Machine Learning Methods in Weather and Climate Applications: A Survey
Source abstract
Abstract
With the rapid development of artificial intelligence, machine learning is gradually becoming popular for predictions in all walks of life. In meteorology, it is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors, including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modeling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrological Forecasting Using AI · Meteorological Phenomena and Simulations · Energy Load and Power Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Commonwealth Scientific and Industrial Research Organisation, Data 61, Sydney, NSW 2015, Australia
Key Laboratory of Meteorological Disaster, Nanjing University of Information Science and Technology, Nanjing 210044, China
References (100+)
Show source-provided references
1. The physical basis of long-range weather10.1175/1520-0493(1901)29[551c:TPBOLW]2.0.CO;2 ↗
2. Urban computing: Concepts, methodologies, and applications
3. Weather forecasting with ensemble methods10.1126/science.1115255 ↗
4. Remote sensing heritage in a petabyte-scale: Satellite data and heritage Earth Engine applications10.1080/17538947.2016.1250829 ↗
5. Bendre, M.R., Thool, R.C., and Thool, V.R. (2015, January 4–5). Big data in precision agriculture: Weather forecasting for future farming. Proceedings of the 2015 1st International Conference on Next Generation Computing Technologies (NGCT), Dehradun, India.10.1109/NGCT.2015.7375220 ↗
6. On-line economic optimization of energy systems using weather forecast information10.1016/j.jprocont.2009.07.004 ↗
7. Nurmi, V., Perrels, A., Nurmi, P., Michaelides, S., Athanasatos, S., and Papadakis, M. (2023, September 08). Economic value of weather forecasts on transportation–Impacts of weather forecast quality developments to the economic effects of severe weather. Available online: http://virtual.vtt.fi/virtual/ewent/Deliverables/D5/D5_2_16_02_2012_revised_final.pdf.
8. The economic impact of weather on the construction industry of the United States10.1175/1520-0477-47.12.967 ↗
9. The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores10.1016/j.jretconser.2019.101921 ↗
10. Tourism climatology: Evaluating environmental information for decision making and business planning in the recreation and tourism sector10.1007/s00484-003-0177-z ↗
11. Smith, K. (2013). Environmental Hazards: Assessing Risk and Reducing Disaster, Routledge.10.4324/9780203805305 ↗
12. Advances in application of climate prediction in agriculture10.1016/S0308-521X(01)00058-0 ↗
13. Welfare consequences of persistent climate prediction errors on insurance markets against natural hazards10.1590/0101-41614922grl ↗
14. A coupled physical and economic model of the response of coastal real estate to climate risk10.1038/nclimate1826 ↗
15. How to make a city climate-proof, addressing the urban heat island effect10.1016/j.resconrec.2011.06.004 ↗
16. Tourism, climate change and adaptation: A review10.1080/13683500.2013.774323 ↗
17. Climate prediction: A limit to adaptation
18. Deep Learning for Multi-Year ENSO Forecasts10.1038/s41586-019-1559-7 ↗
19. Howe, L., and Wain, A. (1993). Predicting the Future, Cambridge University Press.
20. The status and challenge of global fire modelling10.5194/bg-13-3359-2016 ↗
21. ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
22. A nowcasting model for the prediction of typhoon tracks based on a long short term memory neural network10.1007/s13131-018-1219-z ↗
23. Deep Learning-Based Weather Prediction: A Survey10.1016/j.bdr.2020.100178 ↗
24. Deep learning and process understanding for data-driven Earth system science10.1038/s41586-019-0912-1 ↗
25. CMIP6 data citation of evolving data10.5334/dsj-2017-030 ↗
26. Hsieh, W.W. (2009). Machine Learning Methods in the Environmental Sciences: Neural Networks and Kernels, Cambridge University Press.10.1017/CBO9780511627217 ↗
27. New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model10.1175/MWR2923.1 ↗
28. Using ensemble of neural networks to learn stochastic convection parameterizations for climate and numerical weather prediction models from data simulated by a cloud resolving model
29. Use of a neural-network-based long-wave radiative-transfer scheme in the ECMWF atmospheric model
30. Accurate and fast neural network emulations of model radiation for the NCEP coupled climate forecast system: Climate simulations and seasonal predictions10.1175/2009MWR3149.1 ↗
31. Neural network approximations for nonlinear interactions in wind wave spectra: Direct mapping for wind seas in deep water10.1016/j.ocemod.2003.12.008 ↗
32. Markakis, E., Papadopoulos, A., and Perakakis, P. (2018). Spatiotemporal Forecasting: A Survey. arXiv.
33. Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons.
