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    Nimbus-7 Global Cloud Climatology. part I: Algorithms and Validation

    Source: Journal of Climate:;1988:;volume( 001 ):;issue: 005::page 445
    Author:
    Stowe, L. L.
    ,
    Wellemeyer, C. G.
    ,
    Yeh, H. Y. M.
    ,
    Eck, T. F.
    ,
    The Nimbus-7 CLOUD DATA PROCecessing TEAM
    DOI: 10.1175/1520-0442(1988)001<0445:NGCCPI>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: Data from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS), both aboard the Nimbus-7 satellite, are used to determine cloudiness parameters for the globe. The 11.5 ?m THIR radiances and the 0.36 ?m and 0.38 ?m TOMS reflectivities, along with concurrent surface temperature data from the Air Force 3-D nephanalysis, are the primary data sources. They are processed by an algorithm that determines total cloud amount, cloud amount in three altitude categories, cirrus cloud, deep convective cloud, warm cloud, and the radiance of radiation emitted by the clouds. and the underlying surface. The algorithm is of the bispectral threshold type, which yields two independent estimates of total cloud, one from the infrared algorithm and one from the UV reflectivity algorithm. For the daytime observations (local noon at the equator), these two independent estimates are combined to determine a composite estimate, while at night (local midnight at the equator), only the infrared threshold algorithm is used in the estimate. Quantitative validation of total cloud amount was performed by comparing the algorithm results with estimates derived by an analyst interpreting geosynchronous satellite (GOES) images, along with auxiliary meteorological data. It has been concluded that the systematic errors of the Nimbus-7 total cloud amount algorithm relative to the analyst are less than 10%, and that the random errors of daily estimates range between 7% and 16%, day or night. These empirical results are consistent with results from a theoretical sensitivity study. Qualitative validation has also been performed by making comparisons with GOES visible and infrared images for specific days. Results indicate that the TOMS cloud estimates improve the IR algorithm estimates of low cloud amount and provide for the identification of cirrus and deep convective cloud, but cloud amounts over humid tropical regions tend to be overestimated even with the use of TOMS. These results suggest that the spatial and temporal characteristics of daily and monthly averaged global cloud cover, including cirrus acid deep convective cloud types, which are presented in Part II, are generally well represented by the Nimbus-7 dataset, which covers a six-year period from April 1979 to March 1985.
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      Nimbus-7 Global Cloud Climatology. part I: Algorithms and Validation

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    contributor authorStowe, L. L.
    contributor authorWellemeyer, C. G.
    contributor authorYeh, H. Y. M.
    contributor authorEck, T. F.
    contributor authorThe Nimbus-7 CLOUD DATA PROCecessing TEAM
    date accessioned2017-06-09T15:07:27Z
    date available2017-06-09T15:07:27Z
    date copyright1988/05/01
    date issued1988
    identifier issn0894-8755
    identifier otherams-3501.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4172857
    description abstractData from the Temperature Humidity Infrared Radiometer (THIR) and the Total Ozone Mapping Spectrometer (TOMS), both aboard the Nimbus-7 satellite, are used to determine cloudiness parameters for the globe. The 11.5 ?m THIR radiances and the 0.36 ?m and 0.38 ?m TOMS reflectivities, along with concurrent surface temperature data from the Air Force 3-D nephanalysis, are the primary data sources. They are processed by an algorithm that determines total cloud amount, cloud amount in three altitude categories, cirrus cloud, deep convective cloud, warm cloud, and the radiance of radiation emitted by the clouds. and the underlying surface. The algorithm is of the bispectral threshold type, which yields two independent estimates of total cloud, one from the infrared algorithm and one from the UV reflectivity algorithm. For the daytime observations (local noon at the equator), these two independent estimates are combined to determine a composite estimate, while at night (local midnight at the equator), only the infrared threshold algorithm is used in the estimate. Quantitative validation of total cloud amount was performed by comparing the algorithm results with estimates derived by an analyst interpreting geosynchronous satellite (GOES) images, along with auxiliary meteorological data. It has been concluded that the systematic errors of the Nimbus-7 total cloud amount algorithm relative to the analyst are less than 10%, and that the random errors of daily estimates range between 7% and 16%, day or night. These empirical results are consistent with results from a theoretical sensitivity study. Qualitative validation has also been performed by making comparisons with GOES visible and infrared images for specific days. Results indicate that the TOMS cloud estimates improve the IR algorithm estimates of low cloud amount and provide for the identification of cirrus and deep convective cloud, but cloud amounts over humid tropical regions tend to be overestimated even with the use of TOMS. These results suggest that the spatial and temporal characteristics of daily and monthly averaged global cloud cover, including cirrus acid deep convective cloud types, which are presented in Part II, are generally well represented by the Nimbus-7 dataset, which covers a six-year period from April 1979 to March 1985.
    publisherAmerican Meteorological Society
    titleNimbus-7 Global Cloud Climatology. part I: Algorithms and Validation
    typeJournal Paper
    journal volume1
    journal issue5
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(1988)001<0445:NGCCPI>2.0.CO;2
    journal fristpage445
    journal lastpage470
    treeJournal of Climate:;1988:;volume( 001 ):;issue: 005
    contenttypeFulltext
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