Parallel Implementation of a Kalman Filter for Constituent Data Assimilation

Abstract

A Kalman filter for the assimilation of long-lived atmospheric chemical constituents was developed for two-dimensional transport models on isentropic surfaces over the globe. Since the Kalman filter calculates the error covariances of the estimated constituent field, there are five dimensions to this problem, x1, x2, and time, where x1 and x2 are the positions of two points on an isentropic surface. Only computers with large memory capacity and high floating point speed can handle problems of this magnitude. This article describes an implementation of the Kalman filter for distributed-memory, message-passing parallel computers. To evolve the forecast error covariance matrix, an operator decomposition and a covariance decomposition were studied. The latter was found to be scalable and has the general property, of considerable practical advantage, that the dynamical model does not need to be parallelized. Tests of the Kalman filter code examined variance transport and observability properties. This code is being used currently to assimilate constituent data retrieved by limb sounders on the Upper Atmosphere Research Satellite.