Affiliation:
1. a School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China
2. b Institute of Urban Studies, Shanghai Normal University, Shanghai 200234, China
Abstract
Abstract
Multi-source data-fusion approaches have been developed for estimating regional precipitation. However, studies considering the specific upper limits of the improved gridded rainfall data for different fusion approaches are limited. Here, the potential ranges of accuracy improvement for satellite and reanalysis rainfall products were addressed using various machine learning fusion approaches, including multivariate linear regression (MLR), feedforward neural network (FNN), random forest (RF), and long short-term memory (LSTM), over the Chinese mainland. All four fusion methods reduce errors in the original precipitation products. The upper limits of accuracy improvement in terms of correlation coefficient (CC) and root mean square error (RMSE) were 30.65 and 15.27%, respectively. M-RF showed the best average CC (0.828) and RMSE (4.62 mm/day) in the four seasons. LSTM performed the best under light rainfall events, whereas MLR and RF exhibited better performance under moderate and heavy rainfall events, respectively. Overall, these results serve as a basis for the fusion approach and technique selection, based on the comprehensive validation in different climate zones, altitudes, and seasons over the Chinese mainland.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Subject
Atmospheric Science,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering,Water Science and Technology