- 1Jagiellonian University, Institute of Geography and Spatial Managemen, Department of Climatology, Cracow, Poland; Jagiellonian University, Doctoral School of Exact and Natural Sciences, Cracow, Poland (zaneta.nguyen_huu@uj.edu.pl)
- 2Space Research Centre, Polish Academy of Sciences, Warsaw, Poland
- 3Jagiellonian University, Institute of Geography and Spatial Managemen, Department of Climatology, Cracow, Poland
Clouds influence Earth's radiative budget, with high-level clouds playing a critical role in atmospheric warming. Accurate cirrus cloud characterization is crucial and can be achieved using different data sources. Active satellite sensors are presently the most accurate source for cirrus data, but their usefulness in climatological studies is limited. In contrast, passive data, available for the past 40 years, offers sufficient temporal resolution but struggles to detect cirrus clouds effectively. This study evaluates MODIS cloud masking algorithms for cirrus detection, comparing their performance to CALIOP data. Specifically, we aim to assess whether MODIS cloud detection tests used to generate MYD35 operational data can be re-used for masking of cirrus.
Using CALIOP data as the reference, we evaluated six tests for cirrus detection considered in MODIS cloud masking algorithm and their combination (ATC). Additionally we applied two ISCCP-originating tests: ISCCP3.6 and ISCCP23 tests.
Our results showed that the ATC method outperforms others, with 72.98% accuracy during the day 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 kappa coefficient of 0.46 and 0.19 respectively). The ATC test offers a reliable option for creating high-level cloud masks.
How to cite: Nguyen Huu, Ż., Kotarba, A. Z., and Wypych, A.: Cloudy Planet: Cirrus Detection with MODIS Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8784, https://doi.org/10.5194/egusphere-egu25-8784, 2025.
