Application of a radar-rainfall uncertainty model to the NWS multi-sensor precipitation estimator products

Abstract

Given the increasing interest in using radar-based rainfall estimates in hydrologic studies, efforts are critically needed to assess the applicability of recently-proposed methods that focus on quantitative modelling of radar-rainfall uncertainties. The current study reports on the implementation and assessment of an empirically-based approach (known as product-error driven (PED) method) for modelling uncertainties in radar-based rainfall products. In this study, the PED method is applied to a suite of operational radar-based products produced by the U.S. National Weather Service (NWS) Multi-Sensor Precipitation Estimator (MPE) algorithm. The tested MPE products range from a radar-only product, to other products that include various degrees of mean-field and local bias adjustments and gauge-radar optimal merging procedures. Data from an independent dense rain gauge cluster located in south-west Louisiana is used as a proxy for the unknown surface rainfall rates. The focus is on assessing the transferability of the PED across different radar-rainfall products and geographical regions, and the generality of the distributional and parametric assumptions of the PED method. The study also provides insight on the critical issue of the sampling variability and data requirements that govern the implementation, interpretation and possible future enhancements of the radar-error modelling methods.