Journal Article

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Tzallas, Vasileios, Stengel, Martin, Hünerbein, Anja, Benas, Nikos, Macke, Andreas, Meirink, Jan Fokke, Trentmann, Jörg

2022

Overview

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).

• Publisher: MDPI AG
• Subject: Algorithms / Climate / Climate monitoring / Climate system / Climatic data / cloud regimes / Clouds / Cluster analysis / Clustering / CRAAS / Cumulus clouds / Datasets / Europe / Fog / Geographical distribution / Histograms / Meteorological satellites / Optical properties / Optical thickness / Remote sensing / Variability / Vector quantization / Weather patterns / weather types