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Title:   Author  item
                                            CHEN, F - Science Systems, Incitem
                                            Crow, Wadeitem
                                            Holmes, Thomas
  Submitted to: Journal of Applied Remote Sensing (JARS)
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 9/15/2012
                                    Publication  Date: 11/28/2012
                                    Citation: Chen, F., Crow, W.T., Holmes, T.R. 2012. Improving long-term, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART). Journal of Applied Remote Sensing (JARS). 6(1):603-604.Interpretive Summary: Long-term, global retrospective rainfall datasets over land are important for understanding trends and anomalies in agricultural drought and water resource availability. Unfortunately, in many parts of the world, such datasets are difficult to construct and prone to large errors. This paper describes the application of an  innovative inversion technique (called the Soil Moisture Analysis Rainfall Tool or SMART) to improve a long-term (1979 to 1998) global precipitation dataset using heritage remotely-sensed surface soil moisture retrievals products acquired during the same time period. Validation results show that the soil moisture data set can be inverted to improve the short-term (i.e. < 1 month) accuracy of the rainfall accumulation dataset. Using SMART, a corrected 1979 to 1998 global precipitation dataset (over land) is constructed. Distribution of this dataset to the land surface hydrology community will enhance our ability to baseline future trends in water availability for global agricultural regions.
                                Technical Abstract: Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available ground observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed which allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using the variable window method relative to an existing fixed-window version of SMART over a range of accumulation periods. Using this modified version of SMART, and heritage satellite surface soil moisture products, a 1.0-degree, 1979-1998 global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the updated correction scheme demonstrates improved root-mean-square-error and correlation accuracy and provides a higher probability of detection and lower false alarm rates for 3-day rainfall accumulation estimates, except for the heaviest (99th percentile) cases. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.


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