Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4337-2024
© Author(s) 2024. 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-17-4337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2024
Research article |
| 23 Jul 2024
| 23 Jul 2024
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
Adrià Amell
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Simon Pfreundschuh
CORRESPONDING AUTHOR
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Department of Atmospheric Science, Colorado State University, Fort Collins, USA
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Related authors
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Patrick Eriksson, Anders Emrich, Kalle Kempe, Johan Riesbeck, Alhassan Aljarosha, Olivier Auriacombe, Joakim Kugelberg, Enne Hekma, Roland Albers, Axel Murk, Søren Møller Pedersen, Laurenz John, Jan Stake, Peter McEvoy, Bengt Rydberg, Adam Dybbroe, Anke Thoss, Alessio Canestri, Christophe Accadia, Paolo Colucci, Daniele Gherardi, and Ville Kangas
Atmos. Meas. Tech., 18, 4709–4729, https://doi.org/10.5194/amt-18-4709-2025, https://doi.org/10.5194/amt-18-4709-2025, 2025
Short summary
Short summary
The Arctic Weather Satellite (AWS), developed by the European Space Agency, highlights a new approach in satellite design, aiming to expand the network of operational microwave sensors cost-effectively. Launched in August 2024, AWS features a 19-channel microwave cross-track radiometer. Notably, it introduces groundbreaking channels at 325.15 GHz. In addition, AWS acts as the stepping stone to a suggested constellation of satellites, denoted as EUMETSAT Polar System Sterna.
Eleanor May and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2025-2190, https://doi.org/10.5194/egusphere-2025-2190, 2025
Short summary
Short summary
The vertical distribution of atmospheric ice impacts Earth's weather and climate. The Ice Cloud Imager (ICI) will measure at microwave and sub-millimetre frequencies, which are well suited to detect atmospheric ice. In this study, a machine learning model is trained on ICI simulations. Results show that the vertical distribution of ice can be derived from ICI observations, and that ICI could offer a valuable data source that complements existing radar- and lidar-based measurements.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Karina McCusker, Anthony J. Baran, Chris Westbrook, Stuart Fox, Patrick Eriksson, Richard Cotton, Julien Delanoë, and Florian Ewald
Atmos. Meas. Tech., 17, 3533–3552, https://doi.org/10.5194/amt-17-3533-2024, https://doi.org/10.5194/amt-17-3533-2024, 2024
Short summary
Short summary
Polarised radiative transfer simulations are performed using an atmospheric model based on in situ measurements. These are compared to large polarisation measurements to explore whether such measurements can provide information on cloud ice, e.g. particle shape and orientation. We find that using oriented particle models with shapes based on imagery generally allows for accurate simulations. However, results are sensitive to shape assumptions such as the choice of single crystals or aggregates.
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
Atmos. Meas. Tech., 17, 515–538, https://doi.org/10.5194/amt-17-515-2024, https://doi.org/10.5194/amt-17-515-2024, 2024
Short summary
Short summary
The latest version of the GPROF retrieval algorithm that produces global precipitation estimates using observations from the Global Precipitation Measurement mission is validated against ground-based radars. The validation shows that the algorithm accurately estimates precipitation on scales ranging from continental to regional. In addition, we validate candidates for the next version of the algorithm and identify principal challenges for further improving space-borne rain measurements.
Michael Kiefer, Dale F. Hurst, Gabriele P. Stiller, Stefan Lossow, Holger Vömel, John Anderson, Faiza Azam, Jean-Loup Bertaux, Laurent Blanot, Klaus Bramstedt, John P. Burrows, Robert Damadeo, Bianca Maria Dinelli, Patrick Eriksson, Maya García-Comas, John C. Gille, Mark Hervig, Yasuko Kasai, Farahnaz Khosrawi, Donal Murtagh, Gerald E. Nedoluha, Stefan Noël, Piera Raspollini, William G. Read, Karen H. Rosenlof, Alexei Rozanov, Christopher E. Sioris, Takafumi Sugita, Thomas von Clarmann, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 16, 4589–4642, https://doi.org/10.5194/amt-16-4589-2023, https://doi.org/10.5194/amt-16-4589-2023, 2023
Short summary
Short summary
We quantify biases and drifts (and their uncertainties) between the stratospheric water vapor measurement records of 15 satellite-based instruments (SATs, with 31 different retrievals) and balloon-borne frost point hygrometers (FPs) launched at 27 globally distributed stations. These comparisons of measurements during the period 2000–2016 are made using robust, consistent statistical methods. With some exceptions, the biases and drifts determined for most SAT–FP pairs are < 10 % and < 1 % yr−1.
