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- Author or Editor: E. Paul Mcclain x
- Journal of Atmospheric and Oceanic Technology x
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Abstract
An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 × 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real-time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded that the algorithm does well at classifying perfectly clear pixel arrays, except at high latitudes in their winter seasons. It also has difficulties with classifications over some desert and mountainous regions and when viewing regions of ocean specular reflection. Generally, the CLAVR-1 fractional cloud amounts, when computed using a statistically equivalent spatial coherence method, agree to within about 0.05–0.10 of image/analyst estimates on average. There is a tendency for CLAVR-1 to underestimate cloud amount when it is large and to overestimate it when small.
Abstract
An algorithm for the remote sensing of global cloud cover using multispectral radiance measurements from the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites has been developed. The CLAVR-1 (Clouds from AVHRR-Phase I) algorithm classifies 2 × 2 pixel arrays from the Global Area Coverage (GAC) 4-km-resolution archived database into CLEAR, MIXED, and CLOUDY categories. The algorithm uses a sequence of multispectral contrast, spectral, and spatial signature threshold tests to perform the classification. The various tests and the derivation of their thresholds are presented. CLAVR-1 has evolved through experience in applying it to real-time NOAA-11 data, and retrospectively through the NOAA AVHRR Pathfinder Atmosphere project, where 16 years of data have been reprocessed into cloud, radiation budget, and aerosol climatologies. The classifications are evaluated regionally with image analysis, and it is concluded that the algorithm does well at classifying perfectly clear pixel arrays, except at high latitudes in their winter seasons. It also has difficulties with classifications over some desert and mountainous regions and when viewing regions of ocean specular reflection. Generally, the CLAVR-1 fractional cloud amounts, when computed using a statistically equivalent spatial coherence method, agree to within about 0.05–0.10 of image/analyst estimates on average. There is a tendency for CLAVR-1 to underestimate cloud amount when it is large and to overestimate it when small.
Abstract
An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.
Abstract
An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.
Abstract
This paper deals with the problem of aerosol optical thickness (τ A ) retrieval using sun-photometer measurements. The results of the theoretical analysis and computer processing of the dataset collected during the 40th cruise of the R/V Akademik Vernadsky are presented. Accuracy of retrieved τ A is investigated in detail. It is concluded that 1) the τ A measurements from the three shortest wavelength channels are sufficiently accurate (0.02–0.03) for evaluation of the NOAA Advanced Very High Resolution Radiometer aerosol optical thickness operational product; 2) serious discrepancies exist between observation and theory for the two longest wavelength channels, which preclude their use in aerosol optical property studies. Further investigations are required, with emphasis on the computation of atmospheric gaseous absorption, before these channels can be used. Shipboard τ A will be compared with satellite data from the NOAA/National Environment Satellite Data and Information Service in a subsequent paper.
Abstract
This paper deals with the problem of aerosol optical thickness (τ A ) retrieval using sun-photometer measurements. The results of the theoretical analysis and computer processing of the dataset collected during the 40th cruise of the R/V Akademik Vernadsky are presented. Accuracy of retrieved τ A is investigated in detail. It is concluded that 1) the τ A measurements from the three shortest wavelength channels are sufficiently accurate (0.02–0.03) for evaluation of the NOAA Advanced Very High Resolution Radiometer aerosol optical thickness operational product; 2) serious discrepancies exist between observation and theory for the two longest wavelength channels, which preclude their use in aerosol optical property studies. Further investigations are required, with emphasis on the computation of atmospheric gaseous absorption, before these channels can be used. Shipboard τ A will be compared with satellite data from the NOAA/National Environment Satellite Data and Information Service in a subsequent paper.
