JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 94, NO. D15, PAGES 18,521-18,535, DECEMBER 20, 1989 Cloud Cover Analysis With Arctic AVHRR Data 1. Cloud Detection J. KEY AND R. G. BARRY Cooperative Institute for Research in Environmental Sciences and Department of Geography, University of Colorado, Boulder Automated analyses of satellite radiance data have concentrated heavily on low and middle latitude situations. Some of the design objectives for the International Satellite Cloud Climatology Project (ISCCP) cloud detection procedure such as spaceand time contrasts are used in a basic algorithm from which a polar cloud detection algorithm is developed. This algorithm is applied to Arctic data for January and July conditions. Both advanced very high resolution radiometer (AVHRR) and scanning multichannel microwave radiometer (SMMR) data are utilized. Synthetic AVHRR and SMMR data for a 7-day analysis period are also generated to provide a data set with known characteristics on which to test and validate algorithms. Modifications to the basic algorithm for polar conditions include the use of SMMR and SMMR-derived data sets for the estimation of surface parameters, elimination of the spatial test for the warmest pixel, the use of AVHRR channels I (0.7 lxm), 3 (3.7 •un), and 4 (11 lxm) in the temporal tests and the final multispectral thresholding, and the use of surface class characteristic values when clear- sky values cannot be obtained. Additionally, the differencebetween channels 3 and 4 is included in the temporal test for the detection of optically thin cloud. Greatest improvement in computed cloud fraction is realized over snow and ice surfaces; over open water or snow-free land, all versions perform similarly. Since the inclusion of SMMR for surface analysis and additional spectral channelsincreases the computational burden, its use may be justified only over snow and ice-covered regions. 1. INTRODUCTION The important role that polar processes play in the dynamics of global climate is widely recognized [Polar Research Board, 1984]. The variation of cloud amounts over polar ice sheets, sea ice, and ocean surfaces can have important effects on planetary albedo gradients and on surface energy exchanges [Barry et al., 1984; Shine and Crane, 1984]. Cloud cover exerts a major influence over the amount of solar and longwave radiation reaching the surface, and is linked to the sea ice through a series of radiative, dynamical, thermodynamic and hydro- logical feedback processes [Saltzman and Moritz, 1980]. Extent and thickness of sea ice influences oceanic heat loss and surface albedo which thereby influences global climate via the ice-albedo feedback [Budyko, 1969]. In turn, sea ice extent is controlled at least in part by radiative input from above. Previous research in global cloud analysis has made clear the need for cloud retrieval procedures specific to particular climate regimes [e.g., Rossow, 1989; Rossow et al., 1989a, b]. Current procedures for automated analyses of satellite radiance data have been developed for low and middle latitudes but their application to polar regions has been largely unexplored. Those that have been applied to polar data often fail in the polar regions for a number of reasons including: snow-coveredsurfaces are often as reflective as the clouds, the thermal structure of the troposphere is characterized by frequent isothermal and inversion layers; the polar darkness during winter makes data collected in the Copyright 1989 by the American Geophysical Union. Paper number 89JD01377. 0148-0227/89/89JD-01377505.00 visible portion of the spectrum largely unusable; satellite radiometers operate near one limit of their performance range due to extremely low surface temperatures and solar illuminations; there is a maximum concentration of aerosols in spring when the solar zenith angle is large increasing scattering of visible energy; and rapid small-scale variations, which in lower latitudes signify changes in cloud cover, occur on the surface as a result of changes in snow and ice distributions so that clear scenes are much more variable here than in lower latitude regions. Generally not all of these difficulties are encountered at any one location. However, because they can result in rapid small-scale variation from one location and time to another, a complex analysis method that can recognize and cope with these situations is necessary [World Meteorological Organization (WMO), 1987]. The purpose of this paper is to present a cloud detection algorithm specifically for Arctic A• data, based on ideas of the International Satellite Cloud Climatology Project (ISCCP) algorithm [Rossow et al., 1985]. The procedure used as a starting point in this paper is a test version that shares some of the important features of the final ISCCP version [Rossow et al., 1988], such as space and time contrast, but also has some significant differences. Both summer and winter data are examined, although emphasis is placed on the summer analyses. Additionally, emphasis is placed on Arctic analyses, although many of the ideas also apply to Antarctic data. 2. BACKGROUND Techniques for cloud detection from satellite data have been developed for use with visible, near- infrared, and thermal data, and have been based on threshold methods, radiative transfer models, and 18,521