Articles | Volume 15, issue 6
https://doi.org/10.5194/tc-15-2969-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-2969-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Kari Luojus
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Juha Lemmetyinen
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Jouni Pulliainen
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Mikko Moisander
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Matias Takala
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki,
Finland
Related authors
Pinja Venäläinen, Colleen Mortimer, Kari Luojus, Lawrence Mudryk, Matias Takala, and Jouni Pulliainen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3643, https://doi.org/10.5194/egusphere-2024-3643, 2025
Short summary
Short summary
Satellite data-based estimation of large SWE values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Annett Bartsch, Rodrigue Tanguy, Helena Bergstedt, Clemens von Baeckmann, Hans Tømmervik, Marc Macias-Fauria, Juha Lemmentiynen, Kimmo Rautiainen, Chiara Gruber, and Bruce C. Forbes
EGUsphere, https://doi.org/10.5194/egusphere-2025-1358, https://doi.org/10.5194/egusphere-2025-1358, 2025
Short summary
Short summary
We identified similarities between sea ice dynamics and conditions on land across the Arctic. Significant correlations north of 60°N was more common for snow water equivalent and permafrost ground temperature than for the vegetation parameters. Changes across all the different parameters could be specifically determined for Eastern Siberia. The results provide a baseline for future studies on common drivers of essential climate parameters and causative effects across the Arctic.
Kimmo Rautiainen, Manu Holmberg, Juval Cohen, Arnaud Mialon, Mike Schwank, Juha Lemmetyinen, Antonio de la Fuente, and Yann Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-68, https://doi.org/10.5194/essd-2025-68, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The SMOS Soil Freeze Thaw State product uses satellite data to monitor seasonal soil freezing and thawing globally, with a focus on high latitude regions. This is important for understanding greenhouse gas emissions, as frozen soil is associated with methane release. The product provides accurate data on key events such as the first day of soil freezing in autumn, helping scientists to study climate change, ecosystem dynamics and its impact on our planet.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Pinja Venäläinen, Colleen Mortimer, Kari Luojus, Lawrence Mudryk, Matias Takala, and Jouni Pulliainen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3643, https://doi.org/10.5194/egusphere-2024-3643, 2025
Short summary
Short summary
Satellite data-based estimation of large SWE values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
Ella Kivimäki, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
EGUsphere, https://doi.org/10.5194/egusphere-2025-249, https://doi.org/10.5194/egusphere-2025-249, 2025
Short summary
Short summary
We investigate how environmental variables influencing natural methane fluxes explain the large-scale seasonal variability of satellite-observed methane at Northern high latitudes. Our findings show that soil moisture, snow cover, and soil temperature have the strongest influence, with snowmelt playing a surprisingly significant role, likely through soil isolation and wetting. This study highlights the value of multi-satellite observations for understanding large-scale wetland emissions.
David Brodylo, Lauren V. Bosche, Ryan R. Busby, Elias J. Deeb, Thomas A. Douglas, and Juha Lemmetyinen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3936, https://doi.org/10.5194/egusphere-2024-3936, 2025
Short summary
Short summary
We combined field-based snow depth and snow water equivalent (SWE) measurements, remote sensing data, and machine learning to estimate snow depth and SWE over a 10 km2 local scale area in Sodankylä, Finland. Associations were found for snow depth and SWE with carbon- and mineral-based forest surface soils, alongside dry and wet peatbogs. This approach to upscale field-based snow depth and SWE measurements to a local scale can be used in regions that regularly experience snowfall.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Justin Murfitt, Claude Duguay, Ghislain Picard, and Juha Lemmetyinen
The Cryosphere, 18, 869–888, https://doi.org/10.5194/tc-18-869-2024, https://doi.org/10.5194/tc-18-869-2024, 2024
Short summary
Short summary
