Abstract
The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.
Highlights
With the development of atmospheric detection technology, such as automatic weather stations (AWSs), radar, satellite remote sensing, and GPS, human understanding of the mechanism of weather change and the numerical weather prediction (NWP) model has continuously improved
Combined with previous work [3,4,5,6,7], we propose a regional automatic interpretation forecast system supported by multi-source data to predict the temperature and maximum wind speed of the region in the 24 h and combined machine learning methods to improve the performance of traditional interpretation forecast models
Based on the automatic regional interpretation and forecasting system supported by multi-source data, we propose a multi-source meteorological data processing method based on an accurate and meticulous interpolation of grid data and data regionalization
Summary
With the development of atmospheric detection technology, such as automatic weather stations (AWSs), radar, satellite remote sensing, and GPS, human understanding of the mechanism of weather change and the numerical weather prediction (NWP) model has continuously improved. In the 1980s, meteorological interpretation and forecasting based on atmospheric and oceanic dynamic equations began to develop, among which model output statistics (MOS) was a typical example [3]. Combined with previous work [3,4,5,6,7], we propose a regional automatic interpretation forecast system supported by multi-source data to predict the temperature (maximum and minimum temperature) and maximum wind speed of the region in the 24 h and combined machine learning methods to improve the performance of traditional interpretation forecast models. We explain the multi-source data processing method in the ML-MOS model and the model realization method under different constraints in detail
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