34. He, Y., and Kolovos, A. (2015). Wiley StatsRef: Statistics Reference Online, John Wiley & Sons.
35. Spatiotemporal Analysis of Air Quality and Its Application in LASG/IAP Climate System Model
36. Chatfield, C. (2016). The Analysis of Time Series: An Introduction, CRC Press. [7th ed.].
37. Stull, R. (2015). Meteorology for Scientists and Engineers, Brooks/Cole. [3rd ed.].
38. Machine Learning for Parameterization of Moist Convection in the Community Atmosphere Model
39. Machine Learning for Spatial Environmental Data
40. A Multi-Model Deep Learning Ensemble Method for Rainfall Prediction
41. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks10.1175/1520-0434(1998)013<1194:LPFFAN>2.0.CO;2 ↗
42. Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv.
43. Qiu, M., Zhao, P., Zhang, K., Huang, J., Shi, X., Wang, X., and Chu, W. (2017, January 18–21). A short-term rainfall prediction model using multi-task convolutional neural networks. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.10.1109/ICDM.2017.49 ↗
44. Karevan, Z., and Suykens, J.A. (2018). Spatio-temporal stacked lstm for temperature prediction in weather forecasting. arXiv.
45. Analog Forecasting of extreme-causing weather patterns using deep learning10.1029/2019MS001958 ↗
46. Sønderby, C.K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., and Alchbrenner, N. (2020). MetNet: A Neural Weather Model for Precipitation Forecasting. arXiv.
47. Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., and Anandkumar, A. (2022). FourCastNet: A Global Data-Driven High-Resolution Weather Model Using Adaptive Fourier Neural Operators. arXiv.
48. Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Pritzel, A., and Battaglia, P. (2022). GraphCast: Learning skillful medium-range global weather forecasting. arXiv.
49. Accurate Medium-Range Global Weather Forecasting with 3D Neural Networks10.1038/s41586-023-06185-3 ↗
50. Nguyen, T., Brandstetter, J., Kapoor, A., Gupta, J.K., and Grover, A. (2023). ClimaX: A foundation model for weather and climate. arXiv.
51. Gangopadhyay, S., Clark, M., and Rajagopalan, B. (2005). Water Resources Research, Wiley Online Library.
52. Down-scaling of precipitation for climate change scenarios: A support vector machine approach10.1016/j.jhydrol.2006.04.030 ↗
53. Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction10.1016/j.neunet.2006.01.002 ↗
54. Raje, D., and Mujumdar, P.P. (2009). Water Resources Research, Wiley Online Library.
55. Bias correction of global ensemble precipitation forecasts by Random Forest method10.1007/s12145-021-00577-7 ↗
56. Seasonal Arctic Sea Ice Forecasting with Probabilistic Deep Learning10.1038/s41467-021-25257-4 ↗
57. Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes10.5194/gmd-15-3923-2022 ↗
58. Down-scaling Multi-Model Climate Projection Ensembles with Deep Learning (DeepESD): Contribution to CORDEX EUR-4410.5194/gmd-15-6747-2022 ↗
59. Hess, P., Lange, S., and Boers, N. (2022). Deep Learning for bias-correcting comprehensive high-resolution Earth system models. arXiv.
60. On deep learning-based bias correction and Down-scaling of multiple climate models simulations10.1007/s00382-022-06277-2 ↗
61. Improving Seasonal Forecast Using Probabilistic Deep Learning10.1029/2021MS002766 ↗
62. SwinVRNN: A Data-Driven Ensemble Forecasting Model via Learned Distribution Perturbation10.1029/2022MS003211 ↗
63. Chen, L., Zhong, X., Zhang, F., Cheng, Y., Xu, Y., Qi, Y., and Li, H. (2023). FuXi: A cascade machine learning forecasting system for 15-day global weather forecast. arXiv.10.1038/s41612-023-00512-1 ↗
64. Conditional local convolution for spatio-temporal meteorological forecasting
65. Chen, K., Han, T., Gong, J., Bai, L., Ling, F., Luo, J.J., Chen, X., Ma, L., Zhang, T., and Su, R. (2023). FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead. arXiv.