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros
Atmos. Meas. Tech., 15, 6907–6933, https://doi.org/10.5194/amt-15-6907-2022, https://doi.org/10.5194/amt-15-6907-2022, 2022
Short summary
Short summary
We used methods from the field of artificial intelligence to train an algorithm to estimate rain from satellite observations. In contrast to other methods, our algorithm not only estimates rain, but also the uncertainty of the estimate. Using independent measurements from rain gauges, we show that our method performs better than currently available methods and that the provided uncertainty estimates are reliable. Our method makes satellite-based measurements of rain more accurate and reliable.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad
Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, https://doi.org/10.5194/amt-15-5033-2022, 2022
Short summary
Short summary
The Global Precipitation Measurement mission is an international satellite mission providing regular global rain measurements. We present two newly developed machine-learning-based implementations of one of the algorithms responsible for turning the satellite observations into rain measurements. We show that replacing the current algorithm with a neural network improves the accuracy of the measurements. A neural network that also makes use of spatial information unlocks further improvements.
William G. Read, Gabriele Stiller, Stefan Lossow, Michael Kiefer, Farahnaz Khosrawi, Dale Hurst, Holger Vömel, Karen Rosenlof, Bianca M. Dinelli, Piera Raspollini, Gerald E. Nedoluha, John C. Gille, Yasuko Kasai, Patrick Eriksson, Christopher E. Sioris, Kaley A. Walker, Katja Weigel, John P. Burrows, and Alexei Rozanov
Atmos. Meas. Tech., 15, 3377–3400, https://doi.org/10.5194/amt-15-3377-2022, https://doi.org/10.5194/amt-15-3377-2022, 2022
Short summary
Short summary
This paper attempts to provide an assessment of the accuracy of 21 satellite-based instruments that remotely measure atmospheric humidity in the upper troposphere of the Earth's atmosphere. The instruments made their measurements from 1984 to the present time; however, most of these instruments began operations after 2000, and only a few are still operational. The objective of this study is to quantify the accuracy of each satellite humidity data set.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
Short summary
Short summary
We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Jie Gong, Dong L. Wu, and Patrick Eriksson
Earth Syst. Sci. Data, 13, 5369–5387, https://doi.org/10.5194/essd-13-5369-2021, https://doi.org/10.5194/essd-13-5369-2021, 2021
Short summary
Short summary
Launched from the International Space Station, the IceCube radiometer orbited the Earth for 15 months and collected the first spaceborne radiance measurements at 874–883 GHz. This channel is uniquely important to fill in the sensitivity gap between operational visible–infrared and microwave remote sensing for atmospheric cloud ice and snow. This paper delivers the IceCube Level 1 radiance data processing algorithm and provides a data quality evaluation and discussion on its scientific merit.
Francesco Grieco, Kristell Pérot, Donal Murtagh, Patrick Eriksson, Bengt Rydberg, Michael Kiefer, Maya Garcia-Comas, Alyn Lambert, and Kaley A. Walker
Atmos. Meas. Tech., 14, 5823–5857, https://doi.org/10.5194/amt-14-5823-2021, https://doi.org/10.5194/amt-14-5823-2021, 2021
Short summary
Short summary
We present improved Odin/SMR mesospheric H2O concentration and temperature data sets, reprocessed assuming a bigger sideband leakage of the instrument. The validation study shows how the improved SMR data sets agree better with other instruments' observations than the old SMR version did. Given their unique time extension and geographical coverage, and H2O being a good tracer of mesospheric circulation, the new data sets are valuable for the study of dynamical processes and multi-year trends.
