Abstract
In this paper, we propose efficient and practical data-driven methods for weather forecasts. We exploit the information brought by historical weather datasets to build machine-learning-based models. These models are employed to produce numerical forecasts, which can be improved by injecting additional data via data assimilation. Our approaches’ general idea is as follows: given a set of time snapshots of some dynamical system, we group the data by time across multiple days. These groups are employed to build first-order Markovian models that reproduce dynamics from time to time. Our numerical models’ precision can be improved via sequential data assimilation. Experimental tests are performed by using the National-Centers-for-Environmental-Prediction Department-of-Energy Reanalysis II dataset. The results reveal that numerical forecasts can be obtained within reasonable error magnitudes in the \(L_2\) norm sense, and even more, observations can improve forecasts by order of magnitudes, in some cases.
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Acknowledgment
This work was supported by the Applied Math and Computer Science Laboratory (AML-CS) at Universidad del Norte, BAQ, COL.
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Nino-Ruiz, E.D., Acevedo García, F.J. (2021). Data-Driven Methods for Weather Forecast. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_25
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DOI: https://doi.org/10.1007/978-3-030-77970-2_25
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