Fortran 90 handbook: complete ANSI/ISO reference
Fortran 90 handbook: complete ANSI/ISO reference
Improving static and dynamic registration in an optical see-through HMD
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A distributed and iterative method for square root filtering in space-time estimation
Automatica (Journal of IFAC)
The design of parallel square-root covariance Kalman filters using algorithm engineering
Integration, the VLSI Journal - Special issue: algorithms and parallel VLSI architectures
The Architecture of the Earth System Modeling Framework
Computing in Science and Engineering
Design and Implementation of Components in the Earth System Modeling Framework
International Journal of High Performance Computing Applications
Future Generation Computer Systems
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Data assimilation is the process of feeding a partially unknown prediction model with available information from observations, with the objective of correcting and improving the modeled results. One of the most important mathematical tools to perform data assimilation is the Kalman filter. This is essentially a predictor-corrector algorithm that is optimal in the sense of minimizing the trace of the covariance matrix of the errors. Unfortunately, the computational cost of applying the filter to large scale problems is enormous, and the programming of the filter is highly dependent on the model and the format of the data involved. The first objective of this article is to present a set of Fortran 90 modules that implement the reduced rank square root versions of the Kalman filter, adapted for the assimilation of a very large number of variables. The second objective is to present a Kalman filter implementation whose code is independent of both the model and observations and is easy to use. A detailed description of the algorithms, structure, parallelization is given along with examples of using the package to solve practical problems.