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, https://doi.org/10.5194/amt-14-3427-2021, 2021
Short summary
Short summary
Oriented nonspherical ice particles induce polarization that is ignored when cloud-sensitive satellite observations are used in numerical weather prediction systems. We present a simple approach for approximating particle orientation, requiring minor adaption of software and no additional calculation burden. With this approach, the system realistically simulates the observed polarization patterns, increasing the physical consistency between instruments with different polarizations.
Inderpreet Kaur, Patrick Eriksson, Simon Pfreundschuh, and David Ian Duncan
Atmos. Meas. Tech., 14, 2957–2979, https://doi.org/10.5194/amt-14-2957-2021, https://doi.org/10.5194/amt-14-2957-2021, 2021
Short summary
Short summary
Currently, cloud contamination in microwave humidity channels is addressed using filtering schemes. We present an approach to correct the cloud-affected microwave humidity radiances using a Bayesian machine learning technique. The technique combines orthogonal information from microwave channels to obtain a probabilistic prediction of the clear-sky radiances. With this approach, we are able to predict bias-free clear-sky radiances with well-represented case-specific uncertainty estimates.
Robin Ekelund, Patrick Eriksson, and Michael Kahnert
Atmos. Meas. Tech., 13, 6933–6944, https://doi.org/10.5194/amt-13-6933-2020, https://doi.org/10.5194/amt-13-6933-2020, 2020
Short summary
Short summary
Raindrops become flattened due to aerodynamic drag as they increase in mass and fall speed. This study calculated the electromagnetic interaction between microwave radiation and non-spheroidal raindrops. The calculations are made publicly available to the scientific community, in order to promote accurate representations of raindrops in measurements. Tests show that the drop shape can have a noticeable effect on microwave observations of heavy rainfall.
Cited articles
Abel, S. J. and Boutle, I. A.: An improved representation of the raindrop size distribution for single-moment microphysics schemes, Q. J. Roy. Meteor. Soc., 138, 2151–2162, https://doi.org/10.1002/qj.1949, 2012. a
Amell, A. and Pfreundschuh, S.: SEE-GEO/ccic: Paper publication, Version 0.1, Zenodo [code], https://doi.org/10.5281/zenodo.8278127, 2023. a, b, c, d
Amell, A., Eriksson, P., and Pfreundschuh, S.: Ice water path retrievals from Meteosat-9 using quantile regression neural networks, Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, 2022. a, b
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer Normalization, arXiv [preprint], https://doi.org/10.48550/arXiv.1607.06450, 21 July 2016. a
Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations, Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, 2023. a
Bennett, R.: Meteorological and Navigation (Met/Nav) Data, Version 1.0, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.26023/KJDH-MXGE-HK0V, 2019. a
Bony, S., Stevens, B., Frierson, D. M., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
Brunet, G., Shapiro, M., Hoskins, B., Moncrieff, M., Dole, R., Kiladis, G. N., Kirtman, B., Lorenc, A., Mills, B., Morss, R., Polavarapu, S., Rogers, D., Schaake, J., and Shukla, J.: Collaboration of the Weather and Climate Communities to Advance Subseasonal-to-Seasonal Prediction, B. Am. Meteorol. Soc., 91, 1397–1406, https://doi.org/10.1175/2010BAMS3013.1, 2010. a
