Role of Parallel Computing in Numerical Weather Forecasting Models
Subhendu Maity, Subba Reddy Bonthu, Kaushik Sasmal and Hari Warrior. Article: Role of Parallel Computing in Numerical Weather Forecasting Models. IJCA Special Issue on International Conference on Computing, Communication and Sensor Network CCSN2012(4):22-27, March 2013. Full text available. BibTeX
@article{key:article,
author = {Subhendu Maity and Subba Reddy Bonthu and Kaushik Sasmal and Hari Warrior},
title = {Article: Role of Parallel Computing in Numerical Weather Forecasting Models},
journal = {IJCA Special Issue on International Conference on Computing, Communication and Sensor Network},
year = {2013},
volume = {CCSN2012},
number = {4},
pages = {22-27},
month = {March},
note = {Full text available}
}
Abstract
Parallel computing plays a crucial role in state-of-the-art numerical weather and ocean forecasting models like WRF, POM, ROMS and RCAOM. The present study is an attempt to explore and examine the computational time required for the highly complex numerical simulations of weather and ocean models with multi core processors and variable RAM/processor speeds. The simulations, carried out using machines of different computational capability/configuration viz. quad core and Xeon machines, have been investigated with different synthetic experiments to evaluate the role of parallel computing in the operational forecasting system. The saturation rates with different number of processors are also calculated before carrying out forecasting studies. Serial and parallel computations have been carried out with WRF (Weather Forecasting Model) model for simulating the track of a natural hazard viz. the Thane cyclone. The simulations reveal that in the initial stage the computational time decreases exponentially with number of processors and later it reaches saturation stage, even though the number of processors is increased. Additionally, parallel computing simulations showed that the model simulations depend upon the model time step, grid resolution, number of cells in the domain, system architecture, and finally number of vertical levels and their resolutions.
References
- R. Hardy, Weather, NTC Publishing Group, 1996.
- I. Foster, Designing and bulding parallel programs, Addison-Wesley, 1995.
- W. C. Skamarock, J. B. Kelmp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang and J. G. Powers, A description of the advanced research WRF version 2, NCAR Technical Note, 2005.
- K. V. Ooyama, A thermodynamic foundation for modeling the moist atmosphere, J. Atmos. Sci. , Vol. 47, 1990, pp. 2580-2593.
- R. Laprise, The Euler equations of motion with hydrostatic pressure as independent variable, Mon. Wea. Rev. , Vol. 120, 1992, pp. 197-207.
- J. Dudhia, The weather research and forecasting model (Version 2. 0) 2nd international workshop on next generation NWP model, Yonsei University Seoul, Korea, pp. 19-23, 2004.
- J. S. Kain, and J. M. Fritsch, Convective parameterization for mesoscale models: The Kain-Fritcsh scheme, The representation of cumulus convection in numerical models, K. A. Emanuel and D. J. Raymond, Eds. , Amer. Meteor. Soc. , pp. 246, 1993.
- A. K. Betts, and M. J. Miller, A new convective adjustment scheme. Part II: single column tests using GATE wave, BOMEX, and arctic air-mass data sets, Quart. J. Roy. Meteor. Soc. , Vol. 112, 1986, pp. 693-709.
- Z. I. Janjic, The step-mountain eta coordinate model: further developments of the convection, viscous sublayer and turbulence closure schemes, Mon. Wea. Rev. , Vol. 122, pp. 927-945.
- G. A. Grell and D. Devenyi, A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, Geophy. Res. Lett. , Vol. 29(14), 2002, Article 1693.
- S. -Y. Hong and J. -O. J. Lim, The WRF Single-Moment 6-Class Microphysics scheme (WSM6), J. Korean Meteor. Soc. , Vol. 42, 2006, pp. 129-151.