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    Toward the Use of Altimetry for Operational Seasonal Forecasting

    Source: Journal of Climate:;2000:;volume( 013 ):;issue: 017::page 3115
    Author:
    Segschneider, J.
    ,
    Anderson, D. L. T.
    ,
    Stockdale, T. N.
    DOI: 10.1175/1520-0442(2000)013<3115:TTUOAF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The TOPEX/Poseidon and ERS-1/2 satellites have now been observing sea level anomalies for a continuous time span of more than 6 yr. These sea level observations are first compared with tide gauge data and then assimilated into an ocean model that is used to initialize coupled ocean?atmosphere forecasts with a lead time of 6 months. Ocean analyses in which altimeter data are assimilated are compared with those from a no-assimilation experiment and with analyses in which subsurface temperature observations are assimilated. Analyses with altimeter data show variations of upper-ocean heat content similar to analyses using subsurface observations, whereas the ocean model has large errors when no data are assimilated. However, obtaining good results from the assimilation of altimeter data is not straightforward: it is essential to add a good mean sea level to the observed anomalies, to filter the sea level observations appropriately, to start the analyses from realistic initial temperature and salinity fields, and to assign appropriate weights for the analyzed increments. To assess the impact of altimeter data assimilation on the coupled system, ensemble hindcasts are initialized from ocean analyses in which either no data, subsurface temperatures, or sea level observations were assimilated. For each kind of ocean analysis, a five-member ensemble is started every 3 months from January 1993 to October 1997, adding up to 100 forecasts for each type. The predicted SST anomalies for the equatorial Pacific are intercompared between the experiments and against observations. The predicted anomalies are on average closer to observed values when forecasts are initialized from the ocean analysis using altimeter data than when initialized from the no-assimilation ocean analysis, and forecast errors appear to be only slightly larger than for forecasts initialized from ocean analyses using subsurface temperatures. However, even based on 100 coupled forecasts, the distinction between the two experiments that benefit from data assimilation is barely statistically significant. The verification should still be considered preliminary, because the period covered by the forecasts is only 5 yr, which is too short properly to sample ENSO variability. It is, nonetheless, encouraging that altimeter assimilation can improve the forecast skill to a level comparable to that obtained from using Tropical Ocean Atmosphere?expendable bathythermograph data.
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      Toward the Use of Altimetry for Operational Seasonal Forecasting

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4195689
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    contributor authorSegschneider, J.
    contributor authorAnderson, D. L. T.
    contributor authorStockdale, T. N.
    date accessioned2017-06-09T15:52:18Z
    date available2017-06-09T15:52:18Z
    date copyright2000/09/01
    date issued2000
    identifier issn0894-8755
    identifier otherams-5556.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4195689
    description abstractThe TOPEX/Poseidon and ERS-1/2 satellites have now been observing sea level anomalies for a continuous time span of more than 6 yr. These sea level observations are first compared with tide gauge data and then assimilated into an ocean model that is used to initialize coupled ocean?atmosphere forecasts with a lead time of 6 months. Ocean analyses in which altimeter data are assimilated are compared with those from a no-assimilation experiment and with analyses in which subsurface temperature observations are assimilated. Analyses with altimeter data show variations of upper-ocean heat content similar to analyses using subsurface observations, whereas the ocean model has large errors when no data are assimilated. However, obtaining good results from the assimilation of altimeter data is not straightforward: it is essential to add a good mean sea level to the observed anomalies, to filter the sea level observations appropriately, to start the analyses from realistic initial temperature and salinity fields, and to assign appropriate weights for the analyzed increments. To assess the impact of altimeter data assimilation on the coupled system, ensemble hindcasts are initialized from ocean analyses in which either no data, subsurface temperatures, or sea level observations were assimilated. For each kind of ocean analysis, a five-member ensemble is started every 3 months from January 1993 to October 1997, adding up to 100 forecasts for each type. The predicted SST anomalies for the equatorial Pacific are intercompared between the experiments and against observations. The predicted anomalies are on average closer to observed values when forecasts are initialized from the ocean analysis using altimeter data than when initialized from the no-assimilation ocean analysis, and forecast errors appear to be only slightly larger than for forecasts initialized from ocean analyses using subsurface temperatures. However, even based on 100 coupled forecasts, the distinction between the two experiments that benefit from data assimilation is barely statistically significant. The verification should still be considered preliminary, because the period covered by the forecasts is only 5 yr, which is too short properly to sample ENSO variability. It is, nonetheless, encouraging that altimeter assimilation can improve the forecast skill to a level comparable to that obtained from using Tropical Ocean Atmosphere?expendable bathythermograph data.
    publisherAmerican Meteorological Society
    titleToward the Use of Altimetry for Operational Seasonal Forecasting
    typeJournal Paper
    journal volume13
    journal issue17
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(2000)013<3115:TTUOAF>2.0.CO;2
    journal fristpage3115
    journal lastpage3138
    treeJournal of Climate:;2000:;volume( 013 ):;issue: 017
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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