Recurrent neural networks for the problem of improving numerical meteorological forecasts

А.Yu. Doroshenko, R.V. Kushnirenko


This paper briefly describes examples of how deep learning can be applied to geoscientific problems, as well as the main difficulties that arise when scientists apply this technique to the problems of meteorological forecasting. This paper aims at comparing the two most popular types of recurrent neural network architectures, namely the long short-term memory network and the gated recurrent unit when they are used to improve 2m temperature forecast results obtained using numerical hydrodynamic methods of meteorological forecasting. An efficiency comparison of architectures of recurrent neural networks was performed using the root-mean-square error. It is shown that all models with gated recurrent units are more efficient than models with long short-term memory. Thus the best architecture of recurrent neural networks for solving the problem of improving numerical meteorological forecasts has been revealed.

Prombles in programming 2023; 4: 90-97


deep learning; recurrent neural networks; meteorological forecasting


Agapiou, A., 2017. Remote sensing heritage in a petabyte-scale: satellite data and heritage Earth Engine© applications. International Journal of Digital Earth, 10(1), pp.85-102. CrossRef

LeCun, Y., Bengio, Y. and Hinton, G., 2015. Deep learning. nature, 521(7553), pp.436-444. CrossRef

Bhimji, W., Farrell, S.A., Kurth, T., Paganini, M., Prabhat and Racah, E., 2018, September. Deep neural networks for physics analysis on low-level whole-detector data at the LHC. In Journal of Physics: Conference Series (Vol. 1085, p. 042034). IOP Publishing. CrossRef

Schütt, K.T., Arbabzadah, F., Chmiela, S., Müller, K.R. and Tkatchenko, A., 2017. Quantum-chemical insights from deep tensor neural networks. Nature communications, 8(1), p.13890. CrossRef

Alipanahi, B., Delong, A., Weirauch, M.T. and Frey, B.J., 2015. Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33(8), pp.831-838. CrossRef

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 preprint arXiv:1605.01156.

Racah, E., Beckham, C., Maharaj, T., Ebrahimi Kahou, S., Prabhat, M. and Pal, C., 2017. Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. Advances in neural information processing systems, 30.

LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), pp.2278-2324. CrossRef

Sak, H., Senior, A. and Beaufays, F., 2014. Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128. CrossRef

Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F. and Fraundorfer, F., 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE geoscience and remote sensing magazine, 5(4), pp.8-36. CrossRef

Oh, J., Guo, X., Lee, H., Lewis, R.L. and Singh, S., 2015. Action-conditional video prediction using deep networks in atari games. Advances in neural information processing systems, 28.

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. Advances in neural information processing systems, 28.

Schultz, M.G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L.H., Mozaffari, A. and Stadtler, S., 2021. Can deep learning beat numerical weather prediction?. Philosophical Transactions of the Royal Society A, 379(2194), p.20200097. CrossRef

Montavon, G., Samek, W. and Müller, K.R., 2018. Methods for interpreting and understanding deep neural networks. Digital signal processing, 73, pp.1-15. CrossRef

Runge, J., Petoukhov, V., Donges, J.F., Hlinka, J., Jajcay, N., Vejmelka, M., Hartman, D., Marwan, N., Paluš, M. and Kurths, J., 2015. Identifying causal gateways and mediators in complex spatio-temporal systems. Nature communications, 6(1), p.8502. CrossRef

Bauer, P., Dueben, P.D., Hoefler, T., Quintino, T., Schulthess, T.C. and Wedi, N.P., 2021. The digital revolution of Earth-system science. Nature Computational Science, 1(2), pp.104-113. CrossRef

Prudden, R., Adams, S., Kangin, D., Robinson, N., Ravuri, S., Mohamed, S. and Arribas, A., 2020. A review of radar-based nowcasting of precipitation and applicable machine learning techniques. arXiv preprint arXiv:2005.04988.

Bonavita, M. and Laloyaux, P., 2020. Machine learning for model error inference and correction. Journal of Advances in Modeling Earth Systems, 12(12), p.e2020MS002232. CrossRef

Krasnopolsky, V.M., Fox-Rabinovitz, M.S. and Chalikov, D.V., 2005. New approach to calculation of atmospheric model physics: Accurate and fast neural network emulation of longwave radiation in a climate model. Monthly Weather Review, 133(5), pp.1370-1383. CrossRef

Rasp, S. and Lerch, S., 2018. Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146(11), pp.3885-3900. CrossRef

Shpyg, V., Budak, I., Pishniak, D. and Poperechnyi, P., 2013, November. The application of regional NWP models to operational weather forecasting in Ukraine. In CAS Technical Conference (TECO) on" Responding to the Environmental Stressors of the 21st Century" Available from: http://www. wmo. int/pages/prog/arep/cas/documents/Ukraine-NWPModels.pdf

Doms, G. and Baldauf, M., 2011. A description of the nonhydrostatic regional COSMO-Model Part I: dynamics and numerics. Deutscher Wetterdienst, Offenbach.

Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780. CrossRef

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. CrossRef

Doroshenko, А.Y., Shpyg, V.M. and Kushnirenko, R.V., 2023. Deeplearning-based approach to improving numerical weather forecasts. PROBLEMS IN PROGRAMMING, (3), pp.91-98. CrossRef

Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.



  • There are currently no refbacks.