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Title:   Author  item
                                            CHEN, F - Science Systems, Incitem
                                            Crow, Wadeitem
                                            Holmes, Thomas
  Submitted to: Meeting Abstract
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 11/15/2012
                                    Publication  Date: 12/3/2012
                                    Citation: Chen, F., Crow, W.T., Holmes, T.R. 2012. Improving long-term global precipitation dataset using multi-sensor surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART)[abstract]. 2012 Fall American Geophysical Union. 2012 CDROM.Interpretive Summary:
                                Technical Abstract: Using multiple historical satellite surface soil moisture products, the Kalman Filtering-based 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 rain gauge 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 this 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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