Articles | Volume 15, issue 4
https://doi.org/10.5194/amt-15-879-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-879-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2022
Research article |
| 21 Feb 2022
| 21 Feb 2022
Cloud-probability-based estimation of black-sky surface albedo from AVHRR data
Terhikki Manninen
CORRESPONDING AUTHOR
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00101, Finland
Emmihenna Jääskeläinen
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00101, Finland
Niilo Siljamo
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00101, Finland
Aku Riihelä
Meteorological Research, Finnish Meteorological Institute, Helsinki, 00101, Finland
Karl-Göran Karlsson
Atmospheric Remote Sensing Unit, Research Department, Swedish
Meteorological and Hydrological Institute, Norrköping, 60176
Norrköping, Sweden
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
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The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
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The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
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Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data....