Buehler, S. A., Mendrok, J., Eriksson, P., Perrin, A., Larsson, R., and Lemke, O.: ARTS, the Atmospheric Radiative Transfer Simulator – version 2.2, the planetary toolbox edition, Geosci. Model Dev., 11, 1537–1556, https://doi.org/10.5194/gmd-11-1537-2018, 2018. a
Davison, C. R., Strapp, J. W., Lilie, L. E., Ratvasky, T. P., and Dumont, C.: Isokinetic TWC Evaporator Probe: Calculations and Systemic Error Analysis, in: 8th AIAA Atmospheric and Space Environments Conference, Washington, D.C., USA, 13–17 June 2016, AIAA 2016-4060, https://doi.org/10.2514/6.2016-4060, 2016. a
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res.-Atmos., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a
Delanoë, J., Protat, A., Vinson, J.-P., Brett, W., Caudoux, C., Bertrand, F., Parent du Châtelet, J., Hallali, R., Barthes, L., Haeffelin, M., and Dupont, J.-C.: BASTA: A 95-GHz FMCW Doppler Radar for Cloud and Fog Studies, J. Atmos. Ocean. Tech., 33, 1023–1038, https://doi.org/10.1175/JTECH-D-15-0104.1, 2016. a
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical Composition, Cloud and Climate Coupling Experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res.-Atmos., 115, D00J15, https://doi.org/10.1029/2009JD013104, 2010. a, b, c
Deng, M., Mace, G. G., Wang, Z., and Lawson, R. P.: Evaluation of Several A-Train Ice Cloud Retrieval Products with In Situ Measurements Collected during the SPARTICUS Campaign, J. Appl. Meteorol. Clim., 52, 1014–1030, https://doi.org/10.1175/JAMC-D-12-054.1, 2013a. a, b
Deng, M., Mace, G. G., Wang, Z., and Lawson, R. P.: Evaluation of Several A-Train Ice Cloud Retrieval Products with In Situ Measurements Collected during the SPARTICUS Campaign, J. Appl. Meteorol. Clim., 52, 1014–1030, https://doi.org/10.1175/JAMC-D-12-054.1, 2013b. a, b
Deng, M., Mace, G. G., Wang, Z., and Berry, E.: CloudSat 2C-ICE product update with a new Ze parameterization in lidar-only region, J. Geophys. Res.-Atmos., 120, 12198–12208, https://doi.org/10.1002/2015JD023600, 2015. a
Deng, M., Mace, G. G., Wang, Z., and Berry, E.: CloudSat 2C-ICE product, CloudSat Data Processing Center [data set], https://www.cloudsat.cira.colostate.edu/data-products/2c-ice (last access: 1 August 2023), 2023. a
Dezitter, F., Grandin, A., Brenguier, J.-L., Hervy, F., Schlager, H., Villedieu, P., and Zalamansky, G.: HAIC - High Altitude Ice Crystals, in: 5th AIAA Atmospheric and Space Environments Conference, San Diego, CA, USA, 24–27 June 2013, AIAA 2013-2674, https://doi.org/10.2514/6.2013-2674, 2013. a
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018. a, b
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Blower, J., Caron, J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M., Raspaud, M., Horne, R., Whiteaker, T., Blodgett, D., Zender, C., Lee, D., Hassell, D., Snow, A. D., Kölling, T., Allured, D., Jelenak, A., Soerensen, A. M., Gaultier, L., and Herlédan, S.: NetCDF Climate and Forecast (CF) Metadata Conventions, version 1.10, NetCDF, https://cfconventions.org/Data/cf-conventions/cf-conventions-1.10/cf-conventions.html (last access: 22 May 2023), 2022. a
Eliasson, S., Buehler, S. A., Milz, M., Eriksson, P., and John, V. O.: Assessing observed and modelled spatial distributions of ice water path using satellite data, Atmos. Chem. Phys., 11, 375–391, https://doi.org/10.5194/acp-11-375-2011, 2011. a
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018. a
Eriksson, P., Rydberg, B., Mattioli, V., Thoss, A., Accadia, C., Klein, U., and Buehler, S. A.: Towards an operational Ice Cloud Imager (ICI) retrieval product, Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, 2020. a