This research focuses on the interaction between microwave signals and lake ice under wet conditions. Field data collected for Lake Oulujärvi in Finland were used to model backscatter under different conditions. The results of the modelling likely indicate that a combination of increased water content and roughness of different interfaces caused backscatter to increase. These results could help to identify areas where lake ice is unsafe for winter transportation.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Carolina Voigt, Nick Rutter, Paul Mann, Jean-Daniel Sylvain, and Alexandre Roy
Biogeosciences, 20, 5087–5108, https://doi.org/10.5194/bg-20-5087-2023, https://doi.org/10.5194/bg-20-5087-2023, 2023
Short summary
Short summary
We present an analysis of soil CO2 emissions in boreal and tundra regions during the non-growing season. We show that when the soil is completely frozen, soil temperature is the main control on CO2 emissions. When the soil is around the freezing point, with a mix of liquid water and ice, the liquid water content is the main control on CO2 emissions. This study highlights that the vegetation–snow–soil interactions must be considered to understand soil CO2 emissions during the non-growing season.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
Short summary
Short summary
We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Alex Mavrovic, Oliver Sonnentag, Juha Lemmetyinen, Jennifer L. Baltzer, Christophe Kinnard, and Alexandre Roy
Biogeosciences, 20, 2941–2970, https://doi.org/10.5194/bg-20-2941-2023, https://doi.org/10.5194/bg-20-2941-2023, 2023
Short summary
Short summary
This review supports the integration of microwave spaceborne information into carbon cycle science for Arctic–boreal regions. The microwave data record spans multiple decades with frequent global observations of soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties, and land cover. This record holds substantial unexploited potential to better understand carbon cycle processes.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
Short summary
Short summary
Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Chao Yan, Yicheng Shen, Dominik Stolzenburg, Lubna Dada, Ximeng Qi, Simo Hakala, Anu-Maija Sundström, Yishuo Guo, Antti Lipponen, Tom V. Kokkonen, Jenni Kontkanen, Runlong Cai, Jing Cai, Tommy Chan, Liangduo Chen, Biwu Chu, Chenjuan Deng, Wei Du, Xiaolong Fan, Xu-Cheng He, Juha Kangasluoma, Joni Kujansuu, Mona Kurppa, Chang Li, Yiran Li, Zhuohui Lin, Yiliang Liu, Yuliang Liu, Yiqun Lu, Wei Nie, Jouni Pulliainen, Xiaohui Qiao, Yonghong Wang, Yifan Wen, Ye Wu, Gan Yang, Lei Yao, Rujing Yin, Gen Zhang, Shaojun Zhang, Feixue Zheng, Ying Zhou, Antti Arola, Johanna Tamminen, Pauli Paasonen, Yele Sun, Lin Wang, Neil M. Donahue, Yongchun Liu, Federico Bianchi, Kaspar R. Daellenbach, Douglas R. Worsnop, Veli-Matti Kerminen, Tuukka Petäjä, Aijun Ding, Jingkun Jiang, and Markku Kulmala
Atmos. Chem. Phys., 22, 12207–12220, https://doi.org/10.5194/acp-22-12207-2022, https://doi.org/10.5194/acp-22-12207-2022, 2022
Short summary
Short summary
Atmospheric new particle formation (NPF) is a dominant source of atmospheric ultrafine particles. In urban environments, traffic emissions are a major source of primary pollutants, but their contribution to NPF remains under debate. During the COVID-19 lockdown, traffic emissions were significantly reduced, providing a unique chance to examine their relevance to NPF. Based on our comprehensive measurements, we demonstrate that traffic emissions alone are not able to explain the NPF in Beijing.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Juha Lemmetyinen, Juval Cohen, Anna Kontu, Juho Vehviläinen, Henna-Reetta Hannula, Ioanna Merkouriadi, Stefan Scheiblauer, Helmut Rott, Thomas Nagler, Elisabeth Ripper, Kelly Elder, Hans-Peter Marshall, Reinhard Fromm, Marc Adams, Chris Derksen, Joshua King, Adriano Meta, Alex Coccia, Nick Rutter, Melody Sandells, Giovanni Macelloni, Emanuele Santi, Marion Leduc-Leballeur, Richard Essery, Cecile Menard, and Michael Kern
Earth Syst. Sci. Data, 14, 3915–3945, https://doi.org/10.5194/essd-14-3915-2022, https://doi.org/10.5194/essd-14-3915-2022, 2022
Short summary
Short summary
The manuscript describes airborne, dual-polarised X and Ku band synthetic aperture radar (SAR) data collected over several campaigns over snow-covered terrain in Finland, Austria and Canada. Colocated snow and meteorological observations are also presented. The data are meant for science users interested in investigating X/Ku band radar signatures from natural environments in winter conditions.
