Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6459-2020
© Author(s) 2020. 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-13-6459-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improved cloud detection over sea ice and snow during Arctic summer using MERIS data
Alfred-Wegener-Institut, Helmholz Zentrum für Polar und
Meeresforschung, 27570 Bremerhaven, Germany
Institute of Environmental Physics, University of Bremen, 28357 Bremen, Germany
Henrik Marks
Institute of Environmental Physics, University of Bremen, 28357 Bremen, Germany
Marcus Huntemann
Alfred-Wegener-Institut, Helmholz Zentrum für Polar und
Meeresforschung, 27570 Bremerhaven, Germany
Institute of Environmental Physics, University of Bremen, 28357 Bremen, Germany
Georg Heygster
Institute of Environmental Physics, University of Bremen, 28357 Bremen, Germany
Gunnar Spreen
Institute of Environmental Physics, University of Bremen, 28357 Bremen, Germany
Related authors
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei, and Anika Rohde
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-14, https://doi.org/10.5194/tc-2021-14, 2021
Preprint withdrawn
Short summary
Short summary
Different approaches to retrieve the optical-equivalent snow grain size using satellite, airborne, and ground-based observations were evaluated and compared to modeled data. The study is focused on low Sun and partly rough surface conditions encountered North of Greenland in March/April 2018. We proposed an adjusted airborne retrieval method to reduce the retrieval uncertainty.
Larysa Istomina, Hannah Niehaus, and Gunnar Spreen
The Cryosphere, 19, 83–105, https://doi.org/10.5194/tc-19-83-2025, https://doi.org/10.5194/tc-19-83-2025, 2025
Short summary
Short summary
Melt water puddles, or melt ponds on top of the Arctic sea ice, are a good measure of the Arctic climate state. In the context of recent climate warming, the Arctic has warmed about 4 times faster than the rest of the world, and a long-term dataset of the melt pond fraction is needed to be able to model the future development of the Arctic climate. We present such a dataset, produce 2002–2023 trends and highlight a potential melt regime shift with drastic regional trends of + 20 % per decade.
Karl Kortum, Suman Singha, and Gunnar Spreen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3351, https://doi.org/10.5194/egusphere-2024-3351, 2024
Short summary
Short summary
Improved sea ice observations are essential to understanding the processes that lead to the strong warming effect currently being observed in the Arctic. In this work, we combine complementary satellite measurement techniques and find remarkable correlations between the two observations. This allows us to expand the coverage of ice topography measurements to a scope and resolution that could not previously be observed.
Rémy Lapere, Louis Marelle, Pierre Rampal, Laurent Brodeau, Christian Melsheimer, Gunnar Spreen, and Jennie L. Thomas
Atmos. Chem. Phys., 24, 12107–12132, https://doi.org/10.5194/acp-24-12107-2024, https://doi.org/10.5194/acp-24-12107-2024, 2024
Short summary
Short summary
Elongated open-water areas in sea ice, called leads, can release marine aerosols into the atmosphere. In the Arctic, this source of atmospheric particles could play an important role for climate. However, the amount, seasonality and spatial distribution of such emissions are all mostly unknown. Here, we propose a first parameterization for sea spray aerosols emitted through leads in sea ice and quantify their impact on aerosol populations in the high Arctic.
Hannah Niehaus, Gunnar Spreen, Larysa Istomina, and Marcel Nicolaus
EGUsphere, https://doi.org/10.5194/egusphere-2024-3127, https://doi.org/10.5194/egusphere-2024-3127, 2024
Short summary
Short summary
Melt ponds on Arctic sea ice affect how much solar energy is absorbed, influencing ice melt and climate change. This study used satellite data from 2017–2023 to examine how these ponds vary across regions and seasons. The results show that the surface fraction of melt ponds is more stable in the Central Arctic, with air temperature and ice surface roughness playing key roles in their formation. Understanding these patterns can help to improve climate models and predictions for Arctic warming.
