Joint Distribution of Multiplicative Errors in Radar and Satellite QPEs and Its Use in Estimating the Conditional Exceedance Probability

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Wednesday, 5 February 2014: 5:00 PM
Room C210 (The Georgia World Congress Center )
Yu Zhang, NOAA/NWS, Silver Spring, MD; and E. Habib, R. J. Kuligowski, and D. Kim

This work characterizes the joint distribution of multiplicative errors (ME) in radar (R) and satellite (S) quantitative precipitation estimates (QPEs). A semi-parametric framework is established on the basis of this joint distribution to describe the probability of rainfall exceeding a particular threshold given concurrent R and S-based estimates (referred to as conditional exceedance probability, or CEP). This framework entails integrating copula-based joint distributions of MEs over a range of rainfall amounts to yield the joint probability of exceedance, which forms the basis for estimating CEP. In demonstrating this approach, MEs were computed for R (Weather Surveillance Radar-1988 Doppler) and S (Self-calibrating Multivariate Precipitation Retrieval) for central Texas over 2000-2007 using gauge records as the reference. Analysis of the MEs in R and S reveals a substantial correlation between the two, and it also shows that the interdependence is complex as a considerable portion of S QPEs are negatively biased while their concurrent R values are bias-neutral. CEP values from the semi-parametric approach is found to be generally superior to those empirically derived based on rainfall estimates: it yields values for a wide range of rainfall thresholds and suffers much fewer discontinuities and artifacts that the empirical results exhibit.