Weather forecasting is a typical problem of coupling big data with physical-process models, according to Prof. ZHANG Pingwen, academician of Chinese Academy of Sciences, Director of the National Engineering Laboratory for Big Data Analysis and Application Technology, Director of the Center for Computational Science & Engineering, Peking University.
Weather forecasting is a typical problem of coupling big data with physical-process models, according to Prof. ZHANG Pingwen, academician of Chinese Academy of Sciences, Director of the National Engineering Laboratory for Big Data Analysis and Application Technology, Director of the Center for Computational Science & Engineering, Peking University. Prof. ZHANG is the corresponding author of a collaborated study by Peking University and Institute of Atmospheric Physics, Chinese Academy of Sciences.
Generally speaking, weather forecasting is a largely successful practice in the geosciences and, nowadays, it is inseparable from numerical weather prediction (NWP). However, because the outputs of NWP and observations contain different systematic errors, a "weather consultation" is an indispensable part of the process towards further improving the accuracy of forecasts.
"In fact, the theory-driven physical model and data-driven machine learning are complementary tools. Combining these two approaches, an intelligent weather consultation system can be built to assist the current manual process of weather consultation," says Prof. ZHANG. "One of the challenges linked with this is to build appropriate feature engineering for both types of information to make full use of the data."
To solve these problems, Prof. ZHANG and his team have proposed the "model output machine learning" (MOML) method for simulating weather consultation, and this research has recently been published in Advances in Atmospheric Sciences.
Read more at Institute of Atmospheric Physics, Chinese Academy of Sciences
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