Abstract
Spatiotemporal prediction on climate data is aiming to predict future spatial data by learning from prior spatial sequence data. In this paper, we are interested in a prediction of upper tropospheric circulations over the Northern Hemisphere by predicting a geopotential height at 300 hPa (Z300) variable. We proposed a predictive model by constructing an architecture with convolutional layers and deconvolutional layers and applied to convolutional long short-term memory (ConvLSTM) network. The results show that our model obtained root mean square error (RMSE) of 77.36 m (0.84% comparing to average Z300 value) in short-term prediction. While, a convolutional neural network (CNN) and a linear regression (LR) model obtained RMSE of 109.35 (1.19%) and 153.61 (1.67%), respectively. The ConvLSTM maintains RMSE even in long-term prediction. Furthermore, the prediction features’ investigation result shows that temperature at 300 hPa (T300) and self prior Z300 features are important for Z300 prediction.
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References
Chen, P., Niu, A., Liu, D., Jiang, W., Ma, B.: Time series forecasting of temperatures using SARIMA: an example from Nanjing. In: IOP Conference Series: Materials Science and Engineering (2018)
Giffard-Roisin, S., Yang, M., Charpiat, G., Ke\(^{\prime }\)gl, B., Monteleoni, C.: Fused deep learning for hurricane track forecast from reanalysis data. In: Proceedings of International Workshop on Climate Informatics, pp. 69–72 (2018)
Graves, A.: Generating sequences with recurrent neural networks. arXiv:1308.0850 (2013)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Hong, S., Kim, S., Joh, M., Kwang Song, S.: GlobeNet: convolutional neural networks for typhoon eye tracking from remote sensing imagery. In: Proceedings of International Workshop on Climate Informatics, pp. 69–72 (2017)
Kim, S., Hong, S., Joh, M., Kwang Song, S.: DeepRain: ConvLSTM network for precipitation prediction using multichannel radar data. In: Proceedings of International Workshop on Climate Informatics, pp. 89–92 (2017)
Kim, S., Yoon, S., Lee, J., Kahou, S., Kim, H., Kashinath, K., Prabhat, M.: Deep-Hurricane-Tracker: tracking extreme climate events. In: Proceedings of International Workshop on Climate Informatics, pp. 16–18 (2018)
Kolstad, E.W., Breiteiga, T., Scaife, A.A.: The association between stratospheric weak polar vortex events and cold air outbreaks in the northern hemisphere. Q. J. R. Meteorol. Soc. 136, 886–893 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems, pp. 1097–1105 (2012)
Linkin, M.E., Nigam, S.: The North Pacific oscillation-West Pacific teleconnection pattern: mature-phase structure and winter impacts. J. Clim. 21, 1979–1997 (2008)
Papacharalampous, G.A., Tyralis, H., Koutsoyiannis, D.: Predictability of monthly temperature and precipitation using automatic time series forecasting methods. Acta Geophysica 66(4), 807–831 (2018)
Qing, C., Xiaoli, Z., Kun, Z.: Research on precipitation prediction based on time series model. In: Computer Distributed Control and Intelligent Environmental Monitoring, pp. 568–571 (2012)
Rao, J., Ren, R., Chen, H., Yu, Y., Zhou, Y.: The stratospheric sudden warming event in February 2018 and its prediction by a climate system model. J. Geophys. Res. Atmos. 123, 332–345 (2018)
Shi, X., Chen, Z., Wang, H., Yeung, D.Y.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Neural Inf. Process. Syst. 1, 802–810 (2015)
Thompson, D.W.J., Wallace, J.M.: The arctic oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett. 25, 1297–1300 (1998)
Tomassini, L., Gerber, E.P., Baldwin, M.P., Bunzel, F., Giorgetta, M.: The role of stratosphere-troposphere coupling in the occurrence of extreme winter cold spells over northern europe. J. Adv. Model. Earth Syst. 4, 1–14 (2012)
Tomita, T., Yamaura, T.: A precursor of the monthly-mean large-scale atmospheric circulation anomalies over the North Pacific. SOLA 13, 85–89 (2017)
Wang, Y., Gao, Z., Long, M., Wang, J., Yu, P.S.: PredRNN++: towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In: Proceedings of International Conference on Machine Learning, pp. 5123–5132 (2018)
Wang, Y., Long, M., Wang, J., Gao, Z., Yu, P.S.: PredRNN: recurrent neural networks for predictive learning using spatiotemporal LSTMs. In: Neural Information Processing Systems, pp. 879–888 (2017)
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This work was supported in part by the Network Joint Research Center for Materials and Devices and by JSPS KAKENHI Grant Number 19K22876.
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Phermphoonphiphat, E., Tomita, T., Numao, M., Fukui, Ki. (2020). A Study of Upper Tropospheric Circulations over the Northern Hemisphere Prediction Using Multivariate Features by ConvLSTM. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_12
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