Sub-hourly resolution quality control of rain-gauge data significantly improves regional sub-daily return level...

This research demonstrates how the use of high-resolution rain-gauge data for quality control (QC) significantly changes extreme rainfall estimates, with implications in scientific, meteorological and engineering applications. Current open QC algorithms only consider data at hourly or daily accumulations. Here we present the first open QC algorithm utilising sub-hourly rain-gauge data from official networks at a national, multi-decade scale. We use data from 1,301 rain-gauges in Great Britain (GB) to develop a threshold-based methodology for sub-hourly QC that can be used to complement existing, freely available hourly QC methods by developing an algorithm for sub-hourly QC that uses monthly thresholds for 1 hr, 15 min and 1 min rainfall totals. We then evaluated the effect of combining these QC procedures on rainfall distributions using graphical and statistical methods, with an emphasis on extreme value analysis. We demonstrate that the additional information in sub-hourly rainfall allows our new QC to remove spuriously large values undetected by existing methods which generate errors in extreme rainfall estimates. This results in statistically significant differences between extreme rainfall estimates for 15 min and 1 hr accumulations, with smaller differences found for 6 and 24 hr totals. We also find that extremes in the distributions of 15 min and 1 hr rainfall accumulations tend to grow more rapidly with return period than for longer accumulation periods. We observe similarities between the shape parameter populations for 15 min and 1 hr rainfall accumulations, suggesting that hourly records may be used to improve shape parameter estimates for extreme sub-hourly rainfall in GB. Sub-hourly QC moderates unrealistically large return level estimates for short-duration rainfall, with beneficial impacts on data required for the design of urban drainage infrastructure and the validation of high-resolution climate models.