Open AccessNWCSAF AVHRR Cloud Detection and Analysis Using Dynamic Thresholds and Radiative Transfer Modeling. Part I: Algorithm Description
Author(s) -
Adam Dybbroe,
KarlGöran Karlsson,
Anke Thoss
Publication year - 2005
Publication title -
journal of applied meteorology
Language(s) - English
Resource type - Journals
eISSN - 1520-0450
pISSN - 0894-8763
DOI - 10.1175/jam-2188.1
Subject(s) - cloud computing , remote sensing , subpixel rendering , cloud top , advanced very high resolution radiometer , radiative transfer , environmental science , cloud cover , meteorology , radiance , computer science , algorithm , cloud fraction , atmospheric radiative transfer codes , numerical weather prediction , satellite , pixel , geology , artificial intelligence , geography , physics , quantum mechanics , operating system , aerospace engineering , engineering
New methods and software for cloud detection and classification at high and midlatitudes using Advanced Very High Resolution Radiometer (AVHRR) data are developed for use in a wide range of meteorological, climatological, land surface, and oceanic applications within the Satellite Application Facilities (SAFs) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), including the SAF for Nowcasting and Very Short Range Forecasting Applications (NWCSAF) project. The cloud mask employs smoothly varying (dynamic) thresholds that separate fully cloudy or cloud-contaminated fields of view from cloud-free conditions. Thresholds are adapted to the actual state of the atmosphere and surface and the sun–satellite viewing geometry using cloud-free radiative transfer model simulations. Both the cloud masking and the cloud-type classification are done using sequences of grouped threshold tests that employ both spectral and textural features. The cloud-type classification divides the cloudy pixels into 10 different categories: 5 opaque cloud types, 4 semitransparent clouds, and 1 subpixel cloud category. The threshold method is fuzzy in the sense that the distances in feature space to the thresholds are stored and are used to determine whether to stop or to continue testing. They are also used as a quality indicator of the final output. The atmospheric state should preferably be taken from a short-range NWP model, but the algorithms can also run with climatological fields as input.