Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5701-2022
© 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-15-5701-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Oct 2022
Research article |
| 12 Oct 2022
| 12 Oct 2022
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Simon Pfreundschuh
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
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Cited articles
AERIS/ICARE Data and Services Center: ICARE On-line Data Archive,
ftp://ftp.icare.univ-lille1.fr/SPACEBORNE/MULTI_SENSOR/DARDAR_CLOUD.v2.1.1 (last access: 29 September 2022), 2019. a
Amell, A.: Ice water path retrievals from Meteosat-9 with quantile regression neural networks: code and models, Zenodo [code], https://doi.org/10.5281/zenodo.6570587, 2022a. a
Amell, A.: Ice water path retrievals from Meteosat-9 with quantile regression neural networks: video supplement, Zenodo [video], https://doi.org/10.5281/zenodo.6639443, 2022b. a
Aminou, D. M. A., Jacquet, B., and Pasternak, F.: Characteristics of the Meteosat Second Generation (MSG) radiometer/imager: SEVIRI, in: Sensors, Systems, and Next-Generation Satellites, edited by: Fujisada, H., International Society for Optics and Photonics, SPIE, 3221, 19–31, https://doi.org/10.1117/12.298084, 1997. a
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017. a, b, c, d
Brath, M., Fox, S., Eriksson, P., Harlow, R. C., Burgdorf, M., and Buehler, S. A.: Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures, Atmos. Meas. Tech., 11, 611–632, https://doi.org/10.5194/amt-11-611-2018, 2018. a
Cazenave, Q., Ceccaldi, M., Delanoë, J., Pelon, J., Groß, S., and Heymsfield, A.: Evolution of DARDAR-CLOUD ice cloud retrievals: new parameters and impacts on the retrieved microphysical properties, Atmos. Meas. Tech., 12, 2819–2835, https://doi.org/10.5194/amt-12-2819-2019, 2019. a, b
Chollet, F.: Xception: Deep Learning with Depthwise Separable Convolutions, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
Honolulu, HI, USA, 21–26 July 2017, 1800–1807, https://doi.org/10.1109/CVPR.2017.195, 2017. a
CM SAF: SEVIRI Cloud Physical Products CLAAS Edition 2, Algorithm Theoretical Basis Document SAF/CM/KNMI/ATBD/SEVIRI/CPP 2.2, Satellite Application Facility on Climate Monitoring (CM SAF), https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002, 2016. a
CM SAF: SEVIRI cloud products CLAAS Edition 2.1, Algorithm Theoretical Basis Document SAF/CM/KNMI/ATBD/SEV/CLD 2.5, Satellite Application Facility on Climate Monitoring (CM SAF), https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01, 2020a. a, b
CM SAF: SEVIRI cloud products CLAAS Edition 2.1, Validation Report SAF/CM/KNMI/VAL/SEV/CLD 2.3, Satellite Application Facility on Climate Monitoring (CM SAF), https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01, 2020b. a, b, c
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., Testud, J., Bouniol, D., Heymsfield, A. J., Bansemer, A., Brown, P. R. A., and Forbes, R. M.: Statistical properties of the normalized ice particle size distribution, J. Geophys. Res.-Atmos., 110, D10201, https://doi.org/10.1029/2004JD005405, 2005. a
Delanoë, J. M. E., Heymsfield, A. J., Protat, A., Bansemer, A., and Hogan, R. J.: Normalized particle size distribution for remote sensing application, J. Geophys. Res.-Atmos., 119, 4204–4227, https://doi.org/10.1002/2013JD020700, 2014. a, b
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
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., Rydberg, B., Sagawa, H., Johnston, M. S., and Kasai, Y.: Overview and sample applications of SMILES and Odin-SMR retrievals of upper tropospheric humidity and cloud ice mass, Atmos. Chem. Phys., 14, 12613–12629, https://doi.org/10.5194/acp-14-12613-2014, 2014. 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, b, c
EUMETSAT: High Rate SEVIRI Level 1.5 Image Data - MSG - 0 degree, EUMETSAT [data set], https://data.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI (last access: 29 September 2022), 2009. a
EUMETSAT: Meteosat orbital parameters, EUMETSAT, https://www.eumetsat.int/meteosat-orbital-parameters, last access: 2 May 2022. a
Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., Kaiser, J., and Werscheck, M.: CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI – Edition 2.1, Satellite Application Facility on Climate Monitoring (CM SAF) [data set], https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01, 2020. a, b, c
