Quantification of Radar Rainfall Uncertainty—Toward a Real-Time Algorithm

Precipitation is a major input in hydrological models. Radar rainfall data compared with rain gauge measurements provide higher spatial and temporal resolutions. However, radar data obtained from reflectivity patterns are subject to various errors such as errors in reflectivity-rainfall Z-R relationships, variation in vertical profile of reflectivity, and spatial and temporal sampling among others. Characterization of such uncertainties in radar data and their effects on hydrologic simulations is a challenging issue. One way to express these uncertainties is to stochastically generate random ensembles of radar rainfall estimates in order to obtain error bounds of rainfall data. In this presentation, first, several copula-based stochastic ensemble generators are reviewed. Then, a prototype methodology is introduced for near real-time uncertainty quantification of radar rainfall data. The model includes an Artificial Neural Network module for parameter estimation and real-time updating, coupled with a copula-based uncertainty simulator. The model is verified over the state of California and efforts are underway to extend the analyses across the continental United States.