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Ensemble Synoptic Analysis

  • Gregory J. Hakim
  • Ryan D. Torn
Chapter
Part of the Meteorological Monographs book series (METEOR, volume 33, No. 55)

Abstract

Synoptic and mesoscale meteorology underwent a revolution in the 1940s and 1950s with the widespread deployment of novel weather observations, such as the radiosonde network and the advent of weather radar. These observations provoked a rapid increase in our understanding of the structure and dynamics of the atmosphere by pioneering analysts such as Fred Sanders. The authors argue that we may be approaching an analogous revolution in our ability to study the structure and dynamics of atmospheric phenomena with the advent of probabilistic objective analyses. These probabilistic analyses provide not only best estimates of the state of the atmosphere (e.g., the expected value) and the uncertainty about this state (e.g., the variance), but also the relationships between all locations and all variables at that instant in time. Up until now, these relationships have been determined by sampling in time by, for example, case studies, composites, and time-series analysis. Here the authors propose a new approach, ensemble synoptic analysis, which exploits the information contained in probabilistic samples of analyses at one or more instants in time.

One source of probabilistic analyses is ensemble-based state-estimation methods, such as ensemble-based Kalman filters. Analyses from such a filter may be used to study atmospheric phenomena and the relationships between fields and locations at one or more instants in time. After a brief overview of a research-based ensemble Kalman filter, illustrative examples of ensemble synoptic analysis are given for an extratropical cyclone, including relationships between the cyclone minimum sea level pressure and other synoptic features, statistically determined operators for potential-vorticity inversion, and ensemble-based sensitivity analysis.

Keywords

Geopotential Height Ensemble Member Potential Vorticity Extratropical Cyclone Surface Cyclone 
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

© American Meteorological Society 2008

Authors and Affiliations

  • Gregory J. Hakim
    • 1
  • Ryan D. Torn
    • 1
  1. 1.Department of Atmospheric SciencesUniversity of WashingtonSeattleUSA

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