Independent Assessment of Incremental Complexity in NWS Multisensor Precipitation Estimator Algorithms

Abstract

This paper presents a comprehensive intercomparison analysis of different radar-based multisensor precipitation products generated operationally by the National Weather Service (NWS) Multisensor Precipitation Estimator (MPE) algorithm from the Weather Surveillance Radar–1988 Doppler version and concurrent rain gauge data. The analysis provides close insight into different effects of the increasing degree of complexitzy in the MPE algorithms. First, a gauge-only product produced by the MPE algorithm was assessed. Then six MPE products were analyzed: a radar-only product, a mean-field bias-adjusted product, a local bias-adjusted product, two products that are based on merging the bias-adjusted products with gauge observations, and a final product that includes human intervention by NWS forecasters. Data from a dense, carefully maintained experimental rain gauge cluster are used as an independent surface reference. A number of summary and conditional statistics are applied to the product intercomparisons. The results reported in this paper show that the most effective improvement of the rainfall products comes from applying the mean-field bias adjustment to the radar-only product. The analysis demonstrates that, for the current study site, some best-intended schemes for the optimal merging of radar and rain gauge data processing did not necessarily lead to clear improvements and, in some respects, caused accuracy degradation in the final products. This behavior by the MPE merging schemes is possibly attributed to the rather poor density of operational rain gauges that need to be available in real time for the implementation of such schemes. Future research is required to examine whether this behavior persists in other regions that may have better coverage and availability of operational rain gauges.