Merging Weather Surveillance Radar Precipitation Estimates From Different Sources: A Quality-Index Approach

Abstract

Weather surveillance radar (WSR) provide distributed quantitative precipitation estimates (QPEs) of great value to the modelling, understanding and management of many hydro-meteorological processes. To obtain these observations over regional or larger scale domains it is necessary to composite data from multiple WSRs. These composites are often produced operationally by national or international meteorological agencies yet valuable data from ad-hoc sources such as research groups and local-level WSR operators are not included in these products. This study presents a methodology for incorporating data from a research radar deployment (the National Centre for Atmospheric Science mobile X-band weather radar, NXPol-1) into a national scale composite (the UK Met Office British Isles gridded composite) using a quality-index. Firstly a quality-index is developed for NXPol-1 using an intuitive, multi-factor approach. The quality-index is then cross-referenced with the existing quality-index for the national composite, to allow production of a dynamically merged two source WSR QPE. The method developed is then evaluated using surface precipitation measurements from an extensive rain gauge network. Merging QPE from the two sources using a quality-index improves the accuracy of WSR QPE when compared to either individual data source, showing it is possible to combine ad-hoc WSR data with national products dynamically such that precipitation estimation is improved. Improving local QPE using additional radar deployments will benefit flood forecasting accuracy and local incident response, particularly when that data is used to enhance existing coverage.