An assessment of diurnal and seasonal cloud cover changes over the Hawaiian islands using Terra and Aqua MODIS

Abstract

Knowledge of cloud cover patterns is important in effective management of natural resources, climate change studies, and remote sensing applications. The objective of this study was to develop a comprehensive understanding of the spatial, seasonal, and diurnal patterns in cloud cover frequency over the Hawaiian Islands using high resolution image data (every 6 hours at 1 km) from the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua satellite platforms. The Terra and Aqua MODIS cloud mask products, which provide the likelihood that clouds obscure a given 1 km pixel, were obtained for the entire MODIS time series (10+ years) over the main Hawaiian Islands. Monthly statistics were generated from the daily cloud mask data, including mean cloud cover frequency at the four overpass times of ~11am, 2pm, 11pm, and 2am. The derived monthly statistics showed several significant trends that were consistent with generally known rainfall patterns. First, cloud cover frequency increased with height above the lifting condensation level (roughly 600 m) until the trade wind inversion (mean elevation 2100-2200 m), above which it was generally clear. Second, cloud cover frequency was higher on the windward (northeastern) sides of mountains than on the leeward (southwestern sides) of the mountains on all of the main Hawaiian Islands. Third, irrespective of season, mean cloud cover frequency was higher in the afternoon than in the morning and higher in the daytime than in the nighttime. Lastly, the dry season months (May--Oct) were less cloudy than the wet season months (Nov--Apr), and this pattern was stronger at nighttime than during the daytime. The derived statistics also revealed a unique and unexpected trend of anomalously low cloud cover in December and January over the Hawaiian Islands. The monthly time series produced in this thesis is the first high spatial resolution cloud cover dataset in Hawaiʻi and is expected to be a useful resource in a variety of applications, including the management of energy and fresh water resources.