Radar Quantitative Precipitation Estimation Algorithm Based on Precipitation Classification and Dynamical Z-R Relationship

Abstract

The choice of Z-R relationship is an essential source of error in radar Quantitative Precipitation Estimation (QPE). A QPE algorithm combining the Optimization process of precipitation Classification and Dynamical adjustments (OCD) is proposed to improve the accuracy of QPE in Yinchuan city, China. A detailed evaluation and study of Z = 300R1.4 (fixed Z-R), Optimization Processing (OP), Optimization processing of Dynamical Adjustments (ODA) and OCD were performed using various evaluation metrics. The results show that ODA and OCD can significantly reduce the error of QPE, with OCD being the best estimator, reaching a correlation coefficient (CC) of 0.7 and reducing mean absolute error (MAE) and root mean square error (RMSE) by 31% and 34%, respectively. OCD outperforms other algorithms in terms of MAE and RMSE for different rain rates (RR), and the various assessment metrics at hourly scales are also more concentrated in reasonable intervals. OP gives fair results at weaker rain rates (0.2 ≤ RR < 8 mm/h) but underestimates rainfall more incorrectly at stronger rain rates (8 mm/h ≤ RR). Both the OCD and ODA provide a more significant improvement in the estimation of the area and magnitude of strong rainfall, with the OCD providing a better description of the local characteristics of the rainfall distribution, further demonstrating the advantages of the ODA.