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    Surface Heat Fluxes over Global Oceans Exclusively from Satellite Observations

    Source: Monthly Weather Review:;2006:;volume( 134 ):;issue: 003::page 965
    Author:
    Singh, Randhir
    ,
    Kishtawal, C. M.
    ,
    Pal, P. K.
    ,
    Joshi, P. C.
    DOI: 10.1175/MWR3119.1
    Publisher: American Meteorological Society
    Abstract: A new approach is introduced for determining surface latent heat flux (LHF) and sensible heat flux (SHF) over the global oceans exclusively from satellite observations. Measurements of wind speed (U), sea surface temperature (SST), near surface specific humidity (Qa), and air?sea temperature difference (?T = SST ? Ta) are required for computing these fluxes by bulk formulas. To compute the heat fluxes exclusively from satellite data, U is obtained from Special Sensor Microwave Imager (SSM/I), SST is obtained from Advanced Very High Resolution Radiometer (AVHRR), empirical algorithm proposed earlier is used to compute ?T, and a new one is developed to estimate Qa. The developed empirical equation for Qa estimations is an extension of the authors? previous method. Compared to the Comprehensive Ocean?Atmosphere Data Set (COADS), the Qa retrieved by the previous approach had a negative bias of the order of more than 2 g kg?1 over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2 g kg?1 over the Arabian Sea and the Bay of Bengal during summertime. The new empirical equation takes into account these seasonal biases over the Gulf Stream, Kuroshio, and the Arabian Sea. Compared to COADS observations, the Qa retrieved from the developed empirical equation has global mean root mean square error (rmse), bias, and correlation of the order of 0.55, ?0.007, and 0.98 g kg?1, respectively. Compared to COADS, the satellite-derived monthly mean LHF has global mean rmse, bias, and correlation of the order of 20, 6, and 0.97 W m?2, respectively. Likewise, satellite-derived monthly mean SHF has global mean rmse, bias, and correlations of the order of 6, 0.4, and 0.98 W m?2, respectively. The monthly fields show that the spatial patterns and seasonal variability of satellite-derived latent and sensible heat fluxes are generally good in agreement with those of the COADS and earlier satellite-derived fluxes. Sixteen-year (January 1988?December 2003) datasets of surface heat fluxes and basic input parameters over the global oceans have been constructed using SSM/I and AVHRR data. This dataset has a spatial resolution of 1° ? 1° latitude?longitude and temporal resolution of one month. This unique dataset is constructed exclusively from satellite observations, and it can be obtained from the Meteorology and Oceanography Group Space Applications Centre.
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      Surface Heat Fluxes over Global Oceans Exclusively from Satellite Observations

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4229138
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    • Monthly Weather Review

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    contributor authorSingh, Randhir
    contributor authorKishtawal, C. M.
    contributor authorPal, P. K.
    contributor authorJoshi, P. C.
    date accessioned2017-06-09T17:27:41Z
    date available2017-06-09T17:27:41Z
    date copyright2006/03/01
    date issued2006
    identifier issn0027-0644
    identifier otherams-85666.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4229138
    description abstractA new approach is introduced for determining surface latent heat flux (LHF) and sensible heat flux (SHF) over the global oceans exclusively from satellite observations. Measurements of wind speed (U), sea surface temperature (SST), near surface specific humidity (Qa), and air?sea temperature difference (?T = SST ? Ta) are required for computing these fluxes by bulk formulas. To compute the heat fluxes exclusively from satellite data, U is obtained from Special Sensor Microwave Imager (SSM/I), SST is obtained from Advanced Very High Resolution Radiometer (AVHRR), empirical algorithm proposed earlier is used to compute ?T, and a new one is developed to estimate Qa. The developed empirical equation for Qa estimations is an extension of the authors? previous method. Compared to the Comprehensive Ocean?Atmosphere Data Set (COADS), the Qa retrieved by the previous approach had a negative bias of the order of more than 2 g kg?1 over the Gulf Stream and Kuroshio during winter but had a positive bias of more than 2 g kg?1 over the Arabian Sea and the Bay of Bengal during summertime. The new empirical equation takes into account these seasonal biases over the Gulf Stream, Kuroshio, and the Arabian Sea. Compared to COADS observations, the Qa retrieved from the developed empirical equation has global mean root mean square error (rmse), bias, and correlation of the order of 0.55, ?0.007, and 0.98 g kg?1, respectively. Compared to COADS, the satellite-derived monthly mean LHF has global mean rmse, bias, and correlation of the order of 20, 6, and 0.97 W m?2, respectively. Likewise, satellite-derived monthly mean SHF has global mean rmse, bias, and correlations of the order of 6, 0.4, and 0.98 W m?2, respectively. The monthly fields show that the spatial patterns and seasonal variability of satellite-derived latent and sensible heat fluxes are generally good in agreement with those of the COADS and earlier satellite-derived fluxes. Sixteen-year (January 1988?December 2003) datasets of surface heat fluxes and basic input parameters over the global oceans have been constructed using SSM/I and AVHRR data. This dataset has a spatial resolution of 1° ? 1° latitude?longitude and temporal resolution of one month. This unique dataset is constructed exclusively from satellite observations, and it can be obtained from the Meteorology and Oceanography Group Space Applications Centre.
    publisherAmerican Meteorological Society
    titleSurface Heat Fluxes over Global Oceans Exclusively from Satellite Observations
    typeJournal Paper
    journal volume134
    journal issue3
    journal titleMonthly Weather Review
    identifier doi10.1175/MWR3119.1
    journal fristpage965
    journal lastpage980
    treeMonthly Weather Review:;2006:;volume( 134 ):;issue: 003
    contenttypeFulltext
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