Amplitude-Position Formulation of Data Assimilation

  • Sai Ravela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


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.


Tropical Cyclone Data Assimilation Position Error Forecast Ensemble Amplitude Error 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sai Ravela
    • 1
  1. 1.Earth, Atmospheric and Planetary Sciences & Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of Technology 

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