Modeling of Precipitation Prediction Based on Causal Analysis and Machine Learning

• Published in: Atmosphere Vol. 14; no. 9; p. 1396
• Main Authors: Li, Hongchen, Li, Ming
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2023
• Subjects: Accuracy; Algorithms; Artificial intelligence; Atmospheric circulation; Atmospheric models; Atmospheric sciences; Back propagation networks; Back Propagation Neural Network; causal analysis; Causality; Circulation indexes; Climate; Climate change; Climate prediction; Correlation analysis; Decomposition; Deep learning; Drought; Empirical analysis; Empirical Orthogonal Function decomposition; Environmental impact; Flood predictions; Forecasts and trends; Hydrologic data; Ice; Information flow; Learning algorithms; Machine learning; Medium-range forecasting; Model accuracy; Modelling; multi-source precipitation data; Neural networks; Orthogonal functions; Permafrost; Precipitation; Precipitation (Meteorology); Precipitation data; Prediction models; Random Forest; Regression analysis; Statistical methods; Trends; Variables; Water shortages; Weather; Weather forecasting; Weather stations; China; Arctic region
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos14091396

The factors influencing precipitation in western China are quite complex, which increases the difficulty in determining accurate predictors. Hence, this paper models the monthly measured precipitation data from 240 meteorological stations in mainland China and the precipitation data from the European Centre for Medium-Range Weather Forecasts and the National Climate Centre and employs 88 atmospheric circulation indices to develop a precipitation prediction scheme. Specifically, a high-quality grid-point field is created by fusing and revising the precipitation data from multiple sources. This field is combined with the Empirical Orthogonal Function decomposition and the causal information flow. Next, the best predictors are screened through Empirical Orthogonal Function decomposition and causal information flow, and a data-driven precipitation prediction model is established using a Back Propagation Neural Network and a Random Forest algorithm to conduct the 1-month, 3-month, and 6-month precipitation predictions. The results show that: The machine learning-based precipitation prediction model has high accuracy and is generally able to predict the precipitation trend in the western region better. The Random Forest algorithm significantly outperforms the Back Propagation Neural Network algorithm in the prediction of the three starting times, and the prediction ability of both models gradually decreases as the starting time increases. Compared with the 2022 flood season prediction scores of the Institute of Atmospheric Sciences of the Chinese Academy of Sciences, the model improves the prediction of 1-month and 3-month precipitation in the western region and provides a new idea for the short-term climate prediction of precipitation in western China.