Articles | Volume 15, issue 5
Development and technical paper
16 Mar 2022
Development and technical paper |  | 16 Mar 2022

Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5

Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath

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Cited articles

Abarbanel, H. D., Rozdeba, P. J., and Shirman, S.: Machine learning: Deepest learning as statistical data assimilation problems, Neural Comput., 30, 2025–2055, 2018. a
Ambadan, J. T. and Tang, Y.: Sigma-point Kalman filter data assimilation methods for strongly nonlinear systems, J. Atmos. Sci., 66, 261–285, 2009. a, b
Arcomano, T., Szunyogh, I., Pathak, J., Wikner, A., Hunt, B. R., and Ott, E.: A Machine Learning-Based Global Atmospheric Forecast Model, Geophys. Res. Lett., 47, e2020GL087776,, 2020. a, b, c
Asch, M., Bocquet, M., and Nodet, M.: Data assimilation: methods, algorithms, and applications, SIAM, ISBN 978-1-61197-453-9, 2016. a, b
Bach, E., Mote, S., Krishnamurthy, V., Sharma, A. S., Ghil, M., and Kalnay, E.: Ensemble Oscillation Correction (EnOC): Leveraging oscillatory modes to improve forecasts of chaotic systems, J. Climate, 34, 5673–5686, 2021. a
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.