Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-4445-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HMRFS–TP: long-term daily gap-free snow cover products over the Tibetan Plateau from 2002 to 2021 based on hidden Markov random field model
Yan Huang
CORRESPONDING AUTHOR
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jiahui Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jingyi Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Yelei Zhao
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Bailang Yu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Hongxing Liu
Department of Geography, the University of Alabama, Tuscaloosa, AL
35487, USA
Shujie Wang
Department of Geography, Earth and Environmental Systems Institute,
Pennsylvania State University, University Park, PA 16802, USA
Wanjia Xu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Jianping Wu
Key Laboratory of Geographic Information Science, Ministry of
Education, East China Normal University, Shanghai 200241, China
School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Zhaojun Zheng
National Satellite Meteorological Center, Beijing 100081, China
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The average time differences (∆T) between green-up date and snowmelt onset date from 2001–2018 on the Tibetan Plateau were 36.7 days. With the increasing spring mean temperature, spring total precipitation and daily snowmelt, ∆T became shorter. Besides, in arid and low-vegetation areas, ∆T is primarily influenced by snowmelt, whereas in humid and high-vegetation areas, temperature plays a dominant role.
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Current satellite-based sea-ice climate data records (CDRs) usually begin in October 1978 with the first multichannel microwave radiometer data. Here, we present a sea ice dataset based on the single-channel Electrical Scanning Microwave Radiometer (ESMR) that operated from 1972-1977 onboard NASA’s Nimbus 5 satellite. The data were processed using modern methods and include uncertainty estimations in order to provide an important, easy-to-use reference period of good quality for current CDRs.
Thomas Lavergne and Emily Down
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Jean Emmanuel Sicart, Victor Ramseyer, Ghislain Picard, Laurent Arnaud, Catherine Coulaud, Guilhem Freche, Damien Soubeyrand, Yves Lejeune, Marie Dumont, Isabelle Gouttevin, Erwan Le Gac, Frédéric Berger, Jean-Matthieu Monnet, Laurent Borgniet, Éric Mermin, Nick Rutter, Clare Webster, and Richard Essery
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Forests strongly modify the accumulation, metamorphism and melting of snow in midlatitude and high-latitude regions. Two field campaigns during the winters 2016–17 and 2017–18 were conducted in a coniferous forest in the French Alps to study interactions between snow and vegetation. This paper presents the field site, instrumentation and collection methods. The observations include forest characteristics, meteorology, snow cover and snow interception by the canopy during precipitation events.
Ying Chen, Ruibo Lei, Xi Zhao, Shengli Wu, Yue Liu, Pei Fan, Qing Ji, Peng Zhang, and Xiaoping Pang
Earth Syst. Sci. Data, 15, 3223–3242, https://doi.org/10.5194/essd-15-3223-2023, https://doi.org/10.5194/essd-15-3223-2023, 2023
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The sea ice concentration product derived from the Microwave Radiation Image sensors on board the FengYun-3 satellites can reasonably and independently identify the seasonal and long-term changes of sea ice, as well as extreme cases of annual maximum and minimum sea ice extent in polar regions. It is comparable with other sea ice concentration products and applied to the studies of climate and marine environment.
Adrià Fontrodona-Bach, Bettina Schaefli, Ross Woods, Adriaan J. Teuling, and Joshua R. Larsen
Earth Syst. Sci. Data, 15, 2577–2599, https://doi.org/10.5194/essd-15-2577-2023, https://doi.org/10.5194/essd-15-2577-2023, 2023
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We provide a dataset of snow water equivalent, the depth of liquid water that results from melting a given depth of snow. The dataset contains 11 071 sites over the Northern Hemisphere, spans the period 1950–2022, and is based on daily observations of snow depth on the ground and a model. The dataset fills a lack of accessible historical ground snow data, and it can be used for a variety of applications such as the impact of climate change on global and regional snow and water resources.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
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Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
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Short summary
Reliable snow cover information is important for understating climate change and hydrological cycling. We generate long-term daily gap-free snow products over the Tibetan Plateau (TP) at 500 m resolution from 2002 to 2021 based on the hidden Markov random field model. The accuracy is 91.36 %, and is especially improved during snow transitional period and over complex terrains. This dataset has great potential to study climate change and to facilitate water resource management in the TP.
Reliable snow cover information is important for understating climate change and hydrological...
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