66. Machine Learning for numerical weather and climate modelling: A review
67. Gradient-based learning applied to document recognition10.1109/5.726791 ↗
68. ImageNet classification with deep convolutional neural networks
69. Scherer, D., Müller, A., and Behnke, S. (2010, January 15–18). Evaluation of pooling operations in convolutional architectures for object recognition. Proceedings of the International Conference on Artificial Neural Networks 2010, Thessaloniki, Greece.10.1007/978-3-642-15825-4_10 ↗
70. Deep learning10.1038/nature14539 ↗
71. Liu, Y., Racah, E., Correa, J., Khosrowshahi, A., Lavers, D., Kunkel, K., Wehner, M., and Collins, W. (2016). Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv.
72. Goodfellow, I., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013, January 16–21). Maxout networks. Proceedings of the International Conference on Machine Learning, Atlanta, GA, USA.
73. Navier-Stokes equations: Theory and approximation
74. Impact of an improved longwave radiation model, RRTM, on the energy budget and thermodynamic properties of the NCAR community climate model, CCM310.1029/2000JD900091 ↗
75. Guo, Y., Shao, C., and Su, A. (2023). Comparative Evaluation of Rainfall Forecasts during the Summer of 2020 over Central East China. Atmosphere, 14.10.20944/preprints202305.1111.v1 ↗
76. Guo, Y., Shao, C., and Su, A. (2023). Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China. Atmosphere, 14.10.20944/preprints202308.1298.v1 ↗
77. The Quiet Revolution of Numerical Weather Prediction10.1038/nature14956 ↗
78. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017, January 4–9). Attention is All You Need. Proceedings of the NeurIPS, Long Beach, CA, USA.
79. Wang, H., Zhu, Y., Green, B., Adam, H., Yuille, A., and Chen, L.C. (2019). Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation. arXiv.10.1007/978-3-030-58548-8_7 ↗
80. A closer look at the ABI on the GOES-R series10.1175/BAMS-D-15-00230.1 ↗
81. Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A. (2021, January 3–7). Fourier Neural Operator for Parametric Partial Differential Equations. Proceedings of the International Conference on Learning Representations (ICLR), Virtual Event.
82. Guibas, J., Mardani, M., Li, Z., Tao, A., Anandkumar, A., and Catanzaro, B. (2022, January 25–29). Adaptive Fourier Neural Operators: Efficient token mixers for transformers. Proceedings of the International Conference on Representation Learning, Virtual Event.
83. Rasp, S., and Thuerey, N. (2020). Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution. arXiv.
84. Weyn, J.A., Durran, D.R., Caruana, R., and Cresswell-Clay, N. (2021). Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models. arXiv.10.1002/essoar.10506235.1 ↗
85. Weatherbench: A benchmark data set for data-driven weather forecasting10.1029/2020MS002203 ↗
86. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11–17). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the International Conference on Computer Vision, Virtual.10.1109/ICCV48922.2021.00986 ↗
87. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv.
88. Single precision in weather forecasting models: An evaluation with the IFS10.1175/MWR-D-16-0228.1 ↗
89. IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.
90. Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S.C., Collins, W., Cox, P., Driouech, F., Emori, S., and Eyring, V. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.
91. Washington, W.M., and Parkinson, C.L. (2005). An Introduction to Three-Dimensional Climate Modeling, University Science Books.
92. Regional Dynamical Down-scaling and the CORDEX Initiative10.1146/annurev-environ-102014-021217 ↗
93. Randall, D.A., Wood, R.A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., and Srinivasan, J. (2007). Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.
94. An overview of CMIP5 and the experiment design10.1175/BAMS-D-11-00094.1 ↗
95. Spatial–temporal ensemble forecasting (STEFS) of high-resolution temperature using machine learning models
96. Mukkavilli, S., Perone, C.S., Rangapuram, S.S., and Müller, K.R. (2020, January 12–18). Distribution regression forests for probabilistic spatio-temporal forecasting. Proceedings of the International Conference on Machine Learning (ICML), Vienna, Austria.
97. Challenges and progress in probabilistic forecasting of convective phenomena: The 2016 GFE/EUMETSAT/NCEP/SPC severe convective weather workshop
98. Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv.
99. Krasting, J.P., John, J.G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., and Zhao, M. (2018). NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP. Earth Syst. Grid Fed., 10.
100. Zhu, J.-Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22–29). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.244 ↗