Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter
The Ensemble Kalman filter assumes the 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-...
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ftdatacite:10.48550/arxiv.1804.07648 2023-05-15T18:18:37+02:00 Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent 2018 https://dx.doi.org/10.48550/arxiv.1804.07648 https://arxiv.org/abs/1804.07648 unknown arXiv https://dx.doi.org/10.1002/qj.3381 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Optimization and Control math.OC Applications stat.AP Methodology stat.ME FOS Mathematics FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2018 ftdatacite https://doi.org/10.48550/arxiv.1804.07648 https://doi.org/10.1002/qj.3381 2022-04-01T09:47:56Z The Ensemble Kalman filter assumes the 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", at a 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. (2015). Both are designed to explicitly assimilate the out-of-range observations: 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 non-linear toy models. Different sensitivity experiments are conducted to assess the influence of the ensemble size, observation detection limit and a 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. Text Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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Optimization and Control math.OC Applications stat.AP Methodology stat.ME FOS Mathematics FOS Computer and information sciences |
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Optimization and Control math.OC Applications stat.AP Methodology stat.ME FOS Mathematics FOS Computer and information sciences Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent Assimilation of semi-qualitative observations with a stochastic Ensemble Kalman Filter |
topic_facet |
Optimization and Control math.OC Applications stat.AP Methodology stat.ME FOS Mathematics FOS Computer and information sciences |
description |
The Ensemble Kalman filter assumes the 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", at a 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. (2015). Both are designed to explicitly assimilate the out-of-range observations: 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 non-linear toy models. Different sensitivity experiments are conducted to assess the influence of the ensemble size, observation detection limit and a 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 |
Text |
author |
Shah, Abhishek Gharamti, Mohamad El Bertino, Laurent |
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 |
arXiv |
publishDate |
2018 |
url |
https://dx.doi.org/10.48550/arxiv.1804.07648 https://arxiv.org/abs/1804.07648 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.1002/qj.3381 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.1804.07648 https://doi.org/10.1002/qj.3381 |
_version_ |
1766195248425336832 |