Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data
Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
El Serafy, Ghada Y. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2011 |
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| Anmerkung: |
© Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 |
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| Übergeordnetes Werk: |
Enthalten in: Ocean Science Journal - Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009, 46(2011), 3 vom: 11. Okt. |
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| Übergeordnetes Werk: |
volume:46 ; year:2011 ; number:3 ; day:11 ; month:10 |
| Links: |
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| DOI / URN: |
10.1007/s12601-011-0015-x |
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| 520 | |a Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. | ||
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| 700 | 1 | |a Aguilar, Sandra Gaytan |4 aut | |
| 700 | 1 | |a Van der Woerd, Hendrik J. |4 aut | |
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10.1007/s12601-011-0015-x doi (DE-627)SPR02627941X (SPR)s12601-011-0015-x-e DE-627 ger DE-627 rakwb eng El Serafy, Ghada Y. verfasserin aut Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. Eleveld, Marieke A. aut Blaas, Meinte aut van Kessel, Thijs aut Aguilar, Sandra Gaytan aut Van der Woerd, Hendrik J. aut Enthalten in Ocean Science Journal Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009 46(2011), 3 vom: 11. Okt. (DE-627)SPR02627874X nnns volume:46 year:2011 number:3 day:11 month:10 https://dx.doi.org/10.1007/s12601-011-0015-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_22 GBV_ILN_72 GBV_ILN_2012 AR 46 2011 3 11 10 |
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10.1007/s12601-011-0015-x doi (DE-627)SPR02627941X (SPR)s12601-011-0015-x-e DE-627 ger DE-627 rakwb eng El Serafy, Ghada Y. verfasserin aut Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. Eleveld, Marieke A. aut Blaas, Meinte aut van Kessel, Thijs aut Aguilar, Sandra Gaytan aut Van der Woerd, Hendrik J. aut Enthalten in Ocean Science Journal Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009 46(2011), 3 vom: 11. Okt. (DE-627)SPR02627874X nnns volume:46 year:2011 number:3 day:11 month:10 https://dx.doi.org/10.1007/s12601-011-0015-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_22 GBV_ILN_72 GBV_ILN_2012 AR 46 2011 3 11 10 |
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10.1007/s12601-011-0015-x doi (DE-627)SPR02627941X (SPR)s12601-011-0015-x-e DE-627 ger DE-627 rakwb eng El Serafy, Ghada Y. verfasserin aut Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. Eleveld, Marieke A. aut Blaas, Meinte aut van Kessel, Thijs aut Aguilar, Sandra Gaytan aut Van der Woerd, Hendrik J. aut Enthalten in Ocean Science Journal Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009 46(2011), 3 vom: 11. Okt. (DE-627)SPR02627874X nnns volume:46 year:2011 number:3 day:11 month:10 https://dx.doi.org/10.1007/s12601-011-0015-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_22 GBV_ILN_72 GBV_ILN_2012 AR 46 2011 3 11 10 |
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10.1007/s12601-011-0015-x doi (DE-627)SPR02627941X (SPR)s12601-011-0015-x-e DE-627 ger DE-627 rakwb eng El Serafy, Ghada Y. verfasserin aut Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. Eleveld, Marieke A. aut Blaas, Meinte aut van Kessel, Thijs aut Aguilar, Sandra Gaytan aut Van der Woerd, Hendrik J. aut Enthalten in Ocean Science Journal Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009 46(2011), 3 vom: 11. Okt. (DE-627)SPR02627874X nnns volume:46 year:2011 number:3 day:11 month:10 https://dx.doi.org/10.1007/s12601-011-0015-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_22 GBV_ILN_72 GBV_ILN_2012 AR 46 2011 3 11 10 |
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10.1007/s12601-011-0015-x doi (DE-627)SPR02627941X (SPR)s12601-011-0015-x-e DE-627 ger DE-627 rakwb eng El Serafy, Ghada Y. verfasserin aut Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. Eleveld, Marieke A. aut Blaas, Meinte aut van Kessel, Thijs aut Aguilar, Sandra Gaytan aut Van der Woerd, Hendrik J. aut Enthalten in Ocean Science Journal Korean Ocean Research and Development Institute and The Korean society of Oceanography, 2009 46(2011), 3 vom: 11. Okt. (DE-627)SPR02627874X nnns volume:46 year:2011 number:3 day:11 month:10 https://dx.doi.org/10.1007/s12601-011-0015-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_22 GBV_ILN_72 GBV_ILN_2012 AR 46 2011 3 11 10 |
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Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data |
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Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 |
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Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 |
| abstract_unstemmed |
Abstract The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme. © Korea Ocean Research & Development Institute (KORDI) and the Korean Society of Oceanography (KSO) and Springer Netherlands 2011 |
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Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data |
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Eleveld, Marieke A. Blaas, Meinte van Kessel, Thijs Aguilar, Sandra Gaytan Van der Woerd, Hendrik J. |
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