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Seasonal Forecast Model (SFM) is an atmosphere general circulation model used for predicting the Indian summer monsoon rainfall in advance of a season. Ensemble forecasting methodology is used to minimize the effect of uncertainties in the initial conditions. The inherent parallelism available with the ensemble forecast methodology makes it a suitable application that can effectively utilize the power of Grid computing paradigm. SFM is implemented on the GARUDA Infrastructure, a national Grid computing initiative of India. Initially, the SFM is run at T62 resolution (Equivalent to 200 km x 200 km physical grid resolution). GridWay meta-scheduler is used to schedule and monitor the jobs with Portable Batch System (PBS) as the local resource manager. GARUDA Visualization Gateway (GVG) tool is used to gather and visualize the outputs from different sites. This prototype run is executed on compute clusters at five different geographical locations. Due to the heterogeneous nature of the GARUDA Infrastructure, variations in performance were noticed. The results of the prototype runs were also used by the GARUDA operational community to fine tune the configurations of compute clusters at various sites of GARUDA. High resolution SFM at T320 resolution (Equivalent to 37 km x 37 km physical grid resolution) is also implemented to understand the scalability of the application on the Grid. Further work is underway to increase the ensemble size. Large ensemble size requires the use of considerable amount of computing power and storage which typically cannot be found at one or two locations. In this work, we describe our experience in conducting the ensemble runs of SFM on GARUDA Grid. We attempt to provide a perspective on the desirable features of a Grid middleware for easier uptake to Grid computing by the climate modeling community.

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