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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATION OF CLIMATE VARIABILITY AND FORECASTS INTO RISK-BASED MANAGEMENT TOOLS FOR AGRICULTURE PRODUCTION AND RESOURCE CONSERVATION

Location: Great Plains Agroclimate and Natural Resources Research Unit

Title: An independent assessment of the monthly PRISM gridded precipitation product in central Oklahoma

Authors
item Schneider, Jeanne
item Ford Jr, Donald

Submitted to: An Independent Assessment of the Monthly PRISM Gridded Precipitation Product in Central Oklahoma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: January 16, 2013
Publication Date: April 30, 2013
Citation: Schneider, J.M., Ford Jr., D.L. 2013. An independent assessment of the monthly PRISM gridded precipitation product in central Oklahoma. Journal of the Atmospheric Sciences. 3(2):249-258.

Interpretive Summary: The development of climate-informed decision support tools for agricultural management requires long-duration location-specific climatologies due to the extreme spatiotemporal variability of precipitation. The traditional source of precipitation data (rain gauges) are too sparsely located to fill this need. Newer sources of data (radar-derived estimates) only cover the last few years. Spatially and temporally continuous climate products covering the contiguous U.S. going back in time to 1895 have been created by the PRISM Climate Group and appear to address this problem. An assessment of the PRISM monthly precipitation product was conducted using independent data collected from a dense network of rain gauges at a USDA research laboratory in central Oklahoma. The quality of the independent data was checked, including comparison to a co-located automated rain gauge operated by the Oklahoma Mesonet. Results indicate that the independent data may slightly underestimate monthly total precipitation, but are of sufficient quality to use in the evaluation of the PRISM product. The area average of the independent gauges over a matching size area was then compared to the PRISM estimates. The monthly gridded PRISM precipitation estimates are close to the independent observed data in terms of means (smaller by 3% to 4.5%) and probability distributions (within ~ 4%), with variances too small by 7% to 11%. From the point of view of agricultural decision support, these results indicate that PRISM estimates might be useful for downscaling climate forecasts or for driving weather generators, assuming appropriate corrections to the statistics were applied. However, the number of months with potentially significant differences (17% of the months examined) precludes the use of PRISM estimates for any retrospective month-by-month analyses of possible interactions between climate, crop management, and productivity.

Technical Abstract: The development of climate-informed decision support tools for agricultural management requires long-duration location-specific climatologies due to the extreme spatiotemporal variability of precipitation. The traditional source of precipitation data (rain gauges) are too sparsely located to fill this need. Newer sources of data (radar-derived estimates) only cover the last few years. Spatially and temporally continuous climate products covering the contiguous U.S. going back in time to 1895 have been created by the PRISM Climate Group and appear to address this problem. An assessment of the PRISM monthly precipitation product was conducted using independent data collected from a dense network of rain gauges at a USDA research laboratory in central Oklahoma. The quality of the independent data was checked, including comparison to a co-located automated rain gauge operated by the Oklahoma Mesonet. Results indicate that the independent data may slightly underestimate monthly total precipitation, but are of sufficient quality to use in the evaluation of the PRISM product. The area average of the independent gauges over a matching size area was then compared to the PRISM estimates. The monthly gridded PRISM precipitation estimates are close to the independent observed data in terms of means (smaller by 3% to 4.5%) and probability distributions (within ~ 4%), with variances too small by 7% to 11%. From the point of view of agricultural decision support, these results indicate that PRISM estimates might be useful for downscaling climate forecasts or for driving weather generators, assuming appropriate corrections to the statistics were applied. However, the number of months with potentially significant differences (17% of the months examined) precludes the use of PRISM estimates for any retrospective month-by-month analyses of possible interactions between climate, crop management, and productivity.

Last Modified: 7/28/2014
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