Amplitude-Position Formulation of Data Assimilation*

Sai Ravela1

1 Earth, Atmospheric and Planetary Sciences & Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
ravela@mit.edu

Abstract. Classical formulations of data-assimilation perform poorly when forecast locations of weather systems are displaced from their observations. They compensate position errors by adjusting amplitudes, which can produce unacceptably “distorted” states, adversely affecting analysis, verification and subsequent forecasts. It is non-trivial to identify sources of position error, but correcting misplaced forecasts is essential for operationally predicting strong, localized weather events such as tropical cyclones. In this paper, we propose a method that accounts for both position and amplitude errors. The proposed method assimilates observations in two steps. The first step is field alignment, where the current model state is aligned with observations by adjusting a continuous field of local displacements, subject to certain constraints. The second step is amplitude adjustment, where contemporary assimilation approaches are used. Our method shows improvements in analyses, with sparse and uncertain observations.

* This material is supported in part by NSF ITR 0121182 and DDDAS 0540259.

LNCS 3993, pp. 497-505.

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