Model Establishment of Atmospheric Weighted Mean Temperature in Guizhou Regin based on Automatic LightGBM
To address the problems of large topographic fluctuation,small number of radiosonde in Guizhou,and the fact that the existing weighted mean temperature(Tm)model can not describe the spatial difference and diurnal variation of Tm and its vertical variation.This paper combined the advantages of Bayes hyperparameter optimization and LightGBM machine learning method,thus proposed an automatic machine learning modeling method,which took into account the spatial difference,annual cycle,seasonal cycle and diurnal variation of Tm and its vertical variation.A local empirical model of Tm(WNTm model)in Guizhou province without meteorological parameter dependence is constructed by taking 4 ERA5 grid points surrounding Weining Sounding Station as an example.The experimental results show that the WNTm model achieves high fitting accuracy on both training set and verification set.It can not only diagnose the diurnal characteristics of Tm,but also describe the vertical variation trend of Tm well.Compared with the current better GPT3 model,WNTm model can achieve higher prediction accuracy,and the mean absolute error and root-mean-root error are reduced by 14.63%and 20.14%respectively.The research methods and ideas in this paper can provide a new way to further improve the accuracy of Tm.
atmospheric weighted mean temperaturedaily variation characteristicsvertical variationautomatic LightGBM
万方数据
维普