Nils Risse, Mario Mech, Catherine Prigent, Gunnar Spreen, and Susanne Crewell
The Cryosphere, 18, 4137–4163, https://doi.org/10.5194/tc-18-4137-2024, https://doi.org/10.5194/tc-18-4137-2024, 2024
Short summary
Short summary
Passive microwave observations from satellites are crucial for monitoring Arctic sea ice and atmosphere. To do this effectively, it is important to understand how sea ice emits microwaves. Through unique Arctic sea ice observations, we improved our understanding, identified four distinct emission types, and expanded current knowledge to include higher frequencies. These findings will enhance our ability to monitor the Arctic climate and provide valuable information for new satellite missions.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Karl Kortum, Suman Singha, Gunnar Spreen, Nils Hutter, Arttu Jutila, and Christian Haas
The Cryosphere, 18, 2207–2222, https://doi.org/10.5194/tc-18-2207-2024, https://doi.org/10.5194/tc-18-2207-2024, 2024
Short summary
Short summary
A dataset of 20 radar satellite acquisitions and near-simultaneous helicopter-based surveys of the ice topography during the MOSAiC expedition is constructed and used to train a variety of deep learning algorithms. The results give realistic insights into the accuracy of retrieval of measured ice classes using modern deep learning models. The models able to learn from the spatial distribution of the measured sea ice classes are shown to have a clear advantage over those that cannot.
Luisa von Albedyll, Stefan Hendricks, Nils Hutter, Dmitrii Murashkin, Lars Kaleschke, Sascha Willmes, Linda Thielke, Xiangshan Tian-Kunze, Gunnar Spreen, and Christian Haas
The Cryosphere, 18, 1259–1285, https://doi.org/10.5194/tc-18-1259-2024, https://doi.org/10.5194/tc-18-1259-2024, 2024
Short summary
Short summary
Leads (openings in sea ice cover) are created by sea ice dynamics. Because they are important for many processes in the Arctic winter climate, we aim to detect them with satellites. We present two new techniques to detect lead widths of a few hundred meters at high spatial resolution (700 m) and independent of clouds or sun illumination. We use the MOSAiC drift 2019–2020 in the Arctic for our case study and compare our new products to other existing lead products.
Evelyn Jäkel, Sebastian Becker, Tim R. Sperzel, Hannah Niehaus, Gunnar Spreen, Ran Tao, Marcel Nicolaus, Wolfgang Dorn, Annette Rinke, Jörg Brauchle, and Manfred Wendisch
The Cryosphere, 18, 1185–1205, https://doi.org/10.5194/tc-18-1185-2024, https://doi.org/10.5194/tc-18-1185-2024, 2024
Short summary
Short summary
The results of the surface albedo scheme of a coupled regional climate model were evaluated against airborne and ground-based measurements conducted in the European Arctic in different seasons between 2017 and 2022. We found a seasonally dependent bias between measured and modeled surface albedo for cloudless and cloudy situations. The strongest effects of the albedo model bias on the net irradiance were most apparent in the presence of optically thin clouds.
Hannah Niehaus, Larysa Istomina, Marcel Nicolaus, Ran Tao, Aleksey Malinka, Eleonora Zege, and Gunnar Spreen
The Cryosphere, 18, 933–956, https://doi.org/10.5194/tc-18-933-2024, https://doi.org/10.5194/tc-18-933-2024, 2024
Short summary
Short summary
Melt ponds are puddles of meltwater which form on Arctic sea ice in the summer period. They are darker than the ice cover and lead to increased absorption of solar energy. Global climate models need information about the Earth's energy budget. Thus satellite observations are used to monitor the surface fractions of melt ponds, ocean, and sea ice in the entire Arctic. We present a new physically based algorithm that can separate these three surface types with uncertainty below 10 %.
Pablo Saavedra Garfias, Heike Kalesse-Los, Luisa von Albedyll, Hannes Griesche, and Gunnar Spreen
Atmos. Chem. Phys., 23, 14521–14546, https://doi.org/10.5194/acp-23-14521-2023, https://doi.org/10.5194/acp-23-14521-2023, 2023
Short summary
Short summary
An important Arctic climate process is the release of heat fluxes from sea ice openings to the atmosphere that influence the clouds. The characterization of this process is the objective of this study. Using synergistic observations from the MOSAiC expedition, we found that single-layer cloud properties show significant differences when clouds are coupled or decoupled to the water vapour transport which is used as physical link between the upwind sea ice openings and the cloud under observation.
