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  • Author or Editor: Elizabeth E. Ebert x
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Elizabeth E. Ebert


The analysis of cloud cover in the polar regions from satellite data is more difficult than at other latitudes because the visible and thermal contrasts between the cloud cover and the underlying surface are frequently quite small. Pattern recognition has proven to be a useful tool in detecting and identifying several cloud types over snow and ice. Here a pattern recognition algorithm in combined with a hybrid histogram-spatial coherence (HHSC) scheme to derive cloud classification and fractional coverage, surface and cloud visible albedos and infrared brightness temperatures from multispectral AVHRR satellite imagery. The accuracy of the cloud fraction estimates were between 0.05 and 0.26, based on the mean absolute difference between the automated and manual nephanalyses of nearly 1000 training samples. The HHSC demonstrated greater accuracy at estimating cloud friction than three different threshold. methods. An important result is that the prior classification of a sample may significantly improve the accuracy of the analysis of cloud fraction, albedos and brightness temperatures over that of an unclassified sample.

The algorithm is demonstrated for a set of AVHRR imagery from the summertime Arctic. The automated classification and analysis are in good agreement with manual interpretation of the satellite imagery and with surface observations.

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Elizabeth E. Ebert and Gary T. Weymouth


Geostationary satellite observations can be used to distinguish potential rain-bearing clouds from nonraining areas, thereby providing surrogate observations of “no rain” over large areas. The advantages of including such observations are the provision of data in regions void of conventional rain gauges or radars, as well as the improved delineation of raining from nonraining areas in gridded rainfall analyses.

This paper describes a threshold algorithm for delineating nonraining areas using the difference between the daily minimum infrared brightness temperature and the climatological minimum surface temperature. Using a fixed difference threshold of −13 K, the accuracy of “no rain” detection (defined as the percentage of no-rain diagnoses that was correct) was 98%. The average spatial coverage was 45%, capturing about half of the observed space–time frequency of no rain over Australia. By delineating cool, moderate, and warm threshold areas, the average spatial coverage was increased to 54% while maintaining the same level of accuracy.

The satellite no-rain observations were sampled to a density consistent with the existing gauge network, then added to the real-time gauge observations and analyzed using the Bureau of Meteorology’s operational three-pass Barnes objective rainfall analysis scheme. When verified against independent surface rainfall observations, the mean bias in the satellite-augmented analyses was roughly half of bias in the gauge-only analyses. The most noticeable impact of the additional satellite observations was a 66% reduction in the size of the data-void regions.

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