The Computational Complexity and Parallel Scalability of Atmospheric Data Assimilation Algorithms

Abstract

Abstract This paper quantifies the computational complexity and parallel scalability of two algorithms for four-dimensional data assimilation (4DDA) at NASA's Global Modeling and Assimilation Office (GMAO). The first, the Goddard Earth Observing System Data Assimilation System (GEOS DAS), uses an atmospheric general circulation model (GCM) and an observation-space-based analysis system, the Physical-Space Statistical Analysis System (PSAS). GEOS DAS is very similar to global meteorological weather forecasting data assimilation systems but is used at NASA for climate research. The second, the Kalman filter, uses a more consistent algorithm to determine the forecast error covariance matrix than does GEOS DAS. For atmospheric assimilation, the gridded dynamical fields typically have more than 106 variables; therefore, the full error covariance matrix may be in excess of a teraword. For the Kalman filter this problem will require petaflop s−1 computing to achieve effective throughput for scientific research.