Transparent grid enablement of weather research and forecasting

The impact of hurricanes is so devastating throughout different levels of society that there is a pressing need to provide a range of users with accurate and timely information that can enable effective planning for and response to potential hurricane landfalls. The Weather Research and Forecasting (WRF) code is the latest numerical model that has been adopted by meteorological services worldwide. The current version of WRF has not been designed to scale out of a single organization's local computing resources. However, the high resource requirements of WRF for fine-resolution and ensemble forecasting demand a large number of computing nodes, which typically cannot be found within one organization. Therefore, there is a pressing need for the Grid-enablement of the WRF code such that it can utilize resources available in partner organizations. In this paper, we present our research on Grid enablement of WRF by leveraging our work in transparent shaping, GRID superscalar, profiling, code inspection, code modeling, meta-scheduling, and job flow management.

[1]  Shuyi S. Chen,et al.  The CBLAST-Hurricane program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction , 2007 .

[2]  Onyeka Ezenwoye,et al.  TRAP/BPEL - A Framework for Dynamic Adaptation of Composite Services , 2007, WEBIST.

[3]  Miron Livny,et al.  Condor-a hunter of idle workstations , 1988, [1988] Proceedings. The 8th International Conference on Distributed.

[4]  Wolfgang Emmerich,et al.  Engineering Distributed Objects , 2000, Lecture Notes in Computer Science.

[5]  Allen D. Malony,et al.  The Tau Parallel Performance System , 2006, Int. J. High Perform. Comput. Appl..

[6]  Hector A. Duran-Limon,et al.  Platform-independent modeling and prediction of application resource usage characteristics , 2009, J. Syst. Softw..

[7]  Seyed Masoud Sadjadi,et al.  TRAP/J: Transparent Generation of Adaptable Java Programs , 2004, CoopIS/DOA/ODBASE.

[8]  Onyeka Ezenwoye,et al.  Composing aggregate web services in BPEL , 2006, ACM-SE 44.

[9]  Toni Cortes,et al.  PARAVER: A Tool to Visualize and Analyze Parallel Code , 2007 .

[10]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.

[11]  S. Masoud Sadjadi,et al.  Transparent shaping of existing software to support pervasive and autonomic computing , 2005 .

[12]  Xabriel J. Collazo-Mojica Finding an Appropriate Profiler for the Weather Research and Forecasting Code Technical Report , 2007 .

[13]  Michael Fiorino,et al.  Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific , 2003 .

[14]  Seyed Masoud Sadjadi,et al.  Improving Separation of Concerns in the Development of Scientific Applications , 2007, SEKE.

[15]  T. N. Krishnamurti,et al.  Real-Time Multimodel Superensemble Forecasts of Atlantic Tropical Systems of 1999 , 2003 .

[16]  Wen-Chau Lee,et al.  Hurricane Intensity and Eyewall Replacement , 2007, Science.

[17]  Tim O'Reilly,et al.  What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software , 2007 .

[18]  Wei Wang,et al.  Prediction of Landfalling Hurricanes with the Advanced Hurricane WRF Model , 2008 .

[19]  Jesús Labarta,et al.  eNANOS Grid Resource Broker , 2005, EGC.

[20]  Gargi Dasgupta,et al.  Innovative Grid Technologies Applied to Bioinformatics and Hurricane Mitigation , 2006, High Performance Computing Workshop.