A Comparative Study of Four Merging Approaches for Regional Precipitation Estimation

Abstract

To identify suitable merging methods to improve regional precipitation estimates using multiple sources of precipitation data, this study applied four different approaches (multiple linear regression (MLR), feedforward neural network (FNN), random forest (RF) and long short-term memory network (LSTM)) to merge four satellite precipitation products and one reanalysis data in the Jiangsu, Zhejiang and Shanghai of China. The pros and cons of the merging approaches are analyzed comprehensively, using correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) as evaluation indexes. Our results show that: (1) All merging approaches can improve the accuracy of precipitation estimations, but only RF and LSTM can improve the daily precipitation event detection capacity. These approaches can significantly reduce errors in moderate precipitation scenarios, but do not effectively improve accuracy in light and heavy precipitation scenarios. (2) MLR was the least expensive computing cost method in our study and performed better than the other three methods when gauge density was low. However, MLR had the worst daily precipitation event detection capacity (CSI = 0.67). (3) FNN performed moderately in most experiments (CC = 0.87, RMSE = 4.65 mm/day, RB = 1.19 %, POD = 0.94, FAR = 0.29, CSI = 0.70). (4) The merged data generated by RF was the most accurate and had the best daily precipitation event detection capacity (CC = 0.87, RMSE = 4.61 mm/day, RB = − 0.33 %, POD = 0.97, FAR = 0.20, CSI = 0.78). RF performed best in moderate precipitation scenarios. However, it performed worse than other methods when gauge density was low. (5) LSTM was the most robust methods and performed best in light precipitation scenarios. The FAR of the LSTM-generated data was the smallest (0.15) among four fusion methods. However, LSTM had the most expensive computing cost and the worst accuracy of the merged data (CC = 0.86, RMSE = 4.68 mm/day, RB = − 9.36 %).

Keywords

• Accuracy improvement
• data merging
• gridded precipitation data
• precipitation estimation
• robustness