Esmaili, R. B., Tian, Y., Vila, D. A., and Kim, K.-M.: A Lagrangian analysis of cold cloud clusters and their life cycles with satellite observations, J. Geophys. Res.-Atmos., 121, 11723–11738, https://doi.org/10.1002/2016JD025653, 2016. a
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze Jr., R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021. a, b, c, d, e, f, g, h
Field, P. R., Heymsfield, A. J., and Bansemer, A.: Snow Size Distribution Parameterization for Midlatitude and Tropical Ice Clouds, J. Atmos. Sci., 64, 4346–4365, https://doi.org/10.1175/2007JAS2344.1, 2007. a, b, c
Fiolleau, T. and Roca, R.: Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: method and sampling considerations, Q. J. Roy. Meteor. Soc., 139, 941–953, https://doi.org/10.1002/qj.2174, 2013. a
Foster, M. J., Phillips, C., Heidinger, A. K., and NOAA CDR Program: NOAA Climate Data Record (CDR) of Advanced Very High Resolution Radiometer (AVHRR) and High-resolution Infra-Red Sounder (HIRS) Reflectance, Brightness Temperature, and Cloud Products from Pathfinder Atmospheres - Extended (PATMOS-x), Version 6.0, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5X9287S, 2021. a
Foster, M. J., Phillips, C., Heidinger, A. K., Borbas, E. E., Li, Y., Menzel, W. P., Walther, A., and Weisz, E.: PATMOS-x Version 6.0: 40 Years of Merged AVHRR and HIRS Global Cloud Data, J. Climate, 36, 1143–1160, https://doi.org/10.1175/JCLI-D-22-0147.1, 2023. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a
Heymsfield, G. M. and Lin, T.: GPM Ground Validation Cloud Radar System (CRS) OLYMPEX, NASA Global Hydrometeorology Resource Center DAAC, Huntsville, Alabama, U.S.A. [data set], https://doi.org/10.5067/GPMGV/OLYMPEX/CRS/DATA101, 2017. a
Houze, R. A., McMurdie, L. A., Petersen, W. A., Schwaller, M. R., Baccus, W., Lundquist, J. D., Mass, C. F., Nijssen, B., Rutledge, S. A., Hudak, D. R., Tanelli, S., Mace, G. G., Poellot, M. R., Lettenmaier, D. P., Zagrodnik, J. P., Rowe, A. K., DeHart, J. C., Madaus, L. E., Barnes, H. C., and Chandrasekar, V.: The Olympic Mountains Experiment (OLYMPEX), B. Am. Meteorol. Soc., 98, 2167–2188, https://doi.org/10.1175/BAMS-D-16-0182.1, 2017. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in Python, Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015. a
Janowiak, J., Joyce, B., and Xie, P.: NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], Greenbelt, MD, https://doi.org/10.5067/P4HZB9N27EKU, 2017. a, b
Janowiak, J. E., Joyce, R. J., and Yarosh, Y.: A Real-Time Global Half-Hourly Pixel-Resolution Infrared Dataset and Its Applications, B. Am. Meteorol. Soc., 82, 205–218, https://doi.org/10.1175/1520-0477(2001)082<0205:ARTGHH>2.3.CO;2, 2001. a
Knapp, K. R. and NOAA CDR Program: NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature, Version 2, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V59P2ZKR, 2014. a, b
Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J., Funk, C., Helms, C. N., Hennon, C. C., Holmes, C. D., Huffman, G. J., Kossin, J. P., Lee, H.-T., Loew, A., and Magnusdottir, G.: Globally Gridded Satellite Observations for Climate Studies, B. Am. Meteorol. Soc., 92, 893–907, https://doi.org/10.1175/2011BAMS3039.1, 2011. a, b, c
Li, L., Heymsfield, G. M., Racette, P. E., Tian, L., and Zenker, E.: A 94-GHz Cloud Radar System on a NASA High-Altitude ER-2 Aircraft, J. Atmos. Ocean. Tech., 21, 1378–1388, https://doi.org/10.1175/1520-0426(2004)021<1378:AGCRSO>2.0.CO;2, 2004. a
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S.: A ConvNet for the 2020s, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18–24 June 2022, IEEE, 11966–11976, https://doi.org/10.1109/CVPR52688.2022.01167, 2022. a
