典型文献
A station-data-based model residual machine learning method for fine-grained meteorological grid prediction
文献摘要：
Fine-grained weather forecasting data,i.e.,the grid data with high-resolution,have attracted increasing attention in recent years,especially for some specific applications such as the Winter Olympic Games.Although European Centre for Medium-Range Weather Forecasts (ECMWF) provides grid prediction up to 240 hours,the coarse data are unable to meet high requirements of these major events.In this paper,we propose a method,called model residual machine learning (MRML),to generate grid prediction with high-resolution based on high-precision stations forecasting.MRML applies model output machine learning (MOML) for stations forecasting.Subsequently,MRML utilizes these forecasts to improve the quality of the grid data by fitting a machine learning (ML)model to the residuals.We demonstrate that MRML achieves high capability at diverse meteorological elements,specifically,temperature,relative humidity,and wind speed.In addition,MRML could be easily extended to other post-processing methods by invoking different techniques.In our experiments,MRML outperforms the traditional downscaling methods such as piecewise linear interpolation (PLI) on the testing data.
文献关键词：
中图分类号：
作者姓名：
Chuansai ZHOU;Haochen LI;Chen YU;Jiangjiang XIA;Pingwen ZHANG
作者机构：
School of Mathematical Sciences,Peking University,Beijing 100871,China;School of Science,Beijing University of Posts and Telecommunications,Beijing 100876,China;Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China
文献出处：
引用格式：
[1]Chuansai ZHOU;Haochen LI;Chen YU;Jiangjiang XIA;Pingwen ZHANG-.A station-data-based model residual machine learning method for fine-grained meteorological grid prediction)[J].应用数学和力学（英文版）,2022(02):155-166
A类：
MRML,MOML
B类：
data,model,machine,learning,fine,grained,meteorological,grid,prediction,Fine,weather,forecasting,high,resolution,have,attracted,increasing,attention,recent,years,especially,some,applications,such,Winter,Olympic,Games,Although,European,Centre,Medium,Range,Weather,Forecasts,ECMWF,provides,up,hours,coarse,are,unable,meet,requirements,these,major,events,In,this,paper,propose,called,generate,precision,stations,applies,output,Subsequently,utilizes,forecasts,improve,quality,by,fitting,residuals,demonstrate,that,achieves,capability,diverse,elements,specifically,temperature,relative,humidity,wind,speed,addition,could,be,easily,extended,other,post,processing,methods,invoking,different,techniques,experiments,outperforms,traditional,downscaling,piecewise,linear,interpolation,PLI,testing
AB值：
0.582152
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