Extended Abstract (712K)4.6
Multi-sensor precipitation reanalysis
Brian R. Nelson, NOAA/NESDIS/NCDC, Asheville, NC; and D. Kim, D. J. Seo, and J. Bates
The network of Weather Surveillance Radar – 1988 Doppler (WSR-88D), commonly known as the Next Generation Weather Radar (NEXRAD), consists of approximately 140 sites in the Continenal U.S. Most radars have been operational for approximately 10 years and have been transmitting radar reflectivity estimates for the NEXRAD Precipitation Processing System (PPS), which produces radar-derived products in real time for forecasters in support of the National Weather Service's mission. The PPS computes rainfall estimates in stages and, historically, the Stage III products are used at the River Forecast Centers (RFCs).
Recently, the Multisensor Precipitation Estimation (MPE) algorithm has replaced the Stage III algorithm in the PPS. The MPE algorithm is an improvement on Stage III radar rainfall estimates in several areas. The MPE algorithm delineates an effective radar coverage area based on seasonal radar climatology. The mosaicking scheme uses data from adjoining radars based on the radar sampling geometry. The algorithm provides analysis over the entire RFC not just radar by radar. The algorithm has an improved mean-field bias correction, and it includes a new local bias correction procedure. Each of these improvements are designed to reduce or eliminate biases that are inherent in the radar rainfall estimates, but the algorithm is geared toward real-time operational implementation.
The NEXRAD data are available for an approximately 10 year record, which provides for a long term data set at high resolution both spatially and temporally. We have implemented the MPE algorithm with the historical NEXRAD data in a reanalysis mode to develop a data set that is suited for long term climatological applications. We perform the reanalysis with the hope of reducing biases that continue to plague operational products. Reanalysis allows for several improvements to the historical radar rainfall products. One of the main improvements included in the reanalysis is to incorporate more in-situ measurements of rainfall, which are important for the bias correction procedures. Higher quality and higher density rain gauge measurements will help to improve the multisensor rainfall estimates. Further, the reanalysis allows for detailed experiments for parameter tuning. All of these experiments will allow us to improve current estimates such that they are more suited for long term water resources and climate applications. We present preliminary results of our analysis over the Southeastern U.S.
Session 4, Hydrologic Data Assimilation, Parameter Estimation, And Uncertainty
Thursday, 2 February 2006, 1:30 PM-5:15 PM, A403
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