|Van Den Berg, M -|
|Huffman, G -|
|Pellarin, T -|
Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 4, 2011
Publication Date: August 20, 2011
Repository URL: http://handle.nal.usda.gov/10113/58477
Citation: Crow, W.T., Van Den Berg, M.J., Huffman, G.J., Pellarin, T. 2011. Correcting rainfall using satellite-based surfae soil moisture retrievals: The soil moisture analysis rainfall tool(SMART). Water Resources Research. 47:W08521. DOI: 10.1029/2011WR010576. Interpretive Summary: Satellite remote sensing of precipitation offers the potential to monitor agricultural drought in areas of world vulnerable to food security problems. Unfortunately, precipitation accumulation estimates acquired from current and planned satellite missions are plagued by high levels of error over continental areas. Such error severely limits their usefulness for important agricultural applications (like e.g. crop yield forecasting or famine detection and mitigation planning). This research describes a new algorithm – called the Soil Moisture Analysis Rainfall Tool or SMART – which attempts to improve the accuracy of satellite-based precipitation products using a time series of remotely-sensed surface soil moisture retrievals. SMART infers recent rainfall activity (of the lack of it) by examining temporal variations in the surface soil moisture levels and uses these inferences to improve the accuracy of other rainfall accumulation estimates. As a result, SMART provides a badly-needed independent external constraint on existing satellite-based rainfall accumulation products. Results presented here demonstrate that the approach works well in a number of global test bed sites and is of potential value for agricultural drought monitoring activities.
Technical Abstract: Recent work in 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 (AMMA) experiment. Results demonstrate that the algorithm is superior to the Crow et al. (2009) baseline algorithm and capable of broadly improving coarse-scale rainfall accumulations measurements with low risk of degradation. Comparisons with Tropical Rainfall Measurement Mission (TRMM) Precipitation Analysis (TMPA) multi-sensor data products between 2002 July 1 and 2009 December 31 suggest that the introduction of soil moisture information via SMART provides as much coarse-scale (3-day, 1-degree lat/long rainfall accumulation information as thermal infrared satellite observations and more information than monthly rain gauge observations in poorly-instrumented regions.