by the American Geophysical Union. 0043-1397/11/2011WR010576 W08521 1 of 15 WATER RESOURCES RESEARCH, VOL. 47, W08521, doi :10.1029/2011WR010576, 2011