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A Space-time Multiscale Analysis System: A Sequential Variational Analysis Approach


As new observation systems are developed and deployed, new and presumably more precise information is becoming available for weather forecasting and climate monitoring. To take advantage of these new observations, it is desirable to have schemes to accurately retrieve the information before statistical analyses are performed so that statistical computation can be more effectively used where it is needed most. The authors propose a sequential variational approach that possesses advantages of both a standard statistical analysis [such as with a three-dimensional variational data assimilation (3DVAR) or Kalman filter] and a traditional objective analysis (such as the Barnes analysis). The sequential variational analysis is multiscale, inhomogeneous, anisotropic, and temporally consistent, as shown by an idealized test case and observational datasets in this study. The real data cases include applications in two-dimensional and three-dimensional space and time for storm outflow boundary detection (surface application) and hurricane data assimilation (three-dimensional space application). Implemented using a multigrid technique, this sequential variational approach is a very efficient data assimilation method

Article / Publication Data
Print 0027-0644/Online 1520-0493
Available Metadata
Accepted On
November 23, 2010
Fiscal Year
Publication Name
Monthly Weather Review
Published On
April 01, 2011
Final Online Publication On
April 01, 2011
Print Volume
Print Number
Page Range
Submitted On
January 06, 2010


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Authors who have authored or contributed to this publication.

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