A Machine Learning (ML)-Based Approach to Improve Tropical Cyclone Intensity Prediction of NCMRWF Ensemble Prediction System
Author:
Publisher
Springer Science and Business Media LLC
Subject
Geochemistry and Petrology,Geophysics
Link
https://link.springer.com/content/pdf/10.1007/s00024-022-03206-6.pdf
Reference47 articles.
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3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
4. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78(1), 1–3. https://doi.org/10.1175/1520-0493(1950)078%3c0001:VOFEIT%3e2.0.CO;2
5. Bright, D. R., & Mullen, S. L. (2002). Short-range ensemble forecasts of precipitation during the southwest monsoon. Weather and Forecasting, 17(5), 1080–1100. https://doi.org/10.1175/1520-0434(2002)017%3c1080:SREFOP%3e2.0.CO;2
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