Quadratic polynomial regression using serial observation processing: Implementation within DART

It is well known that the ensemble-based variants of the Kalman filter may be thought of as producing a state estimate that is consistent with linear regression. Here, it is shown how quadratic polynomial regression can be performed within a serial data assimilation framework. The addition of quadratic polynomial regression to the Data Assimilation Research Testbed (DART) is also discussed and its performance is illustrated using a hierarchy of models from simple scalar systems to a GCM.

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Author Hodyss, Daniel
Anderson, Jeffrey L.
Collins, Nancy
Campbell, William F.
Reinecke, Patrick A.
Publisher UCAR/NCAR - Library
Publication Date 2017-11-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2023-08-18T19:16:17.085388
Metadata Record Identifier edu.ucar.opensky::articles:21350
Metadata Language eng; USA
Suggested Citation Hodyss, Daniel, Anderson, Jeffrey L., Collins, Nancy, Campbell, William F., Reinecke, Patrick A.. (2017). Quadratic polynomial regression using serial observation processing: Implementation within DART. UCAR/NCAR - Library. http://n2t.net/ark:/85065/d7fr009s. Accessed 27 June 2025.

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