Loshchilov, I. and Hutter, F.: SGDR: Stochastic gradient descent with warm restarts, arXiv [preprint], https://doi.org/10.48550/arXiv.1608.03983, 2016. a
Met Office: Cartopy: a cartographic python library with a matplotlib interface, Met Office, Exeter, Devon, http://scitools.org.uk/cartopy (last access: 1 August 2023), 2010–2015. a
National Academies of Sciences, Engineering, and Medicine: Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, The National Academies Press, Washington, DC, https://doi.org/10.17226/24938, ISBN 978-0-309-46757-5, 2018. a
Nayak, M., Witkowski, M., Vane, D., Livermore, T., Rokey, M., Barthuli, M., Gravseth, I. J., Pieper, B., Rodzinak, A., Silva, S., and Woznick, P.: CloudSat Anomaly Recovery and Operational Lessons Learned, in: SpaceOps 2012 Conference, Stockholm, Sweden, 11–15 June 2012, AIAA 2012-1295798, https://doi.org/10.2514/6.2012-1295798, 2012. a
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library, in: Advances in Neural Information Processing Systems, edited by: Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., Curran Associates, Inc., 32, 8024–8035, http://papers.neurips.cc/
9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf (last access: 1 August 2023), 2019. a, b
Perez, F. and Granger, B. E.: IPython: A System for Interactive Scientific Computing, Comput. Sci. Eng., 9, 21–29, https://doi.org/10.1109/MCSE.2007.53, 2007. a
Pfreundschuh, S., Eriksson, P., Duncan, D., Rydberg, B., Håkansson, N., and Thoss, A.: A neural network approach to estimating a posteriori distributions of Bayesian retrieval problems, Atmos. Meas. Tech., 11, 4627–4643, https://doi.org/10.5194/amt-11-4627-2018, 2018. a, b
Pfreundschuh, S., Brown, P. J., Kummerow, C. D., Eriksson, P., and Norrestad, T.: GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm, Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022, 2022a. a
Pfreundschuh, S., Fox, S., Eriksson, P., Duncan, D., Buehler, S. A., Brath, M., Cotton, R., and Ewald, F.: Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems, Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, 2022b. a, b, c
Pfreundschuh, S., Ingemarsson, I., Eriksson, P., Vila, D. A., and Calheiros, A. J. P.: An improved near-real-time precipitation retrieval for Brazil, Atmos. Meas. Tech., 15, 6907–6933, https://doi.org/10.5194/amt-15-6907-2022, 2022c. a, b
Platnick, S., Ackerman, S., King, M., Meyer, K., Menzel, W., Holz, R., Baum, B., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], https://doi.org/10.5067/MODIS/MOD06_L2.061, 2015. a
Poellot, M. R., Heymsfield, A. J., and Bansemer, A.: GPM Ground Validation UND Citation Cloud Microphysics OLYMPEX, NASA Global Hydrometeorology Resource Center DAAC [data set], Huntsville, Alabama, U.S.A., https://doi.org/10.5067/GPMGV/OLYMPEX/MULTIPLE/DATA201, 2017. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Raspaud, M., Hoese, D., Lahtinen, P., Finkensieper, S., Holl, G., Proud, S., Dybbroe, A., Meraner, A., Feltz, J., Zhang, X., strandgren, Joro, S., Roberts, W., Ørum Rasmussen, L., BENR0, Méndez, J. H. B., Zhu, Y., mherbertson, rdaruwala, de Buyl, P., Jasmin, T., Kliche, C., Barnie, T., Sigurðsson, E., Brodehl, S., R. K. Garcia, Leppelt, T., Tsukada, T., and ColinDuff: pytroll/satpy: Version 0.36.0 (2022/04/14), Zenodo [code], https://doi.org/10.5281/zenodo.6460193, 2022. a