Alexander Mchedlishvili, Christof Lüpkes, Alek Petty, Michel Tsamados, and Gunnar Spreen
The Cryosphere, 17, 4103–4131, https://doi.org/10.5194/tc-17-4103-2023, https://doi.org/10.5194/tc-17-4103-2023, 2023
Short summary
Short summary
In this study we looked at sea ice–atmosphere drag coefficients, quantities that help with characterizing the friction between the atmosphere and sea ice, and vice versa. Using ICESat-2, a laser altimeter that measures elevation differences by timing how long it takes for photons it sends out to return to itself, we could map the roughness, i.e., how uneven the surface is. From roughness we then estimate drag force, the frictional force between sea ice and the atmosphere, across the Arctic.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Philip Rostosky and Gunnar Spreen
The Cryosphere, 17, 3867–3881, https://doi.org/10.5194/tc-17-3867-2023, https://doi.org/10.5194/tc-17-3867-2023, 2023
Short summary
Short summary
During winter, storms entering the Arctic region can bring warm air into the cold environment. Strong increases in air temperature modify the characteristics of the Arctic snow and ice cover. The Arctic sea ice cover can be monitored by satellites observing the natural emission of the Earth's surface. In this study, we show that during warm air intrusions the change in the snow characteristics influences the satellite-derived sea ice cover, leading to a false reduction of the estimated ice area.
Vishnu Nandan, Rosemary Willatt, Robbie Mallett, Julienne Stroeve, Torsten Geldsetzer, Randall Scharien, Rasmus Tonboe, John Yackel, Jack Landy, David Clemens-Sewall, Arttu Jutila, David N. Wagner, Daniela Krampe, Marcus Huntemann, Mallik Mahmud, David Jensen, Thomas Newman, Stefan Hendricks, Gunnar Spreen, Amy Macfarlane, Martin Schneebeli, James Mead, Robert Ricker, Michael Gallagher, Claude Duguay, Ian Raphael, Chris Polashenski, Michel Tsamados, Ilkka Matero, and Mario Hoppmann
The Cryosphere, 17, 2211–2229, https://doi.org/10.5194/tc-17-2211-2023, https://doi.org/10.5194/tc-17-2211-2023, 2023
Short summary
Short summary
We show that wind redistributes snow on Arctic sea ice, and Ka- and Ku-band radar measurements detect both newly deposited snow and buried snow layers that can affect the accuracy of snow depth estimates on sea ice. Radar, laser, meteorological, and snow data were collected during the MOSAiC expedition. With frequent occurrence of storms in the Arctic, our results show that
wind-redistributed snow needs to be accounted for to improve snow depth estimates on sea ice from satellite radars.
Wenkai Guo, Polona Itkin, Suman Singha, Anthony P. Doulgeris, Malin Johansson, and Gunnar Spreen
The Cryosphere, 17, 1279–1297, https://doi.org/10.5194/tc-17-1279-2023, https://doi.org/10.5194/tc-17-1279-2023, 2023
Short summary
Short summary
Sea ice maps are produced to cover the MOSAiC Arctic expedition (2019–2020) and divide sea ice into scientifically meaningful classes. We use a high-resolution X-band synthetic aperture radar dataset and show how image brightness and texture systematically vary across the images. We use an algorithm that reliably corrects this effect and achieve good results, as evaluated by comparisons to ground observations and other studies. The sea ice maps are useful as a basis for future MOSAiC studies.
Christian Melsheimer, Gunnar Spreen, Yufang Ye, and Mohammed Shokr
The Cryosphere, 17, 105–126, https://doi.org/10.5194/tc-17-105-2023, https://doi.org/10.5194/tc-17-105-2023, 2023
Short summary
Short summary
It is necessary to know the type of Antarctic sea ice present – first-year ice (grown in one season) or multiyear ice (survived one summer melt) – to understand and model its evolution, as the ice types behave and react differently. We have adapted and extended an existing method (originally for the Arctic), and now, for the first time, daily maps of Antarctic sea ice types can be derived from microwave satellite data. This will allow a new data set from 2002 well into the future to be built.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Ruzica Dadic, Philip Rostosky, Michael Gallagher, Robbie Mallett, Andrew Barrett, Stefan Hendricks, Rasmus Tonboe, Michelle McCrystall, Mark Serreze, Linda Thielke, Gunnar Spreen, Thomas Newman, John Yackel, Robert Ricker, Michel Tsamados, Amy Macfarlane, Henna-Reetta Hannula, and Martin Schneebeli
The Cryosphere, 16, 4223–4250, https://doi.org/10.5194/tc-16-4223-2022, https://doi.org/10.5194/tc-16-4223-2022, 2022
Short summary
Short summary
Impacts of rain on snow (ROS) on satellite-retrieved sea ice variables remain to be fully understood. This study evaluates the impacts of ROS over sea ice on active and passive microwave data collected during the 2019–20 MOSAiC expedition. Rainfall and subsequent refreezing of the snowpack significantly altered emitted and backscattered radar energy, laying important groundwork for understanding their impacts on operational satellite retrievals of various sea ice geophysical variables.
Alexander Mchedlishvili, Gunnar Spreen, Christian Melsheimer, and Marcus Huntemann
The Cryosphere, 16, 471–487, https://doi.org/10.5194/tc-16-471-2022, https://doi.org/10.5194/tc-16-471-2022, 2022
Short summary
Short summary
In this paper we show that the activity leading to the open-ocean polynyas near the Maud Rise seamount that have occurred repeatedly from 1974–1976 as well as 2016–2017 does not simply stop for polynya-free years. Using apparent sea ice thickness retrieval, we have identified anomalies where there is thinning of sea ice on a scale that is comparable to that of the polynya events of 2016–2017. These anomalies took place in 2010, 2013, 2014 and 2018.
Thomas Krumpen, Luisa von Albedyll, Helge F. Goessling, Stefan Hendricks, Bennet Juhls, Gunnar Spreen, Sascha Willmes, H. Jakob Belter, Klaus Dethloff, Christian Haas, Lars Kaleschke, Christian Katlein, Xiangshan Tian-Kunze, Robert Ricker, Philip Rostosky, Janna Rückert, Suman Singha, and Julia Sokolova
The Cryosphere, 15, 3897–3920, https://doi.org/10.5194/tc-15-3897-2021, https://doi.org/10.5194/tc-15-3897-2021, 2021
Short summary
Short summary
We use satellite data records collected along the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) drift to categorize ice conditions that shaped and characterized the floe and surroundings during the expedition. A comparison with previous years is made whenever possible. The aim of this analysis is to provide a basis and reference for subsequent research in the six main research areas of atmosphere, ocean, sea ice, biogeochemistry, remote sensing and ecology.
Susanne Crewell, Kerstin Ebell, Patrick Konjari, Mario Mech, Tatiana Nomokonova, Ana Radovan, David Strack, Arantxa M. Triana-Gómez, Stefan Noël, Raul Scarlat, Gunnar Spreen, Marion Maturilli, Annette Rinke, Irina Gorodetskaya, Carolina Viceto, Thomas August, and Marc Schröder
Atmos. Meas. Tech., 14, 4829–4856, https://doi.org/10.5194/amt-14-4829-2021, https://doi.org/10.5194/amt-14-4829-2021, 2021
Short summary
Short summary
Water vapor (WV) is an important variable in the climate system. Satellite measurements are thus crucial to characterize the spatial and temporal variability in WV and how it changed over time. In particular with respect to the observed strong Arctic warming, the role of WV still needs to be better understood. However, as shown in this paper, a detailed understanding is still hampered by large uncertainties in the various satellite WV products, showing the need for improved methods to derive WV.
Anja Rösel, Sinead Louise Farrell, Vishnu Nandan, Jaqueline Richter-Menge, Gunnar Spreen, Dmitry V. Divine, Adam Steer, Jean-Charles Gallet, and Sebastian Gerland
The Cryosphere, 15, 2819–2833, https://doi.org/10.5194/tc-15-2819-2021, https://doi.org/10.5194/tc-15-2819-2021, 2021
Short summary
Short summary
Recent observations in the Arctic suggest a significant shift towards a snow–ice regime caused by deep snow on thin sea ice which may result in a flooding of the snowpack. These conditions cause the brine wicking and saturation of the basal snow layers which lead to a subsequent underestimation of snow depth from snow radar mesurements. As a consequence the calculated sea ice thickness will be biased towards higher values.
Yu Zhang, Tingting Zhu, Gunnar Spreen, Christian Melsheimer, Marcus Huntemann, Nick Hughes, Shengkai Zhang, and Fei Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-85, https://doi.org/10.5194/tc-2021-85, 2021
Revised manuscript not accepted
Short summary
Short summary
We developed an algorithm for ice-water classification using Sentinel-1 data during melting seasons in the Fram Strait. The proposed algorithm has the OA of nearly 90 % with STD less than 10 %. The comparison of sea ice concentration demonstrate that it can provide detailed information of sea ice with the spatial resolution of 1km. The time series shows the average June to September sea ice area does not change so much in 2015–2017 and 2019–2020, but it has a significant decrease in 2018.
Stephan Paul and Marcus Huntemann
The Cryosphere, 15, 1551–1565, https://doi.org/10.5194/tc-15-1551-2021, https://doi.org/10.5194/tc-15-1551-2021, 2021
Short summary
Short summary
Cloud cover in the polar regions is difficult to identify at night when using only thermal-infrared data. This is due to occurrences of warm clouds over cold sea ice and cold clouds over warm sea ice. Especially the standard MODIS cloud mask frequently tends towards classifying open water and/or thin ice as cloud cover. Using a neural network, we present an improved discrimination between sea-ice, open-water and/or thin-ice, and cloud pixels in nighttime MODIS thermal-infrared satellite data.
Evelyn Jäkel, Tim Carlsen, André Ehrlich, Manfred Wendisch, Michael Schäfer, Sophie Rosenburg, Konstantina Nakoudi, Marco Zanatta, Gerit Birnbaum, Veit Helm, Andreas Herber, Larysa Istomina, Linlu Mei, and Anika Rohde
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-14, https://doi.org/10.5194/tc-2021-14, 2021
Preprint withdrawn
Short summary
Short summary
Different approaches to retrieve the optical-equivalent snow grain size using satellite, airborne, and ground-based observations were evaluated and compared to modeled data. The study is focused on low Sun and partly rough surface conditions encountered North of Greenland in March/April 2018. We proposed an adjusted airborne retrieval method to reduce the retrieval uncertainty.
Julienne Stroeve, Vishnu Nandan, Rosemary Willatt, Rasmus Tonboe, Stefan Hendricks, Robert Ricker, James Mead, Robbie Mallett, Marcus Huntemann, Polona Itkin, Martin Schneebeli, Daniela Krampe, Gunnar Spreen, Jeremy Wilkinson, Ilkka Matero, Mario Hoppmann, and Michel Tsamados
The Cryosphere, 14, 4405–4426, https://doi.org/10.5194/tc-14-4405-2020, https://doi.org/10.5194/tc-14-4405-2020, 2020
Short summary
Short summary
This study provides a first look at the data collected by a new dual-frequency Ka- and Ku-band in situ radar over winter sea ice in the Arctic Ocean. The instrument shows potential for using both bands to retrieve snow depth over sea ice, as well as sensitivity of the measurements to changing snow and atmospheric conditions.
Cited articles
Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller,
C. C., and
Gumley, L. E.: Discriminating clear sky from clouds with MODIS, J. Geophys.
Res., 103, 32141–32157, 1998.
Ackerman, S. A., Holz, R., Frey, R., Eloranta, E., Maddux, B., and McGill, M.: Cloud Detection with MODIS. Part II: Validation, J. Atmos.
Ocean. Tech., 25, 1073–1086, https://doi.org/10.1175/2007JTECHA1053.1,
2008.
Allen, R. C., Durkee, P. A., and Wash, C. H.: Snow/Cloud Discrimination with
Multispectral Satellite Measurements, J. Appl. Meteorol., 29, 994–1004, 1990.
Bourg, L., D'Alba, L., and Colagrande, P.: MERIS Smile Effect
Characterization and Correction, Tech. rep., European Space Agency, available at: https://earth.esa.int/documents/700255/707220/MERIS_Smile_Effect.pdf (last access: 30 July 2020), 2008.
Bréon, F.-M. and Colzy, S.: Cloud detection from the spaceborne POLDER
instrument and validation against surface synoptic observations, J. Appl.
Meteorol., 38, 777–785, 1999.
Curry, J. A., Rossow, W. B., Randall, D., and Schramm, J. L.: Overview of
Arctic cloud and radiation characteristics, J. Climate, 9, 1731–1764,
https://doi.org/10.1175/1520-0442(1996)009<1731:OOACAR>2.0.CO;2,
1996.
Diner, D., Clothiaux, E., and Di Girolamo, L.: MISR Multi-angle imaging
spectro-radiometer algorithm theoretical basis. Level 1 Cloud detection, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA, JPL D-13397, 1999.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gómez-Chova, L., Camps-Valls, G., Calpe-Maravilla, J., Guanter, L., and
Moreno, J.: Cloud-Screening Algorithm for ENVISAT/MERIS Multispectral
Images, IEEE T. Geosci. Remote, 45, 4105–4118,
https://doi.org/10.1109/TGRS.2007.905312, 2007.
Hollstein, A., Fischer, J., Carbajal Henken, C., and Preusker, R.: Bayesian cloud detection for MERIS, AATSR, and their combination, Atmos. Meas. Tech., 8, 1757–1771, https://doi.org/10.5194/amt-8-1757-2015, 2015.
Istomina, L.: The MPD MPF dataset, available at:
https://seaice.uni-bremen.de/data/meris/gridded/ (last access: 30 July 2020), 2017.
Istomina, L. G., von Hoyningen-Huene, W., Kokhanovsky, A. A., and Burrows, J. P.: The detection of cloud-free snow-covered areas using AATSR measurements, Atmos. Meas. Tech., 3, 1005–1017, https://doi.org/10.5194/amt-3-1005-2010, 2010.
Istomina, L. G., von Hoyningen-Huene, W., Kokhanovsky, A. A., Schultz, E., and Burrows, J. P.: Remote sensing of aerosols over snow using infrared AATSR observations, Atmos. Meas. Tech., 4, 1133–1145, https://doi.org/10.5194/amt-4-1133-2011, 2011.
Istomina, L., Nicolaus, M., Perovich, D. K.: Spectral albedo of sea ice and
melt ponds measured during POLARSTERN cruise ARK-XXVII/3 (IceArc) in 2012,
Institut für Umweltphysik, Universität Bremen, PANGAEA,
https://doi.org/10.1594/PANGAEA.815111, 2013.
Istomina, L., Heygster, G., Huntemann, M., Schwarz, P., Birnbaum, G., Scharien, R., Polashenski, C., Perovich, D., Zege, E., Malinka, A., Prikhach, A., and Katsev, I.: Melt pond fraction and spectral sea ice albedo retrieval from MERIS data – Part 1: Validation against in situ, aerial, and ship cruise data, The Cryosphere, 9, 1551–1566, https://doi.org/10.5194/tc-9-1551-2015, 2015.
Istomina, L., Marks, H., and Huntemann, M.: MECOSI cloud screening dataset, University of Bremen, available at: https://www.seaice.uni-bremen.de/start/ (last access: 30 July 2020), 2017.
Jäger, M.: Advanced MERIS Channel 11 Smile Correction as a Basis for
Cloud Top Height Estimation Developed from SCIATRAN Radiative Transfer
Calculations in the O2-A Absorption Band, Master's thesis, University of
Bremen, Bremen, Germany, 2013.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A.,
Reynolds, B., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.
C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year
reanalysis project, B. Am. Meteorol. Soc., 77, 437–470, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Key, J. and Barry, R.G.: Cloud cover analysis with Arctic AVHRR data. 1.
Cloud detection, J. Geophys. Res., 94, 18521–18535, 1989.
Kokhanovsky, A. A.: Cloud Optics, edited by: Mysak, L. A. and Hamilton, K.,
Springer, Dordrecht, the Netherlands, 2006.
Kokhanovsky, A. A., von Hoyningen-Huene, W., and Burrows, J. P.:
Determination of the cloud fraction in the SCIAMACHY ground scene using
MERIS spectral measurements, Int. J. Remote Sens.,
30, 6151–6167, https://doi.org/10.1080/01431160902842326, 2009.
Krijger, J. M., Tol, P., Istomina, L. G., Schlundt, C., Schrijver, H., and Aben, I.: Improved identification of clouds and ice/snow covered surfaces in SCIAMACHY observations, Atmos. Meas. Tech., 4, 2213–2224, https://doi.org/10.5194/amt-4-2213-2011, 2011.
Lindstrot, R., Preusker, R.,
and Fischer, J.: Empirical Correction of Stray Light within the MERIS Oxygen
A-Band Channel, J. Atmos. Ocean. Tech., 27,
1185–1194, https://doi.org/10.1175/2010JTECHA1430.1, 2010.
Liu, Y., Key, J. R., Frey, R. A., Ackerman, S .A., and Menzel, W. P.: Nighttime
polar cloud detection with MODIS, Remote Sens. Environ., 92, 181–194,
2004.
Lotz, W. A., Vountas, M., Dinter, T., and Burrows, J. P.: Cloud and surface classification using SCIAMACHY polarization measurement devices, Atmos. Chem. Phys., 9, 1279–1288, https://doi.org/10.5194/acp-9-1279-2009, 2009.
Lyapustin, A. and Wang, Y.: The time series technique for aerosol retrievals
over land from MODIS, Satellite Aerosol Remote Sensing over Land, edited by:
Kokhanovsky A. A. and de Leeuw, G., Springer Praxis Publ., Chichester, UK,
69–99, 2009.
