Machine Learning Methods for Weather Forecasting: A Survey
Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/1/82 |
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| author | Huijun Zhang Yaxin Liu Chongyu Zhang Ningyun Li |
| author_facet | Huijun Zhang Yaxin Liu Chongyu Zhang Ningyun Li |
| author_sort | Huijun Zhang |
| collection | DOAJ |
| description | Weather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results. |
| format | Article |
| id | doaj-art-7930673818284ecfa2ad83102284860a |
| institution | Kabale University |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-7930673818284ecfa2ad83102284860a2025-01-24T13:21:57ZengMDPI AGAtmosphere2073-44332025-01-011618210.3390/atmos16010082Machine Learning Methods for Weather Forecasting: A SurveyHuijun Zhang0Yaxin Liu1Chongyu Zhang2Ningyun Li3China Huaneng Clean Energy Research Institute, Beijing 102209, ChinaChina Huaneng Clean Energy Research Institute, Beijing 102209, ChinaChina Huaneng Clean Energy Research Institute, Beijing 102209, ChinaBeijing Big Data Center, No. 3 Courtyard, Liuzhuang Road, Tongzhou District, Beijing 101117, ChinaWeather forecasting, a vital task for agriculture, transportation, energy, etc., has evolved significantly over the years. Comprehensive surveys play a crucial role in synthesizing knowledge, identifying trends, and addressing emerging challenges in this dynamic field. In this survey, we critically examines machine learning (ML)-based weather forecasting methods, which demonstrate exceptional capability in handling complex, high-dimensional datasets and leveraging large volumes of historical and real-time data, enabling the identification of subtle patterns and relationships among weather variables. Research on specific tasks such as global weather forecasting, downscaling, extreme weather prediction, and how to combine machine learning methods with physical principles are very active in the current field. However, several unresolved or challenging issues remain, including the interpretability of models and the ability to predict rare weather events. By identifying these gaps, this research provides a roadmap for advancing machine learning-based weather forecasting techniques to complement and enhance weather prediction results.https://www.mdpi.com/2073-4433/16/1/82machine learningweather forecastingdeep learningsurvey |
| spellingShingle | Huijun Zhang Yaxin Liu Chongyu Zhang Ningyun Li Machine Learning Methods for Weather Forecasting: A Survey Atmosphere machine learning weather forecasting deep learning survey |
| title | Machine Learning Methods for Weather Forecasting: A Survey |
| title_full | Machine Learning Methods for Weather Forecasting: A Survey |
| title_fullStr | Machine Learning Methods for Weather Forecasting: A Survey |
| title_full_unstemmed | Machine Learning Methods for Weather Forecasting: A Survey |
| title_short | Machine Learning Methods for Weather Forecasting: A Survey |
| title_sort | machine learning methods for weather forecasting a survey |
| topic | machine learning weather forecasting deep learning survey |
| url | https://www.mdpi.com/2073-4433/16/1/82 |
| work_keys_str_mv | AT huijunzhang machinelearningmethodsforweatherforecastingasurvey AT yaxinliu machinelearningmethodsforweatherforecastingasurvey AT chongyuzhang machinelearningmethodsforweatherforecastingasurvey AT ningyunli machinelearningmethodsforweatherforecastingasurvey |