Quality control of a global hourly rainfall dataset
Year: 2021
Type: article
Abstract: Sub-daily rainfall observations are vital to help us understand, model and adapt to changing climate extremes. However, gauge records often have quality issues, for example due to equipment malfunctions and recording errors. This paper presents a new, open-source quality control algorithm (GSDR-QC) to identify these issues in hourly rainfall data, along with an application of the algorithm to the Global Sub-Daily Rainfall (GSDR) observational dataset. The algorithm is based on 25 quality checks, which are combined into a simple rule base to remove suspicious data. The quality checks and rule base are adaptable to help incorporate local or regional information. Comparison with manually quality-controlled gauge data shows that the procedure results in an overall improvement to the quality of the GSDR dataset. A UK case study further demonstrates the performance of the GSDR-QC procedure, while showing how region-specific data and understanding can be incorporated into the quality control process.
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,100
Awards: G5497712825, FUTURE-STORMS: Quantifying uncertainties and identifying drivers of future changes in weather extremes from convection-permitting model ensembles, G7174306828, PYRAMID: Platform for dYnamic, hyper-resolution, near-real time flood Risk AssessMent Integrating repurposed and novel Data sources
OpenAlex ID: W3196036586