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Terhikki Manninen, Emmihenna Jääskeläinen, and Aku Riihelä

Abstract

Surface albedo, the fraction of incoming solar radiation reflected hemispherically by the surface, is an essential climate variable (ECV) directly related to the energy budget of Earth. The presence and properties of snow cover alter surface albedo significantly, with variability in temporal scales reaching from seasonal to diurnal. The diurnal variation of snow albedo is typically parameterized with the solar zenith angle, but it cannot take into account asymmetry with respect to midday. Using the solar azimuth angle instead is suggested, since especially in the melting season the snow albedo varies highly asymmetrically during the day. To derive a general time- and latitude-independent formula, the azimuth angle values are normalized. Baseline Surface Radiation Network data are used to derive an empirical formula for the diurnal variation of snow black-sky surface albedo. The overall accuracy is on the order of 0.02, and the relative accuracy is about 3%.

Open access
Niilo Siljamo, Otto Hyvärinen, Aku Riihelä, and Markku Suomalainen

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 also has far-reaching effects on ecosystems of 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 that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization 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 that cannot be classified reliably to snow or snow-free, because of 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 that may be suitable for numerical weather prediction, hydrological modeling, and other applications.

Open access