An automatic quality evaluation procedure for third-party daily rainfall observations and its application over Australia

Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible due to variations in quality control applied and there is a need to provide greater confidence in them. In this study, we develop an automatic quality evaluation procedure for daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, these reference data sources are used to provide separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p-value-based confidence score as a measure of quality and the confidence of the third-party data observation. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data.

Automated summary

This study proposes an automatic quality evaluation method for daily rainfall observations collected from third-party stations. The method uses reference data sources to provide separate tests and issues a p-value-based confidence score as a measure of quality. The method has been validated with synthetic and real datasets, achieving high accuracy in identifying erroneous data.