Abstract
Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.
Highlights
Numeric weather prediction has achieved good results through the accumulation of scientific knowledge, greatly improved computing power, and continuous improvement of the observation system [1]
Traditional post-processing methods are based on pre-statistical assumptions, which limits the methods to further improve the effect of model correction to a certain extent
root mean square error (RMSE), MAE, R2 and forecast accuracy were used as the evaluation metrics
Summary
Numeric weather prediction has achieved good results through the accumulation of scientific knowledge, greatly improved computing power, and continuous improvement of the observation system [1]. Glahn et al [7] proposed a Model Output Statistical (MOS) method, which became the most commonly used post-processing method It was used by Taylor et al to assess temperature forecast error [8]; the spatial and temporal patterns of U.S nationwide MOS forecast errors compared to individual station error trends prove to be a powerful forecasting tool for real-time forecasters, and nationwide MOS forecast error maps are useful. Combined with the field of grid analysis, Guan et al [9] applied the Kalman filter-based decayed mean deviation estimation method to effectively improve the accuracy of temperature prediction for most of the year, with the greatest benefit from April to June, but sometimes it does not perform well when the spring and fall transition seasons are longer. Traditional post-processing methods are based on pre-statistical assumptions, which limits the methods to further improve the effect of model correction to a certain extent
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