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http://dx.doi.org/10.14191/Atmos.2021.31.1.073

Development of Surface Weather Forecast Model by using LSTM Machine Learning Method  

Hong, Sungjae (Department of Atmospheric Sciences, Pusan National University)
Kim, Jae Hwan (Department of Atmospheric Sciences, Pusan National University)
Choi, Dae Sung (Department of Atmospheric Sciences, Pusan National University)
Baek, Kanghyun (Research Center for Climate Sciences, Pusan National University)
Publication Information
Atmosphere / v.31, no.1, 2021 , pp. 73-83 More about this Journal
Abstract
Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.
Keywords
Weather forecast; deep learning; RNN; LSTM;
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1 Gal, Y., and Z. Ghahramani, 2016: A theoretically grounded application of dropout in recurrent neural network. Preprints, 30th Conference on Neural Information Processing Systems, 14 pp [Available online at https://arxiv.org/abs/1512.05287].
2 Hewage, P., M. Trovati, E. Pereira, and A. Behera, 2021: Deep learning-based effective fine-grained weather forecasting model. Pattern Anal. Applic., 24, 343-366, doi: 10.1007/s10044-020-00898-1.   DOI
3 Holton, J., and G. Hakim, 2012: An Introduction to Dynamic Meteorology, 5th edition. Academic Press, 552 pp.
4 Hochreiter, S., and J. Schmidhuber, 1997: Long short-term memory. Neural Comput., 9, 1735-1780.   DOI
5 Hong, S.-Y., and M. Kanamitsu, 2014: Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations. Asia-Pac. J. Atmos. Sci., 50, 83-104, doi:10.1007/s13143-014-0029-2.   DOI
6 Jozefowicz, R., W. Zaremba, and I. Sutskever, 2015: An empirical exploration of recurrent network architectures. Proc., The 32nd International conference on machine learning, 37, ICML, 2342-2350, doi:10.5555/3045118.3045367.
7 Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, 2014: Dropout: a simple way to prevent neural networks from overfitting. J. Machine Learn. Res., 15, 1929-1958.
8 Tieleman, T., and G. Hinton, 2012: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 4, 26-31.
9 Wang, H., and B. Raj, 2017: On the origin of deep learning. Preprint, arXiv, 72 pp [Available online at https://arxiv.org/abs/1702.07800].
10 Yang, B., S. Sun, J. Li, X. Lin, and Y. Tian, 2019: Traffic flow prediction using LSTM with feature enhancement. Neurocomputing, 332, 320-327, doi:10.1016/j.neucom.2018.12.016.   DOI
11 Zhang, Q., H. Wang, J. Dong, G. Zhong, and X. Sun, 2017: Prediction of sea surface temperature using long short-term memory. IEEE Geosci. Remote S., 14, 1745-1749, doi:10.1109/LGRS.2017.2733548.   DOI
12 Zhang, T., S. Song, S. Li, L. Ma, S. Pan, and L. Han, 2019: Research on gas concentration prediction models based on LSTM multidimensional time series. Energies, 12, 161, doi:10.3390/en12010161.   DOI
13 Chen, J., J. Yu, M. Song, and V. Valdmanis, 2019: Factor decomposition and prediction of solar energy consumption in the United States. J. Clean. Prod., 234, 1210-1220, doi:10.1016/j.jclepro.2019.06.173.   DOI
14 Majhi, B., D. Naidu, A. P. Mishra, and S. C. Satapathy, 2020: Improved prediction of daily pan evaporation using Deep-LSTM model. Neural Comput. Applic., 32, 7823-7838, doi:10.1007/s00521-019-04127-7.   DOI
15 Kiperwasser, E., and Y. Goldberg, 2016: Simple and accurate dependency parsing using bidirectional LSTM feature representations. T. Assoc. Comput. Linguist., 4, 313-327, doi:10.1162/tacl_a_00101.   DOI
16 KMA, 2020: Validation of Numerical Prediction System (2019). Tech. Rep., Numerical Modeling Center, Korea Meteorological Administration, 11-1360709-000001-10, 297 pp (in Korean).
17 Lea, C., M. D. Flynn, R. Vidal, A. Reiter, and G. D. Hager, 2017: Temporal convolutional networks for action segmentation and detection. Proc., 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Institute of Electrical and Electronics Engineers Inc., 1003-1012, doi:10.1109/CVPR.2017.113.   DOI
18 Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986: Learning representations by back-propagating errors. Nature, 323, 533-536.   DOI
19 Farzad, A., H. Mashayekhi, and H. Hassanpour, 2019: A comparative performance analysis of different activation functions in LSTM networks for classification. Neural Comput. Applic., 31, 2507-2521, doi:10.1007/s00521-017-3210-6.   DOI
20 Feng, C., M. Cui, B.-M. Hodge, and J. Zhang, 2017: A data-driven multi-model methodology with deep feature selection for short-term wind forecasting. Appl. Energ., 190, 1245-1257, doi:10.1016/j.apenergy.2017.01.043.   DOI
21 Poornima, S., and M. Pushpalatha, 2019: Prediction of rainfall using intensified LSTM based recurrent neural network with weighted linear units. Atmosphere, 10, 668, doi:10.3390/atmos10110668.   DOI