TITLE:
Application of Machine-Learning-Based Objective Correction Method in the Intelligent Grid Maximum and Minimum Temperature Predictions
AUTHORS:
Jing Liu, Chuan Ren, Ningle Yuan, Shuai Zhang, Yue Wang
KEYWORDS:
Machine Learning, Sliding Training, Forecast Correction, Maximum and Minimum Temperature
JOURNAL NAME:
Atmospheric and Climate Sciences,
Vol.13 No.4,
October
13,
2023
ABSTRACT: Post-processing correction is an effective way to
improve the model forecasting result. Especially, the machine learning methods have
played increasingly important roles in recent years. Taking the meteorological
observational data in a period of two years as the reference, the maximum and
minimum temperature predictions of Shenyang station from the European Center
for Medium-Range Weather Forecasts (ECMWF) and national intelligent grid
forecasts are objectively corrected by using wavelet analysis, sliding training
and other technologies. The evaluation results show that the sliding training
time window of the maximum temperature is smaller than that of the minimum
temperature, and their difference is the largest in August, with a difference
of 2.6 days. The objective correction product of maximum temperature shows a
good performance in spring, while that of minimum temperature performs well throughout
the whole year, with an accuracy improvement of 97% to 186%. The correction
effect in the central plains is better than in the regions with complex
terrain. As for the national intelligent grid forecasts, the objective
correction products have shown positive skills in predicting the maximum
temperatures in spring (the skill-score reaches 0.59) and in predicting the
minimum temperature at most times of the year (the skill-score reaches 0.68).