Neural Network Implementation of a Mesoscale Meteorological Model

  • Robert Firth
  • Jianhua Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)


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.


Recurrent neural networks spatial-temporal weather prediction forecasting temperature wind 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robert Firth
    • 1
  • Jianhua Chen
    • 1
  1. 1.Division of Computer Science and Engineering, School of Electrical Engineering and Computer ScienceLouisiana State UniversityBaton RougeUSA

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