Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter
The ensemble Kalman filter assumes observations to be Gaussian random variables with a pre‐specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases, most data assimilation schemes discard out‐of‐ran...
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crwiley:10.1002/qj.3381 2024-06-02T08:14:20+00:00 Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent 2018 http://dx.doi.org/10.1002/qj.3381 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fqj.3381 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3381 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qj.3381 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3381 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Quarterly Journal of the Royal Meteorological Society volume 144, issue 715, page 1882-1894 ISSN 0035-9009 1477-870X journal-article 2018 crwiley https://doi.org/10.1002/qj.3381 2024-05-03T10:42:29Z The ensemble Kalman filter assumes observations to be Gaussian random variables with a pre‐specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases, most data assimilation schemes discard out‐of‐range values, treating them as “not a number,” with the loss of possibly useful qualitative information. The current work focuses on the development of a data assimilation scheme that tackles observations with a detection limit. We present the Ensemble Kalman Filter Semi‐Qualitative (EnKF‐SQ) and test its performance against the Partial Deterministic Ensemble Kalman Filter (PDEnKF) of Borup et al. Both are designed to assimilate out‐of‐range observations explicitly: the out‐of‐range values are qualitative by nature (inequalities), but one can postulate a probability distribution for them and then update the ensemble members accordingly. The EnKF‐SQ is tested within the framework of twin experiments, using both linear and nonlinear toy models. Different sensitivity experiments are conducted to assess the influence of the ensemble size, observation detection limit and number of observations on the performance of the filter. Our numerical results show that assimilating qualitative observations using the proposed scheme improves the overall forecast mean, making it viable for testing on more realistic applications such as sea‐ice models. Article in Journal/Newspaper Sea ice Wiley Online Library Quarterly Journal of the Royal Meteorological Society 144 715 1882 1894 |
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English |
description |
The ensemble Kalman filter assumes observations to be Gaussian random variables with a pre‐specified mean and variance. In practice, observations may also have detection limits, for instance when a gauge has a minimum or maximum value. In such cases, most data assimilation schemes discard out‐of‐range values, treating them as “not a number,” with the loss of possibly useful qualitative information. The current work focuses on the development of a data assimilation scheme that tackles observations with a detection limit. We present the Ensemble Kalman Filter Semi‐Qualitative (EnKF‐SQ) and test its performance against the Partial Deterministic Ensemble Kalman Filter (PDEnKF) of Borup et al. Both are designed to assimilate out‐of‐range observations explicitly: the out‐of‐range values are qualitative by nature (inequalities), but one can postulate a probability distribution for them and then update the ensemble members accordingly. The EnKF‐SQ is tested within the framework of twin experiments, using both linear and nonlinear toy models. Different sensitivity experiments are conducted to assess the influence of the ensemble size, observation detection limit and number of observations on the performance of the filter. Our numerical results show that assimilating qualitative observations using the proposed scheme improves the overall forecast mean, making it viable for testing on more realistic applications such as sea‐ice models. |
format |
Article in Journal/Newspaper |
author |
Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent |
spellingShingle |
Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
author_facet |
Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent |
author_sort |
Shah, Abhishek |
title |
Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
title_short |
Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
title_full |
Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
title_fullStr |
Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
title_full_unstemmed |
Assimilation of semi‐qualitative observations with a stochastic ensemble Kalman filter |
title_sort |
assimilation of semi‐qualitative observations with a stochastic ensemble kalman filter |
publisher |
Wiley |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/qj.3381 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fqj.3381 https://onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3381 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/qj.3381 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3381 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Quarterly Journal of the Royal Meteorological Society volume 144, issue 715, page 1882-1894 ISSN 0035-9009 1477-870X |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1002/qj.3381 |
container_title |
Quarterly Journal of the Royal Meteorological Society |
container_volume |
144 |
container_issue |
715 |
container_start_page |
1882 |
op_container_end_page |
1894 |
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1800738132843298816 |