Research on Optimization of Quantitative Precipitation Estimation Calibration Method

The optimal interpolation method is widely used in meteorological applications worldwide. However, due to the different precipitation climate characteristics and the spatial distribution of gauges, the implementation of the optimal interpolation method involves many empirical models and key parameters. There is still some uncertainty on how to derive a set of optimised parameters for the application of the optimal interpolation method in a local region. This study analyses and optimises the key parameters of the optimal interpolation method for the calibration of weather radar quantitative precipitation estimation using radar and gauge observations from May to September 2020 in Qingdao, Shandong. The search radius of gauges from a grid point is determined by analysing the spatial distribution of all gauges relative to the analysis point. The optimal number of gauges used to calibrate the precipitation estimation of a grid point is determined by analysing the change of relative analysis error with respect to the number of gauges. Eight groups of sensitivity experiments with different correlation functions are compared by random sampling and cross-validation to find the best set of parameters for the Qingdao radar. The verification of calibration results produced by the best parameters shows that the calibration significantly improves the accuracy of the quantitative precipitation estimation. The median values of MAE, RMAE, and BIAS are 1.5 mm, 1.0, and 0.03 mm respectively, and the CORR is higher than 0.9. Comparing the quantitative precipitation estimation at different levels of precipitation before and after calibration produced by the best parameters shows that the MAE and RMAE of light rain are reduced by 90%, and the CORR is about 0.87. The MAE and RMAE in moderate to heavy rain are decreased by 89%, and the CORR is higher than 0.9. The MAE and RMAE of rainstorm are decreased by more than 83.9%, and the CORR is about 0.77. Based on the verification of a widespread rainfall case on 26 August 2020, the intensity and spatial distribution of the quantitative precipitation estimation after calibration are closer to the gauge observations. The original quantitative precipitation estimation is relatively smooth and lacks small-scale variations. The calibrated results can reflect the characteristics of the local change that is consistent with the observation of gauges. The results of this paper suggest that the optimal interpolation method with optimised local parameters can significantly improve the accuracy of the quantitative precipitation estimation, which has important application value for rainstorm warning and flood disaster prevention.