Abstract
Numerical weather prediction is a computationally expensive task that requires not only the numerical solution to a complex set of non-linear partial differential equations, but also the creation of a parameterization scheme to estimate sub-grid scale phenomenon. This paper outlines an alternative approach to developing a mesoscale meteorological model – a modified recurrent neural network that learns to simulate the solution to these equations. Along with an appropriate time integration scheme and learning algorithm, this method can be used to create multi-day forecasts for a large region.
The learning method presented in this paper is an extended form of Backpropagation Through Time for a recurrent network with outputs that feed back through as inputs only after undergoing a fixed transformation.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Stensrud, D.J.: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, pp. 7–9. Cambridge, New York (2007)
Coiffier, J.: Fundamentals of Numerical Weather Prediction, vol. 4-6, pp. 15–16. Cambridge, New York (2011)
Zakerinia, M., Ghaderi, S.F.: Short Term Wind Power Forecasting Using Time Series Neural Networks. University of Tehran, Tehran (2011)
Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Eng. App. of Art. Intel. (2004)
Mitchell, T.M.: Machine Learning, pp. 119–121. McGraw-Hill, Singapore (1997)
Krasnopolsky, V.M., Michael, S.F., Dmitry, V.C.: New Approach to Calculation of Atmospheric Model Physics: Accurate and Fast Neural Network Emulation of Longwave Radiation in a Climate Model. Mon. Wea. Rev. 133, 1370–1383 (2005)
Corne, D., Reynolds, A., Galloway, S., Owens, E., Peacock, A.: Short term wind speed forecasting with evolved neural networks. In: Blum, C. (ed.) 15th Genetic and Evolutionary Computation Conference Companion (GECCO 2013 Companion), pp. 1521–1528. ACM, New York (2013)
National Centers for Environmental Prediction, ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/rap/prod/
National Climatic Data Center, http://nomads.ncdc.noaa.gov/thredds/dodsC/rap252/
Warner, T.T.: Numerical Weather and Climate Prediction, pp. 456–459. Cambridge, New York (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Firth, R., Chen, J. (2014). Neural Network Implementation of a Mesoscale Meteorological Model. In: Andreasen, T., Christiansen, H., Cubero, JC., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2014. Lecture Notes in Computer Science(), vol 8502. Springer, Cham. https://doi.org/10.1007/978-3-319-08326-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-08326-1_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08325-4
Online ISBN: 978-3-319-08326-1
eBook Packages: Computer ScienceComputer Science (R0)