Initial Developments: Development of the NN methodology for: emulating model radiation

1. Development of neural network emulations of model physics components for improving the computational performance of the NCEP seasonal climate forecastsP.I.s: M. Fox-Rabinovitz, V. Krasnopolsky, Co-I.s: S. Lord, Y.-T. Hou, Collaborator: A. Belochitski, CTB contact: H.-L. Pan • Initial Developments: • Development of the NN methodology for: • emulating model radiation • experimentation and validation framework • Initial development (with a limited training data set) of NN emulations for the CFS long wave radiation parameterization (LWR) and validation of its accuracy • Creating an initial 2 year long LWR training data set with CFS • All three above tasks have been completed during the report period • Due to the major upgrades of the NCEP CFS model and the NCEP supercomputer system occurred during the report period, finalizing the LWR NN emulation and performing initial seasonal climate predictions with NN emulations of CFS radiation will be done shortly.

2. Two representative profiles of LWR heating rates. NN emulation with 80 hidden neurons (preliminary results on a limited training set). Profiles for two very different atmospheric conditions: Black – the profile generated by the original LWR parameterization, red – by the NN emulation.

3. Upcoming Near Term Developments • Completing work on LWR NN emulation • Generating more representative data sets • Training and validation of LWR NN emulation for the CFS model • Performing and validating seasonal climate predictions with LWR NN emulations • Refining the NN methodology for emulating model physics • Work on an NN ensemble approach to improve accuracy of NN emulations • Develop a compound parameterization for quality control (QC) and for dynamical adjustment of the NN emulations • Refining experimentation and validation framework • Continuation of development of NN emulations for the CFS model radiation block • Analysis of CFS SWR, generating data sets, and developing an initial SWR NN emulation. • Initial experiments with SWR NN emulation for the CFS model • Initial development of the project web site and its link to a relevant NCEP and/or CTB web sites

4. Developed methodology was presented and published in: • V.M. Krasnopolsky, 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, in press • V.M. Krasnopolsky, 2007: “Reducing Uncertainties in Neural Network Jacobians and Improving Accuracy of Neural Network Emulations with NN Ensemble Approaches”, Neural Networks, in press • V.M. Krasnopolsky and M.S. Fox-Rabinovitz, 2006: "Complex Hybrid Models Combining Deterministic and Machine Learning Components for Numerical Climate Modeling and Weather Prediction", Neural Networks, 19, 122-134 • V.M. Krasnopolsky, M. S. Fox-Rabinovitz, and A. Belochitski, 2007: “Accurate and Fast Neural Network Emulation of Full, Long- and Short Wave, Model Radiation Used for Decadal Climate Simulations with NCAR CAM”, Proc., 87th Annual AMS Meeting, San Antonio, TX, CD-ROM, J3.3 • V.M. Krasnopolsky, M. S. Fox-Rabinovitz, and A. Belochitski, 2007:“Compound Parameterization for a Quality Control of Outliers and Larger Errors in NN Emulations of Model Physics”, Proc., 2007 International Joint Conference on Neural Networks, accepted • M. S. Fox-Rabinovitz, V. Krasnopolsky, and A. Belochitski, 2006: “Ensemble of Neural Network Emulations for Climate Model Physics: The Impact on Climate Simulations”, Proc.,2006 International Joint Conference on Neural Networks, Vancouver, BC, Canada, July 16-21, 2006, pp. 9321-9326, CD-ROM