Hanna K. Lappalainen, Tuukka Petäjä, Timo Vihma, Jouni Räisänen, Alexander Baklanov, Sergey Chalov, Igor Esau, Ekaterina Ezhova, Matti Leppäranta, Dmitry Pozdnyakov, Jukka Pumpanen, Meinrat O. Andreae, Mikhail Arshinov, Eija Asmi, Jianhui Bai, Igor Bashmachnikov, Boris Belan, Federico Bianchi, Boris Biskaborn, Michael Boy, Jaana Bäck, Bin Cheng, Natalia Chubarova, Jonathan Duplissy, Egor Dyukarev, Konstantinos Eleftheriadis, Martin Forsius, Martin Heimann, Sirkku Juhola, Vladimir Konovalov, Igor Konovalov, Pavel Konstantinov, Kajar Köster, Elena Lapshina, Anna Lintunen, Alexander Mahura, Risto Makkonen, Svetlana Malkhazova, Ivan Mammarella, Stefano Mammola, Stephany Buenrostro Mazon, Outi Meinander, Eugene Mikhailov, Victoria Miles, Stanislav Myslenkov, Dmitry Orlov, Jean-Daniel Paris, Roberta Pirazzini, Olga Popovicheva, Jouni Pulliainen, Kimmo Rautiainen, Torsten Sachs, Vladimir Shevchenko, Andrey Skorokhod, Andreas Stohl, Elli Suhonen, Erik S. Thomson, Marina Tsidilina, Veli-Pekka Tynkkynen, Petteri Uotila, Aki Virkkula, Nadezhda Voropay, Tobias Wolf, Sayaka Yasunaka, Jiahua Zhang, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergej Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala
Atmos. Chem. Phys., 22, 4413–4469, https://doi.org/10.5194/acp-22-4413-2022, https://doi.org/10.5194/acp-22-4413-2022, 2022
Short summary
Short summary
We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
Short summary
Short summary
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.
Bin Cheng, Yubing Cheng, Timo Vihma, Anna Kontu, Fei Zheng, Juha Lemmetyinen, Yubao Qiu, and Jouni Pulliainen
Earth Syst. Sci. Data, 13, 3967–3978, https://doi.org/10.5194/essd-13-3967-2021, https://doi.org/10.5194/essd-13-3967-2021, 2021
Short summary
Short summary
Climate change strongly impacts the Arctic, with clear signs of higher air temperature and more precipitation. A sustainable observation programme has been carried out in Lake Orajärvi in Sodankylä, Finland. The high-quality air–snow–ice–water temperature profiles have been measured every winter since 2009. The data can be used to investigate the lake ice surface heat balance and the role of snow in lake ice mass balance and parameterization of snow-to-ice transformation in snow/ice models.
Cited articles
Armstrong, R. L. and Brodzik, M. J.: Recent northern hemisphere snow extent: A
comparison of data derived from visible and microwave satellite sensors,
Geophys. Res. Lett., 28, 3673–3676,
https://doi.org/10.1029/2000GL012556, 2001.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming
climate on water availability in snow-dominated regions, Nature, 438,
303–309, https://doi.org/10.1038/nature04141, 2005.
Barry, R. G.: The Role of Snow and Ice in the Global Climate System: A
Review, Polar Geogr., 26, 235–246, https://doi.org/10.1080/789610195,
2002.
Brown, R., Fang, B., and Mudryk, L.: Update of Canadian Historical Snow Survey
Data and Analysis of Snow Water Equivalent Trends, 1967–2016,
Atmos.-Ocean, 57, 149–156,
https://doi.org/10.1080/07055900.2019.1598843, 2019.
