89th American Meteorological Society Annual Meeting

Monday, 12 January 2009
Assessment of radar-based precipitation products in the CONUS for advances in multi-sensor precipitation reanalysis
Hall 5 (Phoenix Convention Center)
Brian R. Nelson, NOAA/NESDIS/NCDC, Asheville, NC; and D. Kim and D. J. Seo
Poster PDF (501.4 kB)
NCDC and OHD have worked together on the multi-sensor precipitation reanalysis (MPR) proof of concept. The proof of concept has shown the benefits of performing a reanalysis of radar-based precipitation products, mainly the products that come from the NEXRAD Level III archive. In particular the collaboration used the NEXRAD digital precipitation array (DPA) as well as the Hydrometeorological Automated Data System (HADS) rain gauge data and the NWS Cooperative observers network rain gauge data in a reanalysis. The objectives of the proof of concept were to: implement the real-time multi-sensor precipitation estimation algorithm in order to make improvements to this system and then leverage these for improvments in quantitative precipitation estimation (QPE); use additional data inputs that are not available in real-time; perform optimization for parameter estimation that is also not possible in the real-time setting; and to take advantage of the lessons learned from 15-years of operational experince and put them to use in the reanlysis effort.

As an extension of the reanalysis effort we provide an assessment of the radar-based precipitation products available for the CONUS. Some of the radar-based QPE available are the NCEP Stage IV, the developmental NSSL Q2, the NWS Stage III, and the NCDC-OHD MPR products. In the framework of reanalysis, our assessment looks at the large-scale precipitation products in a climatological sense as well as at finer scales (i.e. daily, monthly). We provide looks at temporal accumulations for CONUS-wide scales as well as at regional scales. The challenges we found in this assessment include finding a suitable overlap period for all products, finding a suitable spatial overlap, determining the input data sets for the multi-sensor products, and deciphering the key points in the data set development that might make one product different from another.

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