181	1996	Jarmo Rantakokko	jarmo@tdb.uu.se	Strategies for parallel variational data assimilation

			Abstract

The prospects to parallelize a variational data assimilation scheme
from the starting point of an existing parallel forecast model and its
adjoint equations have been investigated. Three parallelization
strategies to do the implementation and two partitioning algorithms to
compute data distributions have been suggested. Numerical simulations
of the parallelizations show that the strategies and the partitioning
algorithms can be combined to perform well on shared memory respectively
distributed memory machines and that they give better results than
direct use of standard parallelization methods.