Quality control of the composite radar quantitative precipitation estimation product for Great Britain

• The GB radar QPE product suffers from underestimation and overestimation errors. • Underestimation errors are more frequent, especially for higher hourly rainfalls. • We propose a new QC framework to address errors in the GB radar QPE. • In comparison to gauge data, the QC reduces RMSE by 29 % and improves CC by 31 %. • The QC retains true extreme rainfalls within the GB radar QPE. High-quality gridded precipitation datasets are essential for climate and flood risk research. Quantitative Precipitation Estimation (QPE) from weather radars can provide high resolution gridded rainfall products. However, even after quality control, these datasets still suffer from underestimation (beam blockage and signal attenuation), and overestimation (radar malfunction, ground clutter, and electronic noise) errors, because most QC methods focus solely on radar reflectivity and lack a systematic and holistic approach. By comparing the radar QPE product for Great Britain (GB) (hourly, 1 km resolution, 2006 ∼ 2018), with hourly rain gauge records (∼1300), we find that radar QPE errors increase with elevation, distance from radar, and rainfall intensity. Radar QPE often underestimates high-intensity rainfalls and fails to detect many high-intensity rainfall events (≥40 mm h −1 ). Underestimation occur at 1.71 times as frequently as overestimation in radar QPE (≥0.2 mm h −1 ). We thus propose a QC framework to detect and correct beam blockages, to identify ‘normal’ and ’suspect’ rainfall fields, and to capture bad rainfalls within the radar QPE. We then use a Gaussian interpolation method to replace these bad rainfalls. Our results reveal that all GB radars suffer from beam blockage. Our novel QC framework reduces the RMSE of radar QPE compared to gauge observations from 0.546 to 0.386 (29 % reduction), increases the correlation coefficient from 0.552 to 0.725 (31 % increase), and retains real extreme rainfalls found in gauge observations. The QC framework requires minimal geographical and meteorological knowledge and can be implemented in various radar network settings, making it applicable for other regions globally.