HR: 14:45h
AN: H43E-06    [Abstracts]
TI: Radar Based Probabilistic Quantitative Precipitation Estimation: First Results of Large Sample Data Analysis
AU: Ciach, G J
EM: g-ciach@uiowa.edu
AF: IIHR-Hydroscience & Engineering The University of Iowa, 100 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, IA 52242 United States
AU: * Krajewski, W F
EM: witold-krajewski@uiowa.edu
AF: IIHR-Hydroscience & Engineering The University of Iowa, 100 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, IA 52242 United States
AU: Villarini, G
AF: IIHR-Hydroscience & Engineering The University of Iowa, 100 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, IA 52242 United States
AB: Large uncertainties in the operational precipitation estimates produced by the U.S. national network of WSR-88D radars are well-acknowledged. However, quantitative information about these uncertainties is not operationally available. In an effort to fill this gap, the U.S. National Weather Service (NWS) is supporting the development of a probabilistic approach to the radar precipitation estimation. The probabilistic quantitative precipitation estimation (PQPE) methodology that was selected for this development is based on the empirically-based modeling of the functional-statistical error structure in the operational WSR-88D precipitation products under different conditions. Our first goal is to deliver a realistic parameterization of the probabilistic error model describing its dependences on the radar-estimated precipitation value, distance from the radar, season, spatiotemporal averaging scale, and the setup of the precipitation processing system (PPS). In the long-term perspective, when large samples of relevant data are available, we will extend the model to include the dependences on different types of precipitation estimates (e.g. polarimeteric and multi-sensor), geographic locations and climatic regimes. At this stage of the PQPE project, we organized a 6-year-long sample of the Level II data from the Oklahoma City radar station (KTLX), and processed it with the Built 4 of the PPS that is currently used in the NWS operations. This first set of operational products was generated with the standard setup of the PPS parameters. The radar estimates are completed with the corresponding raingauge data from the Oklahoma Mesonet, the ARS Little Washita Micronet and the EVAC PicoNet covering different spatial scales. The raingauge data are used as a ground reference (GR) to estimate the required uncertainty characteristics in the radar precipitation products. In this presentation, we describe the first results of the large-sample uncertainty analysis of the products. Based on the available surface temperatures, the data-sample is divided into three seasons: warm, intermediate and cold. For each of these sub-samples we estimate several conditional statistics using a nonparametric functional estimation technique. We discuss the possible parametric models that can reproduce the estimated dependences with satisfactory accuracy.
DE: 1800 HYDROLOGY
DE: 1854 Precipitation (3354)
DE: 1869 Stochastic processes
DE: 1894 Instruments and techniques
SC: Hydrology [H]
MN: 2005 Joint Assembly