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A Satellite-Based Summer Convective Cloud Frequency Analysis over the Southeastern United States

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  • 1 Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama
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Abstract

A convective cloud (CC) analysis is performed over the southeastern United States (SEUS) during June, July, and August 2006 and 2007, using data from the Geostationary Operational Environmental Satellite (GOES) visible and infrared sensors as processed by a satellite-based convection cloud mask and initiation algorithm. Six 5–7-day periods are analyzed between the times 1500 and 1900 UTC, representative of summertime conditions in the SEUS. The ~8.7 × 108 pixel database contains information on nonprecipitating CCs possessing various satellite-estimated attributes of cloud size, based on whether they meet set thresholds in eight infrared “interest fields.” CCs at ~1 km × 1 km pixel size in the GOES projection are evaluated in comparison with the land cover classes, elevation gradients, and normalized difference vegetation indices (NDVIs) beneath the CCs. The goals are to relate the frequency of occurrence of CCs to land surface properties, attempting to determine which of these three properties are most correlated with CCs. CCs are more likely to form over forests and dense vegetation and over higher gradients in elevation. Although forest cover classes are not the most common over the SEUS, CC occurrence increases disproportionately where steeply sloped topography and forests are coincident across large regions of the SEUS. Also, as NDVI increases, the percentage of CCs per land class also increases. Analysis of landscape heterogeneity (combining local variability in land classes, topography, and NDVI) shows that as it increases CC development is more widespread. Thus, lakes among forests and hilly topography intermingled with agricultural lands appear most conducive to high CC frequency.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu

Abstract

A convective cloud (CC) analysis is performed over the southeastern United States (SEUS) during June, July, and August 2006 and 2007, using data from the Geostationary Operational Environmental Satellite (GOES) visible and infrared sensors as processed by a satellite-based convection cloud mask and initiation algorithm. Six 5–7-day periods are analyzed between the times 1500 and 1900 UTC, representative of summertime conditions in the SEUS. The ~8.7 × 108 pixel database contains information on nonprecipitating CCs possessing various satellite-estimated attributes of cloud size, based on whether they meet set thresholds in eight infrared “interest fields.” CCs at ~1 km × 1 km pixel size in the GOES projection are evaluated in comparison with the land cover classes, elevation gradients, and normalized difference vegetation indices (NDVIs) beneath the CCs. The goals are to relate the frequency of occurrence of CCs to land surface properties, attempting to determine which of these three properties are most correlated with CCs. CCs are more likely to form over forests and dense vegetation and over higher gradients in elevation. Although forest cover classes are not the most common over the SEUS, CC occurrence increases disproportionately where steeply sloped topography and forests are coincident across large regions of the SEUS. Also, as NDVI increases, the percentage of CCs per land class also increases. Analysis of landscape heterogeneity (combining local variability in land classes, topography, and NDVI) shows that as it increases CC development is more widespread. Thus, lakes among forests and hilly topography intermingled with agricultural lands appear most conducive to high CC frequency.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu
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