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...
Published in: | Quarterly Journal of the Royal Meteorological Society |
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ftncar:oai:drupal-site.org:articles_22113 2023-05-15T18:18:30+02:00 Assimilation of semi-qualitative observations with a stochastic ensemble Kalman filter Shah, Abhishek (author) Gharamti, Mohamad El (author) Bertino, Laurent (author) 2018-10-25 https://doi.org/10.1002/qj.3381 en eng Quarterly Journal of the Royal Meteorological Society--Q J R Meteorol Soc--00359009 articles:22113 ark:/85065/d7251n4h doi:10.1002/qj.3381 Copyright 2018 Royal Meteorological Society article Text 2018 ftncar https://doi.org/10.1002/qj.3381 2022-08-09T17:11:33Z 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 OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) Quarterly Journal of the Royal Meteorological Society 144 715 1882 1894 |
institution |
Open Polar |
collection |
OpenSky (NCAR/UCAR - National Center for Atmospheric Research/University Corporation for Atmospheric Research) |
op_collection_id |
ftncar |
language |
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. |
author2 |
Shah, Abhishek (author) Gharamti, Mohamad El (author) Bertino, Laurent (author) |
format |
Article in Journal/Newspaper |
title |
Assimilation of semi-qualitative observations with a stochastic ensemble Kalman filter |
spellingShingle |
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 |
publishDate |
2018 |
url |
https://doi.org/10.1002/qj.3381 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
Quarterly Journal of the Royal Meteorological Society--Q J R Meteorol Soc--00359009 articles:22113 ark:/85065/d7251n4h doi:10.1002/qj.3381 |
op_rights |
Copyright 2018 Royal Meteorological Society |
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 |
_version_ |
1766195095104651264 |