תמונות העטיפה

Improving 2 m temperature forecasts of numerical weather prediction through a machine learning-based Bayesian model

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הוצא לאור ב:Meteorology and Atmospheric Physics vol. 137, no. 1 (Jan 2025), p. 9
יצא לאור:
Springer Nature B.V.
נושאים:
Weather forecasting
Forecasting data
Accuracy
Artificial intelligence
Forecast accuracy
Socioeconomic aspects
Weather
Temperature
Machine learning
Numerical weather forecasting
Marking and tracking techniques
Systematic errors
Bayesian analysis
Predictions
Root-mean-square errors
Economic development
Medium-range forecasting
Temperature forecasting
Probability theory
Bayesian theory
Environmental
גישה מקוונת:Citation/Abstract
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Resumen:Temperature is a fundamental meteorological factor significantly impacting human life and socio-economic development. This study applies a multi-model fusion technique, integrating three artificial intelligence (AI) methods, to improve temperature forecast accuracy by addressing systematic errors and biases in the European Centre for Medium-Range Weather Forecasts (ECMWF) 2 m temperature predictions for Xiong’an New Area and its upstream regions. Using ECMWF forecast data from January 1, 2018, to December 31, 2021, along with ERA5 reanalysis data, we optimized a Bayesian model averaging (BMA_OP) approach, combining linear regression, LightGBM, and UNet to revise the 2 m temperature forecast. BMA_OP demonstrated improved performance, achieving an overall root-mean-square error (RMSE) of 1.15 °C, an average prediction accuracy of 73% for the ECMWF model, and an accuracy of over 91% for BMA_OP, marking a 24.7% improvement. To further assess generalization, we tested the model using full-year 2022 data, where BMA_OP outperformed the ECMWF model with an RMSE, mean absolute error (MAE), and accuracy of 1.31 °C, 1.03 °C, and 87%, respectively—exceeding the ECMWF model’s results by 16%, 13%, and 6%. These findings confirm the effectiveness of BMA_OP-based multi-model fusion technology for temperature correction.
ISSN:0177-7971
1436-5065
0066-6394
0066-6416
DOI:10.1007/s00703-024-01056-6
Fuente:Science Database