Abstract
Surface Air temperature prediction has been a long-standing challenge in the field of weather forecasting due to the number of variables that can influence the surface temperature of any area. In this paper, we aim to use convolutional Long Short-Term Memory (LSTM) Networks to create an accurate and reliable global surface air temperature model. LSTMs are a variation of recurrent memory networks that are able to learn long-term relationships and patterns in data with the use of dedicated recurrent gates. Since the data is provided to us in the form of spatiotemporal grid sequences, we use a convolutional LSTM layer in order to model the temporal and spatial relations. The model attempts to predict the next value of the surface air temperature for an area based on the historical grids given to it.
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NOAA/NCEP (2017) Global Forecast System (GFS) Atmospheric Model (2017)
Sharma A, Manoria M (2006) A weather forecasting system using concept of soft computing: A new approach. In: 2006 International conference on advanced computer communications, pp 353–356. https://doi.org/10.1109/ADCOM.2006.4289915
Nikam VB, Meshram B (2013) Modeling rainfall prediction using data mining method: a Bayesian approach. In: 2013 2013 fifth international conference on computational intelligence, modelling and simulation, pp 132–136. https://doi.org/10.1109/CIMSim.2013.29, http://ieeexplore.ieee.org/document/6663175/
Anandharajan T, Hariharan GA, Vignajeth KK, Jijendiran R, (2016) Weather monitoring using artificial intelligence. In: 2016 2nd international conference on computational intelligence and networks (CINE), pp 106–11. https://doi.org/10.1109/CINE.2016.26, http://ieeexplore.ieee.org/document/7556813/
Salman AG, Kanigoro B, Heryadi Y (2015) Weather forecasting using deep learning techniques. In: 2015 international conference on advanced computer science and information systems (ICACSIS), pp 281–285. https://doi.org/10.1109/ICACSIS.2015.7415154, http://ieeexplore.ieee.org/document/7415154/
Zhang Q, Wang H, Dong J, Zhong G, Sun X (2017) Prediction of sea surface temperature using long short-term memory. IEEE Geosci Remote Sens Lett 14(10):1745–1749. https://doi.org/10.1109/LGRS.2017.2733548
You J, Li X, Low M, Lobell D, Ermon S (2017) Deep Gaussian process for crop yield prediction based on remote sensing data. Association for the advancement of artificial intelligence, pp 4559–4565
Kalnay AE (1996) The NCEP/NCAR 40-year reanalysis project. Bull Amer Meteor Soc
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Wagle, S., Uttamani, S., Dsouza, S., Devadkar, K. (2020). Predicting Surface Air Temperature Using Convolutional Long Short-Term Memory Networks. In: Kumar, A., Mozar, S. (eds) ICCCE 2019. Lecture Notes in Electrical Engineering, vol 570. Springer, Singapore. https://doi.org/10.1007/978-981-13-8715-9_23
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DOI: https://doi.org/10.1007/978-981-13-8715-9_23
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