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

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  • 1 NOAA/National Environmental Satellite, Data, and Information Service, Washington, DC
  • | 2 ST Systems Corporation, Lanham, Maryland
  • | 3 Science Applications Research, Lanham, Maryland
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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 μm THIR radiances and the 0.36 μm and 0.38 μm 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.

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 μm THIR radiances and the 0.36 μm and 0.38 μm 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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