The Community for Technology Leaders
RSS Icon
Issue No.05 - Sept.-Oct. (2013 vol.15)
pp: 56-67
Bo-Wen Shen , University of Maryland, College Park, and NASA Goddard Space Flight Center
Bron Nelson , NASA Ames Research Center
Samson Cheung , NASA Ames Research Center
Wei-Kuo Tao , NASA Goddard Space Flight Center
One of the current challenges in tropical cyclone (TC) research is how to improve our understanding of TC interannual variability and the impact of climate change on TCs. Recent advances in global modeling, visualization, and supercomputing technologies at NASA show potential for such studies. In this article, the authors discuss recent scalability improvement to the multiscale modeling framework (MMF) that makes it feasible to perform long-term TC-resolving simulations. The MMF consists of the finite-volume general circulation model (fvGCM), supplemented by a copy of the Goddard cumulus ensemble model (GCE) at each of the fvGCM grid points, giving 13,104 GCE copies. The original fvGCM implementation has a 1D data decomposition; the revised MMF implementation retains the 1D decomposition for most of the code, but uses a 2D decomposition for the massive copies of GCEs. Because the vast majority of computation time in the MMF is spent computing the GCEs, this approach can achieve excellent speedup without incurring the cost of modifying the entire code. Intelligent process mapping allows differing numbers of processes to be assigned to each domain for load balancing. The revised parallel implementation shows highly promising scalability, obtaining a nearly 80-fold speedup by increasing the number of cores from 30 to 3,335.
Atmospheric modeling, Clouds, Computational modeling, NASA, Meteorology, Hurricanes, Tropical cyclones, NASA,scientific computing, distributed programming, hurricane modeling, climate modeling, software, software engineering
Bo-Wen Shen, Bron Nelson, Samson Cheung, Wei-Kuo Tao, "Improving NASA's Multiscale Modeling Framework for Tropical Cyclone Climate Study", Computing in Science & Engineering, vol.15, no. 5, pp. 56-67, Sept.-Oct. 2013, doi:10.1109/MCSE.2012.90
1. L. Bengtsson,I. Hodges,, and M. Esch,“Tropical Cyclones in a T159 Resolution Global Climate Model: Comparison with Observations and Re-analyses,” Tellus A, vol. 59, no. 4, 2007, pp. 396-416.
2. B.-W. Shen et al., “Hurricane Forecasts with a Global Mesoscale-Resolving Model: Preliminary Results with Hurricane Katrina,” Geophysical Research Letters, vol. 33, no. 13, 2006; doi:10.1029/2006GL026143.
3. B.-W. Shen et al., “Predicting Tropical Cyclogenesis with a Global Mesoscale Model: Hierarchical Multiscale Interactions During the Formation of Tropical Cyclone Nargis,” J. Geophysical Research, vol. 115, no. D14, 2010; doi:10.1029/2009JD013140.
4. B.-W. Shen,W.-K. Tao,, and B. Green,“Coupling Advanced Modeling and Visualization to Improve High-Impact Tropical Weather Prediction (CAMVis),” Computing in Science & Eng., vol. 13, no. 5, 2011, pp. 56-67.
5. B.-W. Shen et al., “Genesis of Twin Tropical Cyclones as Revealed by a Global Mesoscale Model: The Role of Mixed Rossby Gravity Waves,” J. Geophysical Research, vol. 117, no. D13, 2012; doi:10.1029/2012JD017450.
6. B.-W. Shen et al., “Advanced Visualizations of Scale Interactions of Tropical Cyclone Formation and Tropical Waves,” Computing in Science & Eng., vol. 15, no. 2, 2013, pp. 47-52.
7. B.-W. Shen et al., “Genesis of Hurricane Sandy (2012) Simulated with a Global Mesoscale Model,” Geophysical Research Letters, vol. 40, 2013, pp. 1-7; doi:10.1002/grl.50934.
8. D. Randall et al., “Breaking the Cloud Parameterization Deadlock,” Bull. Am. Meteorological Soc., vol. 84, no. 11, 2003, pp. 1547-1564.
9. W.-K. Tao et al., “A Goddard Multi-Scale Modeling System with Unified Physics,” WCRP/GEWEX Newsletter, vol. 18, no. 1, 2008, pp. 6-8.
10. W.-K. Tao et al., “Multiscale Modeling System: Development, Applications and Critical Issues,” Bull. Am. Meteorological Soc., vol. 90, no. 4, 2009, pp. 515-534.
11. R. Atlas et al., “Hurricane Forecasting with the High-Resolution NASA Finite Volume General Circulation Model,” Geophysical Res. Letters, vol. 32, no. 3, 2005; doi:10.1029/2004GL021513.
12. W.-K. Tao and J. Simpson,“The Goddard Cumulus Ensemble Model. Part I: Model Description,” Terrestrial, Atmospheric and Oceanic Sciences, vol. 4, no. 1, 1993, pp. 19-54.
13. W.-K. Tao et al., “Convective Systems over South China Sea: Cloud-Resolving Model Simulations,” J. Atmospheric Science, vol. 60, no. 24, 2003, pp. 2929-2956.
14. J.-M. Juang et al., “Parallelization of NASA Goddard Cloud Ensemble Model for Massively Parallel Computing,” Terrestrial, Atmospheric and Oceanic Sciences, vol. 18, no. 3, 2007, pp. 593-622.
15. S.-J. Lin,B.-W. Shen,, and W. P. Putman,“Application of the High-Resolution Finite-Volume NASA/NCAR Climate Model for Medium-Range Weather Prediction Experiments,” EGS-AGU-EUG Joint Assembly, 2003, abstract 1738.
16. W. Putman,S.-J. Lin,, and B.-W. Shen,“Cross-Platform Performance of a Portable Communication Module and the NASA Finite Volume General Circulation Model,” Int’l J. High Performance Computing Applications, vol. 19, no. 3, 2005, pp. 213-223.
84 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool