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    An Adaptive Optimal Interpolation Based on Analog Forecasting: Application to SSH in the Gulf of Mexico

    Source: Journal of Atmospheric and Oceanic Technology:;2020:;volume( 37 ):;issue: 009::page 1697
    Author:
    Zhen, Yicun;Tandeo, Pierre;Leroux, Stéphanie;Metref, Sammy;Penduff, Thierry;Le Sommer, Julien
    DOI: 10.1175/JTECH-D-20-0001.1
    Publisher: American Meteorological Society
    Abstract: Because of the irregular sampling pattern of raw altimeter data, many oceanographic applications rely on information from sea surface height (SSH) products gridded on regular grids where gaps have been filled with interpolation. Today, the operational SSH products are created using the simple, but robust, optimal interpolation (OI) method. If well tuned, the OI becomes computationally cheap and provides accurate results at low resolution. However, OI is not adapted to produce high-resolution and high-frequency maps of SSH. To improve the interpolation of SSH satellite observations, a data-driven approach (i.e., constructing a dynamical forecast model from the data) was recently proposed: analog data assimilation (AnDA). AnDA adaptively chooses analog situations from a catalog of SSH scenes—originating from numerical simulations or a large database of observations—which allow the temporal propagation of physical features at different scales, while each observation is assimilated. In this article, we review the AnDA and OI algorithms and compare their skills in numerical experiments. The experiments are observing system simulation experiments (OSSE) on the Lorenz-63 system and on an SSH reconstruction problem in the Gulf of Mexico. The results show that AnDA, with no necessary tuning, produces comparable reconstructions as does OI with tuned parameters. Moreover, AnDA manages to reconstruct the signals at higher frequencies than OI. Finally, an important additional feature for any interpolation method is to be able to assess the quality of its reconstruction. This study shows that the standard deviation estimated by AnDA is flow dependent, hence more informative on the reconstruction quality, than the one estimated by OI.
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      An Adaptive Optimal Interpolation Based on Analog Forecasting: Application to SSH in the Gulf of Mexico

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    contributor authorZhen, Yicun;Tandeo, Pierre;Leroux, Stéphanie;Metref, Sammy;Penduff, Thierry;Le Sommer, Julien
    date accessioned2022-01-30T18:09:02Z
    date available2022-01-30T18:09:02Z
    date copyright9/10/2020 12:00:00 AM
    date issued2020
    identifier issn0739-0572
    identifier otherjtechd200001.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4264571
    description abstractBecause of the irregular sampling pattern of raw altimeter data, many oceanographic applications rely on information from sea surface height (SSH) products gridded on regular grids where gaps have been filled with interpolation. Today, the operational SSH products are created using the simple, but robust, optimal interpolation (OI) method. If well tuned, the OI becomes computationally cheap and provides accurate results at low resolution. However, OI is not adapted to produce high-resolution and high-frequency maps of SSH. To improve the interpolation of SSH satellite observations, a data-driven approach (i.e., constructing a dynamical forecast model from the data) was recently proposed: analog data assimilation (AnDA). AnDA adaptively chooses analog situations from a catalog of SSH scenes—originating from numerical simulations or a large database of observations—which allow the temporal propagation of physical features at different scales, while each observation is assimilated. In this article, we review the AnDA and OI algorithms and compare their skills in numerical experiments. The experiments are observing system simulation experiments (OSSE) on the Lorenz-63 system and on an SSH reconstruction problem in the Gulf of Mexico. The results show that AnDA, with no necessary tuning, produces comparable reconstructions as does OI with tuned parameters. Moreover, AnDA manages to reconstruct the signals at higher frequencies than OI. Finally, an important additional feature for any interpolation method is to be able to assess the quality of its reconstruction. This study shows that the standard deviation estimated by AnDA is flow dependent, hence more informative on the reconstruction quality, than the one estimated by OI.
    publisherAmerican Meteorological Society
    titleAn Adaptive Optimal Interpolation Based on Analog Forecasting: Application to SSH in the Gulf of Mexico
    typeJournal Paper
    journal volume37
    journal issue9
    journal titleJournal of Atmospheric and Oceanic Technology
    identifier doi10.1175/JTECH-D-20-0001.1
    journal fristpage1697
    journal lastpage1711
    treeJournal of Atmospheric and Oceanic Technology:;2020:;volume( 37 ):;issue: 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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