Precipitation Forecast Correction in South China Based on SVD and Machine Learning

Precipitation can be induced by various weather systems and a series of complex physical processes,so its prediction is relatively difficult in weather forecasting.Due to the limitation of numerical model,the prediction error is inevitable.It is a hot topic in meteorological research and operation to explore a more effective method to correct the model product,and to improve the interpretation and applicability.To explore a more effective model product error correction method,a combination of correction methods is put forward,based on singular value decomposition（SVD） and machine learning,including multiple linear regression,LASSO regression and Ridge regression.The results are compared with the traditional matrix coefficient method,and then correction models are tested in pre-flood season precipitation forecast in South China,by correcting European Centre for Medium-Range Weather Forecasts（EC） product.The result shows that the proposed correction models combining SVD and machine learning can effectively reduce the error of EC product.The maximum optimization rate root mean square error is 4.2 %,and more than 69 %of the stations are optimized to different degrees.These correction models have better robustness to deal with the problem of collinearity between factors,and the correction effect is better than that of the traditional matrix coefficient method.Furthermore,the weighted integration of multiple correction models is carried out by assigning different weights to different models,and the root mean square error by the integrated approach in South China is smaller than EC product and any single correction model.It shows that the weighted ensemble method can better integrate the advantages of multiple correction models and enlarge the advantages.For the weighted ensemble of multiple correction models,it is not only better than the precipitation prediction results of EC product,but also better than any one of the integrated correction models.Its optimization rate of root mean squared error can achieve 5.7%,and more than 77% of the stations are optimized to different degrees.