Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data

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Abstract

The Clouds and Earth's Radiant Energy System (CERES) has been monitoring clouds and radiation since 2000 using algorithms developed before 2002 for CERES Edition 2 (Ed2) products. To improve cloud amount accuracy, CERES Edition 4 (Ed4) applies revised algorithms and input data to Terra and Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) radiances. The Ed4 cloud mask uses 5-7 additional channels, new models for clear-sky ocean and snow/ice-surface radiances, and revised Terra MODIS calibrations. Mean Ed4 daytime and nighttime cloud amounts exceed their Ed2 counterparts by 0.035 and 0.068. Excellent consistency between average Aqua and Terra cloud fraction is found over nonpolar regions. Differences over polar regions are likely due to unresolved calibration discrepancies. Relative to Ed2, Ed4 cloud amounts agree better with those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). CALIPSO comparisons indicate that Ed4 cloud amounts are more than or as accurate as other available cloud mask systems. The Ed4 mask correctly identifies cloudy or clear areas 90%-96% of the time during daytime over nonpolar areas depending on the CALIPSO-MODIS averaging criteria. At night, the range is 88%-95%. Accuracy decreases over land. The polar day and night accuracy ranges are 90%-91% and 80%-81%, respectively. The mean Ed4 cloud fractions slightly exceed the average for seven other imager cloud masks. Remaining biases and uncertainties are mainly attributed to errors in Ed4 predicted clear-sky radiances. The resulting cloud fractions should help CERES produce a more accurate radiation budget and serve as part of a cloud property climate data record.

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APA

Trepte, Q. Z., Bedka, K. M., Chee, T. L., Minnis, P., Sun-Mack, S., Yost, C. R., … Smith, W. L. (2019). Global Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data. IEEE Transactions on Geoscience and Remote Sensing, 57(11), 9410–9449. https://doi.org/10.1109/TGRS.2019.2926620

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