Abstract
Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection procedure classifies instantaneous fields of view (IFOVs) as either “confident clear”, “probably clear”, “probably cloudy”, or “confident cloudy”. The cloud amount calculation requires quantitative cloud fractions to be assigned to these classes. The operational procedure used by the MODIS Science Team assumes that confident clear and probably clear IFOVs are cloud-free (cloud fraction 0 %), while the remaining categories are completely filled with clouds (cloud fraction 100 %). This study demonstrates that this “best-guess” approach is unreliable, especially on a regional/local scale. We use data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument flown on the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission, collocated with Aqua MODIS IFOV. Based on 33 793 648 paired observations acquired in January and July 2015, we conclude that actual cloud fractions to be associated with MODIS cloud mask categories are 21.5 %, 27.7 %, 66.6 %, and 94.7 %. Spatial variability is significant, even within a single MODIS algorithm path, and the operational approach introduces uncertainties of up to 30 % of cloud amount, notably in polar regions at night, and in selected locations over the Northern Hemisphere (e.g. China, the north-west coast of Africa, and eastern parts of the United States). Consequently, applications of MODIS data on a regional/local scale should first assess the extent of the uncertainty. We suggest using CALIPSO-based cloud fractions to improve MODIS cloud amount estimates. This approach can also be used for Terra MODIS data, and other passive cloud imagers, where the footprint is collocated with CALIPSO.
Highlights
Cloud plays a key role in distributing solar energy in the Earth’s atmosphere (Trenberth et al, 2009)
The first key point that emerged from the matched Moderate Resolution Imaging Spectroradiometer (MODIS)– lidar observations was the accuracy of MODIS cloud detections
Clouds missed by MODIS but detected by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were most frequent during the polar night, regardless of the hemisphere (Fig. 1c, d)
Summary
Cloud plays a key role in distributing solar energy in the Earth’s atmosphere (Trenberth et al, 2009). Depending on its frequency and physical properties, cloud can both heat (greenhouse effect: +30 W m−2) and cool (albedo effect: −48 W m−2) the atmosphere. Their net effect on the planetary radiation budget is negative, meaning the Earth would be warmer if all cloud disappeared (Ramanathan and Kiehl, 2006). The Global Climate Observing System identifies 13 essential climate variables. This set of critical environmental parameters characterizes the Earth’s climate (Hollmann et al, 2013); they include cloud properties, but they highlight that our knowledge of cloud relies largely on satellite remote sensing. Input data include at-sensor registered radiances, along with other auxiliary information that aims to maximize cloud detection
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