A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space
1852977 Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be a...
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Online Access: | https://doi.org/10.1175/MWR-D-23-0065.1 |
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ftncar:oai:drupal-site.org:articles_26689 2024-06-23T07:56:43+00:00 A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space Anderson, Jeffrey L. (author) 2023-10 https://doi.org/10.1175/MWR-D-23-0065.1 en eng Monthly Weather Review--0027-0644--1520-0493 articles:26689 doi:10.1175/MWR-D-23-0065.1 ark:/85065/d7nv9pbt Copyright 2023 American Meteorological Society (AMS). article Text 2023 ftncar https://doi.org/10.1175/MWR-D-23-0065.1 2024-05-27T14:15:41Z 1852977 Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed. Article in Journal/Newspaper Sea ice OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Monthly Weather Review 151 10 2759 2777 |
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Open Polar |
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OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
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language |
English |
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1852977 Traditional ensemble Kalman filter data assimilation methods make implicit assumptions of Gaussianity and linearity that are strongly violated by many important Earth system applications. For instance, bounded quantities like the amount of a tracer and sea ice fractional coverage cannot be accurately represented by a Gaussian that is unbounded by definition. Nonlinear relations between observations and model state variables abound. Examples include the relation between a remotely sensed radiance and the column of atmospheric temperatures, or the relation between cloud amount and water vapor quantity. Part I of this paper described a very general data assimilation framework for computing observation increments for non-Gaussian prior distributions and likelihoods. These methods can respect bounds and other non-Gaussian aspects of observed variables. However, these benefits can be lost when observation increments are used to update state variables using the linear regression that is part of standard ensemble Kalman filter algorithms. Here, regression of observation increments is performed in a space where variables are transformed by the probit and probability integral transforms, a specific type of Gaussian anamorphosis. This method can enforce appropriate bounds for all quantities and deal much more effectively with nonlinear relations between observations and state variables. Important enhancements like localization and inflation can be performed in the transformed space. Results are provided for idealized bivariate distributions and for cycling assimilation in a low-order dynamical system. Implications for improved data assimilation across Earth system applications are discussed. |
author2 |
Anderson, Jeffrey L. (author) |
format |
Article in Journal/Newspaper |
title |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
spellingShingle |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
title_short |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
title_full |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
title_fullStr |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
title_full_unstemmed |
A quantile-conserving ensemble filter framework. Part II: Regression of observation increments in a probit and probability integral transformed space |
title_sort |
quantile-conserving ensemble filter framework. part ii: regression of observation increments in a probit and probability integral transformed space |
publishDate |
2023 |
url |
https://doi.org/10.1175/MWR-D-23-0065.1 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
Monthly Weather Review--0027-0644--1520-0493 articles:26689 doi:10.1175/MWR-D-23-0065.1 ark:/85065/d7nv9pbt |
op_rights |
Copyright 2023 American Meteorological Society (AMS). |
op_doi |
https://doi.org/10.1175/MWR-D-23-0065.1 |
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Monthly Weather Review |
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151 |
container_issue |
10 |
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2759 |
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2777 |
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1802650017373093888 |