Effectiveness of Cirrus Detection with MODIS Cloud Mask data

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 requires information on cloud coverage, obtainable from various data types. Active satellite sensors are presently the most accurate source for cirrus data, but their usefulness in climatological studies is limited. On the contrary, passive data, available for the past 40 years with sufficient temporal resolution for climatological research, were not specifically designed for cirrus detection. In this study, we assessed the utility of MODIS standard products for creating a cirrus mask by validating them against CALIOP data. Our objective was to determine if a MODIS product exists that detects cirrus with the same accuracy as CALIOP.

Using CALIOP data as the reference, we evaluated six tests for cirrus detection considered in MODIS cloud masking algorithm and their combination (ALL TESTS CONSOLIDATION, ATC). Additionally we applied two ISCCP-originating tests: ISCCP3.6 and ISCCP23 tests. All tests have been applied to MODIS radiances.

Study revealed that ATC test was the most effective resulting with the overall accuracy of 72.98 % during daytime and 59.50 % at night (probability of detection: 80.87 % and 25.46 %, false alarm rate of 34.86 % and 6.90 %, and Cohen's kapppa coefficient of 0.46 and 0.19 respectively). However, its effectiveness was notably reduced during nighttime compared to daytime. We conclude that the test is suitable for creating a mask of high-level clouds.