Ratvasky, T., Harrah, S., Strapp, J. W., Lilie, L., Proctor, F., Strickland, J., Hunt, P., Bedka, K., Diskin, G., Nowak, J. B., Bui, T. P., Bansemer, A., and Dumont, C.: Summary of the High Ice Water Content (HIWC) RADAR Flight Campaigns, in: International Conference on Icing of Aircraft, Engines, and Structures, SAE International, ISSN 0148-7191, https://doi.org/10.4271/2019-01-2027, 2019. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World Scientific, https://doi.org/10.1142/3171, 2000. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., Springer International Publishing, Cham, 234–241, ISBN 978-3-319-24574-4, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a
SAFIRE: French Doppler Radar RASTA Data, Version 3.0, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.26023/72PW-V4FB-E0C, 2016. a
Sassen, K. and Wang, Z.: Classifying clouds around the globe with the CloudSat radar: 1-year of results, Geophys. Res. Lett., 35, L04805, https://doi.org/10.1029/2007GL032591, 2008. a
Sassen, K. and Wang, Z.: CloudSat 2B-CLDCLASS product, CloudSat Data Processing Center [data set], https://www.cloudsat.cira.colostate.edu/data-products/2b-cldclass (last access: 1 August 2023), 2023. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: THE CLOUDSAT MISSION AND THE A-TRAIN: A New Dimension of Space-Based Observations of Clouds and Precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a, b
Strapp, J., Korolev, A., Ratvasky, T., Potts, R., Protat, A., May, P., Ackerman, A., Fridlind, A., Minnis, P., Haggerty, J., Riley, J. T., Lilie, L. E., and Isaac, G. A.: The High Ice Water Content (HIWC) study of deep convective clouds: Science and technical plan, FAA Rep. DOT/FAA/TC-14/31, http://www.tc.faa.gov/its/worldpac/techrpt/tc14-31.pdf (last access: 16 August 2023), 2016a. a
Strapp, J. W., Lilie, L. E., Ratvasky, T. P., Davison, C. R., and Dumont, C.: Isokinetic TWC Evaporator Probe: Development of the IKP2 and Performance Testing for the HAIC-HIWC Darwin 2014 and Cayenne Field Campaigns, in: 8th AIAA Atmospheric and Space Environments Conference, 13–17 June 2016, Washington, D.C., USA, AIAA 2016-4059, https://doi.org/10.2514/6.2016-4059, 2016b. a
Strapp, W.: French Falcon Isokinetic Evaporator Probe (IKP2) Data, Version 5.0b, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.5065/D61N7ZV7, 2016a. a
Strapp, W.: French Falcon Isokinetic Evaporator Probe (IKP2) Data, Version 5.0, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.5065/D6WW7GDS, 2016b. a
Strapp, W.: Ice Water Content from the Isokinetic Probe, Version 1.0, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.5065/D6RN36KJ, 2017. a
Strapp, W.: Ice Water Content from the Isokinetic Probe, Version 2.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/8V5Y-GB2E-CX07, 2019. a
Stubenrauch, C. J., Kinne, S., Mandorli, G., Rossow, W. B., Winker, D. M., Ackerman, S. A., Chepfer, H., Di Girolamo, L., Garnier, A., Heidinger, A., Karlsson, K.-G., Meyer, K., Minnis, P., Platnick, S., Stengel, M., Sun-Mack, S., Veglio, P., Walther, A., Cai, X., Young, A. H., and Zhao, G.: Lessons Learned from the Updated GEWEX Cloud Assessment Database, Surv. Geophys., 1–50, https://doi.org/10.1007/s10712-024-09824-0, 2024. a
The Python Language Foundation: The Python Language Reference, https://docs.python.org/3/reference/index.html (last access: 1 August 2023), 2018. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L., Treut, H. L., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission: A Global 3D View of Aerosols and Clouds, B. Am. Meteorol. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010. a, b
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018. a
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
The representation of clouds in numerical weather and climate models remains a major challenge...
