Using a High Throughput Computational Grid for the Retrieval of Aerosol Properties over China Land

This paper describes the need for and the proposed designing of the high performance quantitative retrieval model to be used on Computational Grid for study of aerosol properties, with particular emphasis on Aerosol Optical Thickness (AOT) determination. A methodology using multi-resource remotely sensed data and adapting available aerosol retrieval model in a Grid environment is demonstrated. The algorithm comprises two complementary parts, collectively used in a distributed application. This paper focused on parallelization method based on a resource management and task partition strategy. A module, called DPPA (Dynamic Partition Points Algorithm for workload estimation), is designed as a portable technology for developing and deploying Grid execution in a generic data parallel paradigm. Experimental results are presented in a realistic application, using data collected by MODIS over China land. Derived result and computing performance of the proposed algorithm is given using the Grid test-bed at the Institute of Remote Sensing Applications of Chinese Academy of Sciences (IRSA, CAS). Combined, the experimental results show that Gridenabled model allowed on-demand large volume of ground-based data assimilation with parameters, and achieved substantial reductions in computational times. The research gives a thoughtful perspective on the potential of applying high performance computing practices to remote sensing quantitative retrieving problems. * Corresponding author.