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Keith D. Hutchison

Abstract

A multisensor, data fusion technique has been developed that merges the spectral signatures of ice and water clouds in Advanced Very High Resolution Radiometer (AVHRR) imagery with cloud-top pressure analyses derived from the High-Resolution Infrared Sounder (HIRS) to retrieve cloud-top phase and then cloud-top temperatures. While the performance of this algorithm has been encouraging, the specification of cloud-top phase is impacted by the absence of a unique spectral signature for either ice particles or water droplets in AVHRR/2 imagery and the inability to successfully identify very thin cirrus clouds, especially in daytime imagery, with automated cloud detection procedures. With the launch of the AVHRR/3 sensor, new spectral imagery in the 1.6-μm band will ultimately become available, which could help resolve these inadequacies. Thus, the utility of data in the 1.6-μm band is examined for improving the specification of cloud-top phase, while a derived 3.7-μm albedo channel is evaluated for enhancing the automated detection of very thin cirrus clouds in daytime imagery collected over a variety of surfaces. It is concluded that optimal performance of the cloud-top phase algorithm requires the use of both the 1.6- and 3.7-μm bands along with other AVHRR/2 channels. Unfortunately, since these data are not scheduled for simultaneous transmission in the Television Infrared Oberservational Satellite data stream, different implementation strategies are recommended for use with the transmission of the 3.7-μm channel, the 1.6-μm data, and both should they become available in the future.

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Keith D. Hutchison, Kenneth R. Hardy, and Bo-Cai Gao

Abstract

The accurate identification of optically thin cirrus clouds in global meteorological satellite imagery by automated cloud analysis algorithms is critical to environmental remote sensing studies, such as those related to climate change. While significant improvements have been realized with the arrival of multispectral, meteorological satellite imagery, collected by NOAA's Advanced Very High Resolution Radiometer (AVHRR), difficulties can be encountered when attempting to make pixel-level cloud decisions over large and diverse geographic areas found around the globe. These problems are due, in part, to the effects of atmospheric attenuation on cloud spectral signatures, caused primarily by variations in water vapor, since the signatures of water vapor and optically thin cirrus are similar in the nighttime AVHRR infrared channels. In this paper, the authors describe an improved thin-cirrus detection technique that uses the brightness temperature differences between AVHRR channel 3 and channel 5 along with total integrated water vapor information. It is concluded that algorithms must accurately compensate for the impact of water vapor on cloud spectral signatures for enhanced detection of optically thin cirrus clouds in nighttime AVHRR imagery.

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Keith D. Hutchison, Barbara D. Iisager, Thomas J. Kopp, and John M. Jackson

Abstract

A new approach is presented to distinguish between clouds and heavy aerosols with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. These new procedures exploit differences in both spectral and textural signatures between clouds and aerosols to isolate pixels originally classified as cloudy by the Visible/Infrared Imager/Radiometer Suite (VIIRS) cloud mask algorithm that in reality contains heavy aerosols. The procedures have been tested and found to accurately distinguish clouds from dust, smoke, volcanic ash, and industrial pollution over both land and ocean backgrounds in global datasets collected by NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This new methodology relies strongly upon data collected in the 0.412-μm bandpass, where smoke has a maximum reflectance in the VIIRS bands while dust simultaneously has a minimum reflectance. The procedures benefit from the VIIRS design, which is dual gain in this band, to avoid saturation in cloudy conditions. These new procedures also exploit other information available from the VIIRS cloud mask algorithm in addition to cloud confidence, including the phase of each cloudy pixel, which is critical to identify water clouds and restrict the use of spectral tests that would misclassify ice clouds as heavy aerosols. Comparisons between results from these new procedures, automated cloud analyses from VIIRS heritage algorithms, manually generated analyses, and MODIS imagery show the effectiveness of the new procedures and suggest that it is feasible to identify and distinguish between clouds and heavy aerosols in a single cloud mask algorithm.

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Keith D. Hutchison, Steve Marusa, John R. Henderson, Robert C. Kenley, Phillip C. Topping, William G. Uplinger, and John A. Twomey

Abstract

The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) requires improved accuracy in the retrieval of sea surface skin temperature (SSTS) from its Visible Infrared Imager Radiometer Suite (VIIRS) sensor over the capability to retrieve bulk sea surface temperature (SSTB) that has been demonstrated with currently operational National Oceanic and Atmospheric Administration (NOAA) satellites carrying the Advanced Very High Resolution Radiometer (AVHRR) sensor. Statistics show an existing capability to retrieve SSTB with a 1σ accuracy of about 0.8 K in the daytime and 0.6 K with nighttime data. During the NPOESS era, a minimum 1σ SSTS measurement uncertainty of 0.5 K is required during daytime and nighttime conditions, while 0.1 K is desired. Simulations have been performed, using PACEOS™ scene generation software and the multichannel sea surface temperature (MCSST) algorithms developed by NOAA, to better understand the implications of this more stringent requirement on algorithm retrieval methodologies and system design concepts. The results suggest that minimum NPOESS SSTS accuracy requirements may be satisfied with sensor NEΔT values of approximately 0.12 K, which are similar to the AVHRR sensor design specifications. However, error analyses of retrieved SSTB from AVHRR imagery suggest that these more stringent NPOESS requirements may be difficult to meet with existing MCSST algorithms. Thus, a more robust algorithm, a new retrieval methodology, or more stringent system characteristics may be needed to satisfy SSTS measurement uncertainty requirements during the NPOESS era. It is concluded that system-level simulations must accurately model all relevant phenomenology and any new algorithm development should be referenced against in situ observations of ocean surface skin temperatures.

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