Broxton, P. D., Dawson, N., and Zeng, X.: Linking snowfall and snow accumulation
to generate spatial maps of SWE and snow depth, Earth Space Sci.
Res., 3, 246–256, https://doi.org/10.1002/2016EA000174, 2016.
Bulygina, O., Groisman, P. Ya., Razuvaev, V., and Korshunova, N.: Changes in
snow cover characteristics over Northern Eurasia since 1966, Environ. Res.
Lett., 6, 045204, https://doi.org/10.1088/1748-9326/6/4/045204, 2011.
Chang, A. and Foster, J.: Nimbus-7 SMMR Derived Global Snow Cover Parameters,
Ann. Glaciol., 9, 39–44, https://doi.org/10.3189/S0260305500200736,
1987.
Derksen, C., Walker, A., and Goodison, B.: Evaluation of passive microwave snow
water equivalent retrievals across the boreal forest/tundra transition of
western Canada, Remote Sens. Environ., 96, 315–327,
https://doi.org/10.1016/j.rse.2005.02.014, 2005.
Dyer, J. L. and Mote, T. L.: Spatial variability and trends in observed snow
depth over North America, Geophys. Res. Lett., 33, L16503,
https://doi.org/10.1029/2006GL027258, 2006.
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., Greene, E., McClun, D.,
Nishimura, K., Satyawali, P. K., and Sokratov, S. A.: The International
Classification for Seasonal Snow on the Ground, IHP-VII Technical Documents
in Hydrology, No. 83, Paris, 2009.
Goovaerts, P.: Geostatistics for Natural Resources Evaluation, Applied
Geostatistics Series, Oxford University Press, Cambridge University Press,
https://doi.org/10.1017/S0016756898631502, 1997.
Haberkorn, A.: European Snow Booklet – an Inventory of Snow Measurements in
Europe, EnviDat, https://https://doi.org/10.16904/envidat.59, 2019.
Høst, G.: Kriging by local polynomials, Comput. Stat.
Data Anal., 29, 295–312,
https://doi.org/10.1016/S0167-9473(98)00063-2, 1999.
Jordan, R., Andreas, E., and Makshtas, A.: Heat budget of snow-covered sea ice
at North Pole 4, J. Geophys. Res.-Oceans, 104, 7785–7806,
https://doi.org/10.1029/1999JC900011, 1999.
Kelly, R.: The AMSR-E Snow Depth Algorithm: Description and Initial Results,
J. Remote Sens. Soc. Jpn., 29, 307–317,
https://doi.org/10.11440/rssj.29.307, 2009.
Kelly, R., Chang, A., Tsang, L., and Foster, J.: A prototype AMSR-E global snow
area and snow depth algorithm, IEEE T. Geosci. Remote, 41, 230–242, https://doi.org/10.1109/TGRS.2003.809118, 2003.
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W.,
Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schöber, J., and
de Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere
mountains observed from space, Nat. Commun., 10, 1–12,
https://doi.org/10.1038/s41467-019-12566-y, 2019.
Luojus, K., Pulliainen, J., Takala, M., Lemmetuinen, J., Kangwa, M.,
Smolander, T., Cohen, J., and Derksen, C.: Preliminary SWE validation report,
European Space Agency Study Contract report, available at: https://www.globsnow.info/swe/GS2_DEL_08_SWE_prel_validation_report_v1_r01_final.pdf (last access: 1 June 2020), 2013a.
Luojus, K., Pulliainen, J., Takala, M., Lemmetuinen, J., Kangwa, M.,
Smolander, T., Cohen, J., and Derksen, C.: Algorithm Theoretical Basis Document
– SWE-algorithm, European Space Agency, available at: https://www.globsnow.info/docs/GS2_SWE_ATBD.pdf (last access: 1 June 2020), 2013b.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., and Moisander, M.: GlobSnow v3.0 snow water equivalent (SWE), available at: https://www.globsnow.info/swe/archive_v3.0/L3A_daily_SWE/, last access: 20 May 2020a.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., and Moisander, M.: GlobSnow v3.0 snow water equivalent (SWE) source codes, available at: http://www.globsnow.info/swe/archive_v3.0/source_codes/, last access: 3 March 2020b.
