Abstract
Flexible fine-grained weather forecasting is a problem of national importance due to its stark impacts on economic development and human livelihoods. It remains challenging for such forecasting, given the limitation of currently employed statistical models, that usually involve the complex simulation governed by atmosphere physical equations. To address such a challenge, we develop a deep learning-based prediction model, called Micro-Macro, aiming to precisely forecast weather conditions in the fine temporal resolution (i.e., multiple consecutive short time horizons) based on both the atmospheric numerical output of WRF-HRRR (the weather research and forecasting model with high-resolution rapid refresh) and the ground observation of Mesonet stations. It includes: 1) an Encoder which leverages a set of LSTM units to process the past measurements sequentially in the temporal domain, arriving at a final dense vector that can capture the sequential temporal patterns; 2) a Periodical Mapper which is designed to extract the periodical patterns from past measurements; and 3) a Decoder which employs multiple LSTM units sequentially to forecast a set of weather parameters in the next few short time horizons. Our solution permits temporal scaling in weather parameter predictions flexibly, yielding precise weather forecasting in desirable temporal resolutions. It resorts to a number of Micro-Macro model instances, called modelets, one for each weather parameter per Mesonet station site, to collectively predict a target region precisely. Extensive experiments are conducted to forecast four important weather parameters at two Mesonet station sites. The results exhibit that our Micro-Macro model can achieve high prediction accuracy, outperforming almost all compared counterparts on four parameters of interest.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mesonet. https://www.mesonet.org/ (2021)
South Alabama Mesonet. http://chiliweb.southalabama.edu/ (2021)
WRF Resources. http://home.chpc.utah.edu/~u0553130/Brian_Blaylock/wrf.html (2021)
Alemany, S., Beltran, J., Perez, A., Ganzfried, S.: Predicting hurricane trajectories using a recurrent neural network. In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 33, pp. 468–475 (2019)
Arshi, S., Zhang, L., Strachan, B.: Weather based photovoltaic energy generation prediction using lstm networks. In: Proceedings of International Joint Conference on Neural Networks (IJCNN) (2019)
Bloomfield, P., Hurd, H.L., Lund, R.B.: Periodic correlation in stratospheric ozone data. J. Time Ser. Anal. 15(2), 127–150 (1994)
Bordes, A., Chopra, S., Weston, J.: Question answering with subgraph embeddings. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 615–620 (2014)
Botev, Z.I., Grotowski, J.F., Kroese, D.P., et al.: Kernel density estimation via diffusion. Ann. Stat. 38(5), 2916–2957 (2010)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734 (2014)
Dalto, M., Matuško, J., Vašak, M.: Deep neural networks for ultra-short-term wind forecasting. In: Proceedings of IEEE International Conference on Industrial Technology (ICIT), pp. 1657–1663 (2015)
Dueben, P.D., Bauer, P.: Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Dev. 11(10), 3999–4009 (2018)
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting-an approach using autoencoder and lstm neural networks. In: Proceedings of International Conference on Systems, Man, and Cybernetics (SMC), pp. 2858–2865 (2016)
Grover, A., Kapoor, A., Horvitz, E.: A deep hybrid model for weather forecasting. In: Proceedings of 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 379–386 (2015)
Hernández, E., Sanchez-Anguix, V., Julian, V., Palanca, J., Duque, N.: Rainfall prediction: a deep learning approach. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds.) HAIS 2016. LNCS (LNAI), vol. 9648, pp. 151–162. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32034-2_13
Qinghua, H., Zhang, R., Zhou, Y.: Transfer learning for short-term wind speed prediction with deep neural networks. Renewable Energy 85, 83–95 (2016)
National Center for Atmospheric Research. Understanding the earth system (2019)
Oppenheim, A.V., Buck, J.R., Schafer, R.W.: Discrete-Time Signal Processing, vol. 2, Prentice Hall, Upper Saddle River (2001)
Pan, B., Hsu, K., AghaKouchak, A., Sorooshian, S.: Improving precipitation estimation using vonvolutional neural network. Water Resour. Res. 55(3), 2301–2321 (2019)
Pandit, R.K., Kolios, A., Infield, D.: Data-driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance. IET Renew. Power Gener. 14(13), 2386–2394 (2020)
Radhika, Y., Shashi, M.: Atmospheric temperature prediction using support vector machines. Int. J. Comput. Theor. Eng. 1(1), 55 (2009)
Scher, S.: Toward data-driven weather and climate forecasting: approximating a simple general circulation model with deep learning. Geophys. Res. Lett. 45(22), 12–616 (2018)
Vlachas, P.R., Byeon, W., Wan, Z.Y., Sapsis, T.P., Koumoutsakos, P.: Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks. Proc. Royal Soc. Math. Phys. Eng. Sci. 474(2213), 20170844 (2018)
Wang, B., et al.: Deep uncertainty quantification: A machine learning approach for weather forecasting. In: Proceedings of 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2087–2095 (2019)
Weinstein, S., Ebert, P.: Data transmission by frequency-division multiplexing using the discrete fourier transform. IEEE Trans. Commun. Technol. 19(5), 628–634 (1971)
Weyn, J.A., Durran, D.R., Caruana, R.: Can machines learn to predict weather? using deep learning to predict gridded 500-hpa geopotential height from historical weather data. J. Adv. Model. Earth Syst. 11(8), 2680–2693 (2019)
Weyn, J.A., Durran, D.R., Caruana, R.: Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere. J. Adv. Model. Earth Syst. 12(9), e2020MS002109 (2020)
Xingjian, S.H.I., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Proceedings of Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Yi, X., Zhang, J., Wang, Z., Li, T., Zheng, Y.: Deep distributed fusion network for air quality prediction. In: Proceedings of 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 965–973 (2018)
Acknowledgement
This work was supported in part by NSF under Grants 1763620, 1948374, and 2019511. Any opinion and findings expressed in the paper are those of the authors and do not necessarily reflect the view of funding agency.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y. et al. (2021). Precise Weather Parameter Predictions for Target Regions via Neural Networks. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-86517-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86516-0
Online ISBN: 978-3-030-86517-7
eBook Packages: Computer ScienceComputer Science (R0)
