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August 18, 2025Open Access

Evaluation of the operational MODIS cloud mask product for detecting cirrus clouds

ŻHŻaneta Nguyen HuuJagiellonian University AKAndrzej Z. KotarbaCentrum Badań Kosmicznych AWAgnieszka WypychJagiellonian University

Key Points

The MODIS cloud mask achieved an overall accuracy of 72.98% for detecting cirrus clouds during daytime, but only 59.50% accuracy at night.
Using CALIOP data as a benchmark, the study evaluated the effectiveness of several MODIS detection tests, especially the combined ATC method.
MODIS shows a higher probability of detection for cirrus clouds at 80.9% during the day, significantly dropping to 25.5% at night.
Findings indicate that MODIS ATC's reliability decreases at night, raising concerns for nighttime cirrus cloud identification.

Abstract

Abstract. All clouds influence the Earth's radiative budget, with their net radiative forcing being negative. However, high-level clouds warrant special attention due to their atmospheric warming effects. A comprehensive characterization of cirrus clouds requires information on their coverage, which can be obtained from various data types. Active satellite sensors (lidars) are presently the most accurate source for cirrus data, but their usefulness in climatological studies is limited (the narrow view and 16 d repeat cycle yield only ∼20 observations per year per region, often insufficient for climatological studies). On the contrary, passive data, which have been available for the past 40 years with sufficient temporal resolution for climatological research, are less effective at detecting cirrus clouds compared to active vertical profiling sensors. In this study, we assessed the utility of Moderate Resolution Imaging Spectroradiometer (MODIS) standard products for creating a cirrus mask by validating them against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Our objective was to determine how well the operational cloud mask from the MODIS Science Team can be used to infer the presence of cirrus clouds relative to data products derived from the highly sensitive CALIOP instrument by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Science Team. Using CALIOP data as the reference, we evaluated six tests for cirrus and high cloud detection considered in the MODIS cloud masking algorithm and their combination (all tests consolidation, ATC). Additionally, we applied classifications based on the cirrus definition from the International Satellite Cloud Climatology Project (ISCCP), which rely on retrieved MODIS cloud-top properties. These were used not as detection tests but as a classification scheme for comparative purposes. All other tests were applied directly to MODIS radiances. The study revealed that ATC was the most effective, resulting in an overall accuracy of 72.98 % (probability of detection 80.9 %, false alarm rate 34.9 %, Cohen's κ 0.46) during the daytime and 59.50 % at night (probability of detection 25.5 %, false alarm rate 6.9 %, Cohen's κ 0.19). However, its effectiveness was notably reduced during the nighttime compared to during the daytime. We conclude that the MODIS operational Cloud Mask after being modified into ATC is moderately suitable for creating a mask of high-level clouds and only during the daytime. During the nighttime, MODIS ATC fails to reliably report the presence of cirrus.

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