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Moisander, M.,
Mortimer, C., Derksen, C., Hiltunen, M., Smolander, T., Ikonen, J., Cohen,
J., Veijola, K., and Venäläinen, P.: GlobSnow v3.0 Northern Hemisphere
snow water equivalent dataset, Sci. Data, https://doi.org/10.1038/s41597-021-00939-2, online first, 2021.
Maurice, G. and Harold, M.: Handbook of snow: Principles, Processes,
Management and Use, Pergamon Press, Toronto, New York, 1981.
Mortimer, C., Mudryk, L., Derksen, C., Luojus, K., Brown, R., Kelly, R., and Tedesco, M.: Evaluation of long-term Northern Hemisphere snow water equivalent products, The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, 2020.
Mudryk, L. R., Derksen, C., Kushner, P. J., and Brown, R.: Characterization of
Northern Hemisphere snow water equivalent datasets, 1981–2010, J.
Climate, 28, 8037–8051, https://doi.org/10.1175/JCLI-D-15-0229.1, 2015.
Mudryk, L. R., Derksen, C., Howell, S., Laliberté, F., Thackeray, C., Sospedra-Alfonso, R., Vionnet, V., Kushner, P. J., and Brown, R.: Canadian snow and sea ice: historical trends and projections, The Cryosphere, 12, 1157–1176, https://doi.org/10.5194/tc-12-1157-2018, 2018.
O'Sullivan, D. and Unwin, D.: Geographic Information Analysis, Wiley, Hoboken, New Jersey, ISBN 978-0-470-28857-3, 2010.
Pulliainen, J.: Mapping of snow water equivalent and snow depth in boreal
and sub-arctic zones by assimilating space-borne microwave radiometer data
and ground-based observations, Remote Sens. Environ., 101, 257–269,
https://doi.org/10.1016/j.rse.2006.01.002, 2006.
Pulliainen, J., Grandell, J., and Hallikainen, M.: HUT snow emission model and
its applicability to snow water equivalent retrieval, IEEE T.
Geosci. Remote, 37, 1378–1390,
https://doi.org/10.1109/36.763302, 1999.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J.,
Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.:
Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018,
Nature, 581, 294–298, https://doi.org/10.1038/s41586-020-2258-0, 2020.
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C.,
Heliere, F., Kern, M., MacElloni, G., Malnes, E., Nagler, T., Pulliainen,
J., Rebhan, H., and Thompson, A.: Cold regions hydrology high-resolution
observatory for snow and cold land processes, Proc. IEEE, 98,
752–765, https://doi.org/10.1109/JPROC.2009.2038947, 2010.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty, R. S.:
Characteristics of the western United States snowpack from snowpack
telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999.
Sturm, M., Holmgren, J., and Liston, G.: A seasonal snow
cover classification system for local to global applications, J. Climate, 8, 1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.:
Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes,
J. Hydrometeorol., 11, 1380–1394,
https://doi.org/10.1175/2010JHM1202.1, 2010.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J.,
Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating Northern
Hemisphere snow water equivalent for climate research through assimilation
of space-borne radiometer data and ground-based measurements, Remote Sens. Environ., 115, 3517–3529, https://doi.org/10.1016/j.rse.2011.08.014,
2011.
Tedesco, M. and Narvekar, P. S.: Assessment of the NASA AMSR-E SWE Product,
IEEE J. Sel. Top. Appl., 3, 141–159,
https://doi.org/10.1109/JSTARS.2010.2040462, 2010.
Tedesco, M. and Jeyaratnam, J.: A new operational snow retrieval
algorithm applied to historical AMSR-E brightness temperatures, Remote Sens., 8, 1–25, https://doi.org/10.3390/rs8121037, 2016.
Short summary
Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Information about snow water equivalent (SWE) is needed in many applications, including climate...