Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter
The swell propagation model built on geometric optics is known to work well when simulating radiated swells from a far located storm. Based on this simple approximation, satellites have acquired plenty of large samples on basin-traversing swells induced by fierce storms situated in mid-latitudes. Ho...
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MDPI AG
2016-11-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/12/2000 |
| _version_ | 1828395184192225280 |
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| author | Xuan Wang Pierre Tandeo Ronan Fablet Romain Husson Lei Guan Ge Chen |
| author_facet | Xuan Wang Pierre Tandeo Ronan Fablet Romain Husson Lei Guan Ge Chen |
| author_sort | Xuan Wang |
| collection | DOAJ |
| description | The swell propagation model built on geometric optics is known to work well when simulating radiated swells from a far located storm. Based on this simple approximation, satellites have acquired plenty of large samples on basin-traversing swells induced by fierce storms situated in mid-latitudes. How to routinely reconstruct swell fields with these irregularly sampled observations from space via known swell propagation principle requires more examination. In this study, we apply 3-h interval pseudo SAR observations in the ensemble Kalman filter (EnKF) to reconstruct a swell field in ocean basin, and compare it with buoy swell partitions and polynomial regression results. As validated against in situ measurements, EnKF works well in terms of spatial–temporal consistency in far-field swell propagation scenarios. Using this framework, we further address the influence of EnKF parameters, and perform a sensitivity analysis to evaluate estimations made under different sets of parameters. Such analysis is of key interest with respect to future multiple-source routinely recorded swell field data. Satellite-derived swell data can serve as a valuable complementary dataset to in situ or wave re-analysis datasets. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T08:08:20Z |
| publishDate | 2016-11-01 |
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| spelling | doaj.art-f3853dd9ea9a47ee861b5e4f86b6fa3b2022-12-22T01:56:38ZengMDPI AGSensors1424-82202016-11-011612200010.3390/s16122000s16122000Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman FilterXuan Wang0Pierre Tandeo1Ronan Fablet2Romain Husson3Lei Guan4Ge Chen5Qingdao Collaborative Innovation Center of Marine Science and Technology, College of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaInstitut Telecom/Telecom Bretagne, UMR LabSTICC, Technopôle Brest-Iroise, Brest 29280, FranceInstitut Telecom/Telecom Bretagne, UMR LabSTICC, Technopôle Brest-Iroise, Brest 29280, FranceCollecte Localisation Satellites, Brest 29280, FranceQingdao Collaborative Innovation Center of Marine Science and Technology, College of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaQingdao Collaborative Innovation Center of Marine Science and Technology, College of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaThe swell propagation model built on geometric optics is known to work well when simulating radiated swells from a far located storm. Based on this simple approximation, satellites have acquired plenty of large samples on basin-traversing swells induced by fierce storms situated in mid-latitudes. How to routinely reconstruct swell fields with these irregularly sampled observations from space via known swell propagation principle requires more examination. In this study, we apply 3-h interval pseudo SAR observations in the ensemble Kalman filter (EnKF) to reconstruct a swell field in ocean basin, and compare it with buoy swell partitions and polynomial regression results. As validated against in situ measurements, EnKF works well in terms of spatial–temporal consistency in far-field swell propagation scenarios. Using this framework, we further address the influence of EnKF parameters, and perform a sensitivity analysis to evaluate estimations made under different sets of parameters. Such analysis is of key interest with respect to future multiple-source routinely recorded swell field data. Satellite-derived swell data can serve as a valuable complementary dataset to in situ or wave re-analysis datasets.http://www.mdpi.com/1424-8220/16/12/2000pseudo SAR observationEnKFswell field reconstructionparameter sensitivity |
| spellingShingle | Xuan Wang Pierre Tandeo Ronan Fablet Romain Husson Lei Guan Ge Chen Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter Sensors pseudo SAR observation EnKF swell field reconstruction parameter sensitivity |
| title | Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter |
| title_full | Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter |
| title_fullStr | Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter |
| title_full_unstemmed | Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter |
| title_short | Validation and Parameter Sensitivity Tests for Reconstructing Swell Field Based on an Ensemble Kalman Filter |
| title_sort | validation and parameter sensitivity tests for reconstructing swell field based on an ensemble kalman filter |
| topic | pseudo SAR observation EnKF swell field reconstruction parameter sensitivity |
| url | http://www.mdpi.com/1424-8220/16/12/2000 |
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