An Effective Algorithm of Outlier Correction in SpaceCTime Radar Rainfall Data Based on the Iterative Localized Analysis

The precise correction of outliers within radar rainfall data is crucial for a wide range of applications, including the analysis of extreme rainfall events, hydrological modeling, and the forecasting and warning of flash floods. Despite its significance, the challenge of correcting these outliers has not yet been fully explored, mainly due to the high dimensionality and spatiotemporal intricacies of radar rainfall data. Furthermore, most existing techniques for outlier correction are overly simplistic, revealing limitations when it comes to effectively correcting sporadic outliers. In response, this study has developed a novel approach for detecting and correcting outliers based on radar rainfall statistics at a local spatiotemporal scale. In this approach, an algorithm for detecting outliers based on the simple three-sigma rule in spatiotemporal context and an algorithm for detecting abrupt change between adjacent radar cells in a spatial context, all on the local scale, are iterated to enhance the quality of radar rainfall data progressively and effectively. This correction method resulted in radar rainfall data with the grid cell value closely resembling that of the ground gauge data and the probability distribution. In addition, compared to the existing methods, it demonstrated its ability to selectively remove only the outliers while preserving the integrity of the normal data. What sets this proposed method apart is not only its practicality, as it relies solely on 2-D radar reflectivity data and can be easily implemented, but also its contribution to improving the analysis accuracy across various domains reliant on radar rainfall data.