Improvement of a 3D CTM and a 4D variational data assimilation on a vector machine CRAY J90 through a multitasking strategy

Abstract

In this paper we report on the improvement of a 3D CTM and a 4D variational data assimilation (4DDA) on a vector machine CRAY J90. Significant speedup has been achieved by applying a general multitasking strategy to both a 3D CTM and a 4DDA on the shared-memory platform of a CRAY J90. The 3D CTM has been multitasked to study many complex processes involved in the troposphere. For example, annual simulation to study the interaction between the atmosphere, biosphere (e.g., terrestrial vegetation), and oceans; while the 4DDA has been multitasked to assimilate observational data from satellites (e.g., UARS and ATMOS) and other measurements (e.g., ozonesondes and aircrafts). Evaluation of the multitasked models (both 3D CTM and 4DDA) are carried out by comparing (1) required job elapsed time, and (2) spatial and temporal distribution of long-lived and short-lived chemical species, physical fields, and photolysis rates between the single-threaded and the multitasked simulations. The agreement from the later comparisons indicate a correct multitasking strategy, while the first comparison shows a significantly reduced elapsed time. This validates the need of a multitasking strategy in complex global biogeochemical modeling and 4D chemical data assimilation.