Advertisement

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)

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.

Keywords

Recurrent neural networks spatial-temporal weather prediction forecasting temperature wind 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Stensrud, D.J.: Parameterization Schemes: Keys to Understanding Numerical Weather Prediction Models, pp. 7–9. Cambridge, New York (2007)CrossRefGoogle Scholar
  2. 2.
    Coiffier, J.: Fundamentals of Numerical Weather Prediction, vol. 4-6, pp. 15–16. Cambridge, New York (2011)CrossRefzbMATHGoogle Scholar
  3. 3.
    Zakerinia, M., Ghaderi, S.F.: Short Term Wind Power Forecasting Using Time Series Neural Networks. University of Tehran, Tehran (2011)Google Scholar
  4. 4.
    Abdel-Aal, R.E.: Hourly temperature forecasting using abductive networks. Eng. App. of Art. Intel. (2004)Google Scholar
  5. 5.
    Mitchell, T.M.: Machine Learning, pp. 119–121. McGraw-Hill, Singapore (1997)zbMATHGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 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. 8.
    National Centers for Environmental Prediction, ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/rap/prod/
  9. 9.
  10. 10.
    Warner, T.T.: Numerical Weather and Climate Prediction, pp. 456–459. Cambridge, New York (2011)zbMATHGoogle Scholar

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

Personalised recommendations