Improving 2 m temperature forecasts of numerical weather prediction through a machine learning-based Bayesian model
Spremljeno u:
| Izdano u: | Meteorology and Atmospheric Physics vol. 137, no. 1 (Jan 2025), p. 9 |
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| Izdano: |
Springer Nature B.V.
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text - PDF |
| Oznake: |
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MARC
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| 024 | 7 | |a 10.1007/s00703-024-01056-6 |2 doi | |
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| 245 | 1 | |a Improving 2 m temperature forecasts of numerical weather prediction through a machine learning-based Bayesian model | |
| 260 | |b Springer Nature B.V. |c Jan 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Weather forecasting | ||
| 653 | |a Forecasting data | ||
| 653 | |a Accuracy | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Forecast accuracy | ||
| 653 | |a Socioeconomic aspects | ||
| 653 | |a Weather | ||
| 653 | |a Temperature | ||
| 653 | |a Machine learning | ||
| 653 | |a Numerical weather forecasting | ||
| 653 | |a Marking and tracking techniques | ||
| 653 | |a Systematic errors | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Predictions | ||
| 653 | |a Root-mean-square errors | ||
| 653 | |a Economic development | ||
| 653 | |a Medium-range forecasting | ||
| 653 | |a Temperature forecasting | ||
| 653 | |a Probability theory | ||
| 653 | |a Bayesian theory | ||
| 653 | |a Environmental | ||
| 773 | 0 | |t Meteorology and Atmospheric Physics |g vol. 137, no. 1 (Jan 2025), p. 9 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3151472444/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3151472444/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |