Articles | Volume 15, issue 20
https://doi.org/10.5194/amt-15-6035-2022
© 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-15-6035-2022
© 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 Oct 2022
Research article |
| 21 Oct 2022
| 21 Oct 2022
DeepPrecip: a deep neural network for precipitation retrievals
Dept. of Geography & Environmental Management, University of Waterloo, 200 University Ave W, Waterloo, Ontario, Canada
George Duffy
Jet Propulsion Laboratory, NASA, 4800 Oak Grove Dr, Pasadena, 91109, California, USA
Earth and Environmental Sciences, University of Syracuse, 900 South Crouse Ave, Syracuse, New York, USA
Lisa Milani
Goddard Space Flight Center, NASA, 8800 Greenbelt Rd, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, 5825 University Research Ct suite 4001, College Park, Maryland, USA
Christopher G. Fletcher
Dept. of Geography & Environmental Management, University of Waterloo, 200 University Ave W, Waterloo, Ontario, Canada
Claire Pettersen
Climate and Space Sciences and Engineering, University of Michigan, Climate and Space Research Building, 2455 Hayward St, Ann Arbor, Michigan, USA
Kerstin Ebell
Institute for Geophysics and Meteorology, University of Cologne, Albertus-Magnus-Platz, Cologne, Germany
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Fraser King, Andre R. Erler, Steven K. Frey, and Christopher G. Fletcher
Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, https://doi.org/10.5194/hess-24-4887-2020, 2020
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Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
Sebastian Zeppenfeld, Jonas Schaefer, Christian Pilz, Kerstin Ebell, Moritz Zeising, Frank Stratmann, Holger Siebert, Birgit Wehner, Matthias Wietz, Astrid Bracher, and Manuela van Pinxteren
EGUsphere, https://doi.org/10.5194/egusphere-2025-4336, https://doi.org/10.5194/egusphere-2025-4336, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Aerosol particles from sea spray transport inorganic salts and carbohydrates from the ocean into the atmosphere. In this field study conducted in Svalbard, we found that carbohydrates reach elevated altitudes that are relevant for cloud formation and properties.
Kerstin Ebell, Christian Buhren, Rosa Gierens, Giovanni Chellini, Melanie Lauer, Andreas Walbröl, Sandro Dahlke, Pavel Krobot, and Mario Mech
Atmos. Chem. Phys., 25, 7315–7342, https://doi.org/10.5194/acp-25-7315-2025, https://doi.org/10.5194/acp-25-7315-2025, 2025
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Ground-based observations of precipitation are rare in the Arctic. Since 2017, additional temporally highly resolved precipitation measurements have been carried out by a precipitation gauge and an optical precipitation sensor at Ny-Ålesund, Svalbard. These new data facilitate the distinction between liquid and solid precipitation. Using reanalysis data, we also find that water vapor transport contributes strongly to precipitation and especially to extreme precipitation events.
Denghui Ji, Mathias Palm, Matthias Buschmann, Kerstin Ebell, Marion Maturilli, Xiaoyu Sun, and Justus Notholt
Atmos. Chem. Phys., 25, 3889–3904, https://doi.org/10.5194/acp-25-3889-2025, https://doi.org/10.5194/acp-25-3889-2025, 2025
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Our study explores how certain aerosols, like sea salt, affect infrared heat radiation in the Arctic, potentially speeding up warming. We used advanced technology to measure aerosol composition and found that these particles grow with humidity, significantly increasing their heat-trapping effect in the infrared region, especially in winter. Our findings suggest these aerosols could be a key factor in Arctic warming, emphasizing the importance of understanding aerosols for climate prediction.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
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We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024, https://doi.org/10.5194/acp-24-8007-2024, 2024
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To support the interpretation of the data collected during the HALO-(AC)3 campaign, which took place in the North Atlantic sector of the Arctic from 7 March to 12 April 2022, we analyze how unusual the weather and sea ice conditions were with respect to the long-term climatology. From observations and ERA5 reanalysis, we found record-breaking warm air intrusions and a large variety of marine cold air outbreaks. Sea ice concentration was mostly within the climatological interquartile range.
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Charles Nelson Helms, Stephen Joseph Munchak, Ali Tokay, and Claire Pettersen
Atmos. Meas. Tech., 15, 6545–6561, https://doi.org/10.5194/amt-15-6545-2022, https://doi.org/10.5194/amt-15-6545-2022, 2022
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This study compares the techniques used to measure snowflake shape by three instruments: PIP, MASC, and 2DVD. Our findings indicate that the MASC technique produces reliable shape measurements; the 2DVD technique performs better than expected considering the instrument was designed to measure raindrops; and the PIP technique does not produce reliable snowflake shape measurements. We also demonstrate that the PIP images can be reprocessed to correct the shape measurement issues.
Giovanni Chellini and Kerstin Ebell
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-22, https://doi.org/10.5194/amt-2022-22, 2022
Preprint withdrawn
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Moisture inversions (MIs), i.e. atmospheric layers where specific humidity increases with height, are frequent in the Arctic. This study assesses the capability of two satellite instruments, IASI and AIRS, and one reanalysis, ERA5, to detect MIs at an Arctic site. The comparison with radiosonde data shows that humidity profiles from IASI and AIRS severely underestimate the occurrence of MIs. On the other hand, MI characteristics in ERA5 are comparable to those in the radiosonde data.
Hélène Bresson, Annette Rinke, Mario Mech, Daniel Reinert, Vera Schemann, Kerstin Ebell, Marion Maturilli, Carolina Viceto, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 173–196, https://doi.org/10.5194/acp-22-173-2022, https://doi.org/10.5194/acp-22-173-2022, 2022
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Arctic warming is pronounced, and one factor in this is the poleward atmospheric transport of heat and moisture. This study assesses the 4D structure of an Arctic moisture intrusion event which occurred in June 2017. For the first time, high-resolution pan-Arctic ICON simulations are performed and compared with global models, reanalysis, and observations. Results show the added value of high resolution in the event representation and the impact of the intrusion on the surface energy fluxes.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
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We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
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
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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.
Linn Karlsson, Radovan Krejci, Makoto Koike, Kerstin Ebell, and Paul Zieger
Atmos. Chem. Phys., 21, 8933–8959, https://doi.org/10.5194/acp-21-8933-2021, https://doi.org/10.5194/acp-21-8933-2021, 2021
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Aerosol–cloud interactions in the Arctic are poorly understood largely due to a lack of observational data. We present the first direct, long-term measurements of cloud residuals, i.e. the particles that remain when cloud droplets and ice crystals are dried. These detailed observations of cloud residuals cover more than 2 years, which is unique for the Arctic and globally. This work studies the size distributions of cloud residuals, their seasonality, and dependence on meteorology.
Elin A. McIlhattan, Claire Pettersen, Norman B. Wood, and Tristan S. L'Ecuyer
The Cryosphere, 14, 4379–4404, https://doi.org/10.5194/tc-14-4379-2020, https://doi.org/10.5194/tc-14-4379-2020, 2020
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Snowfall builds the mass of the Greenland Ice Sheet (GrIS) and reduces melt by brightening the surface. We present satellite observations of GrIS snowfall events divided into two regimes: those coincident with ice clouds and those coincident with mixed-phase clouds. Snowfall from ice clouds plays the dominant role in building the GrIS, producing ~ 80 % of total accumulation. The two regimes have similar snowfall frequency in summer, brightening the surface when solar insolation is at its peak.
Fraser King, Andre R. Erler, Steven K. Frey, and Christopher G. Fletcher
Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, https://doi.org/10.5194/hess-24-4887-2020, 2020
Short summary
Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
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Short summary
Under warmer global temperatures, precipitation patterns are expected to shift substantially, with critical impact on the global water-energy budget. In this work, we develop a deep learning model for predicting snow and rain accumulation based on surface radar observations of the lower atmosphere. Our model demonstrates improved skill over traditional methods and provides new insights into the regions of the atmosphere that provide the most significant contributions to high model accuracy.
Under warmer global temperatures, precipitation patterns are expected to shift substantially,...
