Developing a relative quality index scheme for improving radar composite products and bias adjustment in mountainous regions, Thailand

Radar composite products are essential for tracking and forecasting heavy storms over mountainous catchments where rain gauge information is scarce. The impact of radar beam blockage from an individual radar, resulting in low reflectivity data, can significantly contribute to the underestimation of radar rainfall estimates in such areas. The quality of rain radar composites is critical as these products will be used for near real-time forecasting of hydrometeorological hazards. This study aimed to develop a relative quality index scheme based on the radar reflectivity fraction of the compositing radars to improve the accuracy of heavy rainfall estimates in (partly) blocked areas. Three additional surrounding environmental quality indices, i.e., the distance to the radar station, the height of the beam above the ground, and the radar beam blockage fraction were integrated in the overall quality indices (QI) computation. Furthermore, we expanded the use of the QI to enhance the mean field bias adjustment in tracking high-intensity rainfall. To comprehensively assess the merits and drawbacks of the compositing methods with multiple quality indices, we compared our results with conventional and well-known maximum composite techniques. We have tested this scheme in the Khao Yai National Park, Lamtakong basin, and the surrounding areas. Two rain radar stations were selected: Sattahip, 220 kilometer southwest and Phimai, 140 kilometer North of the Lamtakong basin. Automatic rain gauges in the overlapping area were used to evaluate the radar composite product during storm events in 2020 and 2022. The results indicate that radar composite approach with multiple QIs can effectively identify areas with unreliable radar measurement. The radar reflectivity fraction was the most important quality index in the composite region, especially in the beam blockage area where the reflectivity from Phimai consistently registers lower values compared to that from the Sattahip radar. Combining this novel relative QI scheme with traditional quality indices (distance, height, and beam blockage fraction) increased the overall accuracy and reliability of heavy radar rainfall estimates. While the combined QIs and the maximum composite method resulted in composite products with similar overall accuracy, the proposed new QI method provides more coherent storm structure. Furthermore, a noteworthy finding is that heavy rainstorms in obstructed areas become visibly apparent with higher accuracy when applying thresholds to the quality index values for bias adjustment computation of the composite products. These final products of radar rainfall estimates represent a critical advancement of rain radar based Early Warning Systems for hydrometeorological hazard mitigation in mountainous regions.