Neural Network Implementation of a Mesoscale Meteorological Model
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.
KeywordsRecurrent neural networks spatial-temporal weather prediction forecasting temperature wind
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- 3.Zakerinia, M., Ghaderi, S.F.: Short Term Wind Power Forecasting Using Time Series Neural Networks. University of Tehran, Tehran (2011)Google Scholar
- 4.Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Eng. App. of Art. Intel. (2004)Google Scholar
- 7.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)Google Scholar
- 8.National Centers for Environmental Prediction, ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/rap/prod/
- 9.National Climatic Data Center, http://nomads.ncdc.noaa.gov/thredds/dodsC/rap252/