Abstract

Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It has also far-reaching effects on ecosystems of the snow-covered areas. Therefore, global snow cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow cover information suitable for these needs. Highly variable surface and snow cover features suggest that operational snow extent algorithms may benefit from at least partly empirical approach, based on carefully analyzed training data. Here a new two-phase snow cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the Metop satellites of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the Metop/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud-masking. Pixels which can not be classified reliably to snow or snow-free, due to clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow depth and state of the ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data which may be suitable, e.g., for numerical weather prediction, hydrological modelling and other applications.

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