Lyapustin, A., Wang, Y., and Frey, R.: An automated cloud mask
algorithm based on time series of MODIS measurements, J. Geophys. Res., 113,
D16207, https://doi.org/10.1029/2007JD009641, 2008.
Madhavan, S., Angal, A., Dodd, J., Sun, J., and Xiong, X.: Analog and
digital saturation in the MODIS reflective solar bands, Proc. SPIE 8510, Earth Observing Systems XVII, 85101N,
https://doi.org/10.1117/12.929998, 2012.
Marks, H.: Investigation of Algorithms to Retrieve Melt Pond Fraction on
Arctic Sea Ice from Optical Satellite Observations, Master's thesis,
Universität Tübingen, Tübingen, Germany, 2015.
Martins, J. V., Tanré, D., Remer, L., Kaufman, Y., Mattoo, S., and Levy,
R.: MODIS Cloud screening for remote sensing of aerosols over oceans using
spatial variability, Geophys. Res. Lett., 29, 8009,
https://doi.org/10.1029/2001GL013252, 2002.
Minnis, P., Chakrapani, V., Doelling, D. R., Nguyen, L., Palikonda, R.,
Spangenberg, D. A., Uttal, T., Arduini, R. F., and Shupe, M.: Cloud coverage and
height during FIRE ACE derived from AVHRR data, J. Geophys. Res., 106,
15215–15232, 2001.
Nicolaus, M., Katlein, C., Maslanik, J., and Hendricks, S.: Changes in Arctic
sea ice result in increasing light transmittance and absorption: LIGHT IN A
CHANGING ARCTIC OCEAN, Geophys. Res. Lett., 39, L24501,
https://doi.org/10.1029/2012GL053738, 2012.
Preusker, R. and Lindstrot, R.: Remote Sensing of Cloud-Top Pressure Using
Moderately Resolved Measurements within the Oxygen A Band – A Sensitivity
Study, J. Appl. Meteorol. Clim., 48, 1562–1574,
https://doi.org/10.1175/2009JAMC2074.1, 2009.
Schlundt, C., Kokhanovsky, A. A., von Hoyningen-Huene, W., Dinter, T., Istomina, L., and Burrows, J. P.: Synergetic cloud fraction determination for SCIAMACHY using MERIS, Atmos. Meas. Tech., 4, 319–337, https://doi.org/10.5194/amt-4-319-2011, 2011.
Schröder, D., Feltham, D., Flocco, D., and Tsamados, M.: September Arctic sea-ice
minimum predicted by spring melt-pond fraction, Nat. Clim. Change 4,
353–357, https://doi.org/10.1038/NCLIMATE2203, 2014.
Spangenberg, D. A., Chakrapani, V., Doelling, D. R., Minnis, P., and Arduini,
R. F.: Development of an automated Arctic cloud mask using clear-sky
satellite observations taken over the SHEBA and ARM NSA sites, Proc. 6th
Conf. on Polar Meteor., and Oceanography, 14–18 May 2001, San Diego, CA, USA, 246–249, 2001.
Trepte, Q., Arduini, R. F., Chen, Y., Sun-Mack, S., Minnis, P., Spangenberg,
D. A., and Doelling, D. R.: Development of a daytime polar cloud mask using
theoretical models of near-infrared bidirectional reflectance for ARM and
CERES, Proc. AMS 6th Conf. Polar Meteorology and Oceanography, San Diego,
CA, USA, 4–18 May 2001, 242–245, 2001.
Warren, S. G.: Optical Properties of Snow, Rev. Geophys., 20, 67–89,
1982.
Wiebe, H., Heygster, G., Zege, E., Aoki, T., and Hori, M.: Snow grain size
retrieval SGSP from optical satellite data: Validation with ground
measurements and detection of snow fall events, Remote Sens. Environ., 128,
11–20, https://doi.org/10.1016/j.rse.2012.09.007, 2013.
Zege, E., Malinka, A., Katsev, I., Prikhach, A., Heygster, G., Istomina, L.,
Birnbaum, G., and Schwarz, P.: Algorithm to retrieve the melt pond fraction
and the spectral albedo of Arctic summer ice from satellite optical data,
Remote Sens. Environ., 163, 153–164, https://doi.org/10.1016/j.rse.2015.03.012,
2015.