21st Conference on Climate Variability and Change
Seventh Conference on Artificial Intelligence and its Applications to the Environmental Sciences


(Invited Speaker) Using Neural Networks in Numerical Climate and Weather Forecast Systems

Vladimir M. Krasnopolsky, NCEP/NWS/NOAA (SAIC), Camp Springs, MD

In this presentation several groups of neural network (NN) applications that improve performance of numerical climate and weather forecast systems are reviewed. The fist group of considered NN applications improves the computational performance of numerical climate and weather prediction models and data assimilation systems by providing fast model physics. Accurate and fast NN emulations for parameterizations of model physics have been developed for ocean, ocean wave (Krasnopolsky et al. 2002), and atmospheric (Krasnopolsky et al. 2005, 2008) models.

The second group of NN applications discussed in this presentation promises to improve the description of model physics per se. It includes developing new NN based parameterizations using observed and simulated by fine scale models (e.g., cloud resolving model) data, or emulating super-parameterization (in multi-scale model framework).

The third group of discussed NN applications is related to different types of ensembles of numerical models used to improve climate and weather predictions. An application of NN to generate ensembles with perturbed physics is discussed (Krasnopolsky 2007).

ACKNOWLEDGMENTS. The authors would like to thank M. Fox-Rabinovitz, P. Rasch, H. Tolman, and A. Belochitski for fruitful collaboration and discussions. The research is supported by the NOAA CPO CDEP CTB grant NA06OAR4310047 and NSF Grant 0721585.


Krasnopolsky, V. M., D. V. Chalikov, and H. L. Tolman, 2002: "A Neural Network Technique to Improve Computational Efficiency of Numerical Oceanic Models", Ocean Modelling, v. 4, 363-383

Krasnopolsky, V.M., M.S. Fox-Rabinovitz, and D.V. Chalikov, 2005: “Fast and Accurate Neural Network Approximation of Long Wave Radiation in a Climate Model”, Monthly Weather Review, vol. 133, No. 5, pp. 1370-1383.

Krasnopolsky, V.M., 2007: “Neural Network Emulations for Complex Multidimensional Geophysical Mappings: Applications of Neural Network Techniques to Atmospheric and Oceanic Satellite Retrievals and Numerical Modeling”, Reviews of Geophysics, 45, RG3009, doi:10.1029/2006RG000200.

Krasnopolsky, V. M., M.S. Fox-Rabinovitz, and A. A. Belochitski, 2008: "Decadal Climate Simulations Using Accurate and Fast Neural Network Emulation of Full, Long- and Short Wave, Radiation", Monthly Weather Review, in press.

Krasnopolsky V. M., M. S. Fox-Rabinovitz, S. J. Lord, Y. T. Hou, and A. A. Belochitski, 2009: ”Fast Neural Network Emulations of Long Wave Radiation for the NCEP Climate Forecast System Model: Seasonal Prediction and Climate Simulation”, this conference.

wrf recording  Recorded presentation

Joint Session 2, Applications of artificial learning techniques in climate variability, especially as it relates to the urban environment
Wednesday, 14 January 2009, 8:30 AM-10:00 AM, Room 125A

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