Forster, P., Storelvmo, T., Armour, K., Collins, W., Dufresne, J.-L., Frame, D., Lunt, D. J., Mauritsen, T., Palmer, M. D., Watanabe, M., Wild, M., and Zhang, H.: The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity, Chapter 7, IPCC, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf (last access: 29 September 2022), 2021. a
GCOS: The Global Climate Observing System 2021: The GCOS Status Report, GCOS-240, World Meteorological Organization, https://gcos.wmo.int/en/gcos-status-report-2021 (last access: 3 May 2022), 2021. a
Hendrycks, D. and Gimpel, K.: Gaussian Error Linear Units (GELUs), arXiv [preprint], https://doi.org/10.48550/arXiv.1606.08415, 8 July 2020. a
Holl, G., Eliasson, S., Mendrok, J., and Buehler, S. A.: SPARE-ICE: Synergistic ice water path from passive operational sensors, J. Geophys. Res.-Atmos., 119, 1504–1523, https://doi.org/10.1002/2013JD020759, 2014. a
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International conference on machine learning, Lille, France, 7–9 July 2015, PMLR, 448–456, 2015. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, in press, https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FullReport.pdf (last access 29 September 2022), 2021. a
Islam, T. and Srivastava, P. K.: Synergistic multi-sensor and multi-frequency retrieval of cloud ice water path constrained by CloudSat collocations, J. Quant. Spectrosc. Ra., 161, 21–34, https://doi.org/10.1016/j.jqsrt.2015.03.022, 2015. a
Jiang, J. H., Su, H., Zhai, C., Shen, T. J., Wu, T., Zhang, J., Cole, J. N. S., von Salzen, K., Donner, L. J., Seman, C., Genio, A. D., Nazarenko, L. S., Dufresne, J.-L., Watanabe, M., Morcrette, C., Koshiro, T., Kawai, H., Gettelman, A., Millán, L., Read, W. G., Livesey, N. J., Kasai, Y., and Shiotani, M.: Evaluating the Diurnal Cycle of Upper-Tropospheric Ice Clouds in Climate Models Using SMILES Observations, J. Atmos. Sci., 72, 1022–1044, https://doi.org/10.1175/JAS-D-14-0124.1, 2015. a
Kingma, D. P. and Ba, J. L.: Adam: A method for stochastic gradient descent, in: ICLR: International Conference on Learning Representations, San Diego, CA, USA, 7–9 May 2015, arXiv, 1–15, https://doi.org/10.48550/arXiv.1412.6980 2015. a
Kox, S., Bugliaro, L., and Ostler, A.: Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing, Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, 2014. a
Mastro, P., Masiello, G., Serio, C., Cimini, D., Ricciardelli, E., Di Paola, F., Hultberg, T., August, T., and Romano, F.: Combined IASI-NG and MWS observations for the retrieval of cloud liquid and ice water path: a deep learning artificial intelligence approach, IEEE J. Sel. Top. Appl., 15, 3313–3322, https://doi.org/10.1109/JSTARS.2022.3166992, 2022. a
Minnis, P., Nguyen, L., Palikonda, R., Heck, P. W., Spangenberg, D. A., Doelling, D. R., Ayers Jr., J. K., W. L. S., Khaiyer, M. M., Trepte, Q. Z., Avey, L. A., Chang, F.-L., Yost, C. R., Chee, T. L., and Szedung, S.-M.: Near-real time cloud retrievals from operational and research meteorological satellites, in: Remote Sensing of Clouds and the Atmosphere XIII, edited by: Picard, R. H., Comeron, A., Schäfer, K., Amodeo, A., and van Weele, M., International Society for Optics and Photonics, SPIE, 7107, 19–26, https://doi.org/10.1117/12.800344, 2008. a
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y.,
Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers,
J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y.,
Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data – Part I: Algorithms, IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011. a, b
Minnis, P., Bedka, K., Trepte, Q., Yost, C. R., Bedka, S. T., Scarino, B. A., Khlopenkov, K., and Khaiyer, M. M.: A Consistent Long-Term Cloud and Clear-Sky Radiation Property Dataset from the Advanced Very High Resolution Radiometer (AVHRR), Climate Algorithm Theoretical Basis Document CDRP-ATBD-0826 01B-30b 1, NOAA National Centers for Environmental Information, https://doi.org/10.7289/V5HT2M8T, 2016a. a
Minnis, P., Hong, G., Sun-Mack, S., Smith Jr., W. L., Chen, Y., and Miller, S. D.: Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method, J. Geophys. Res.-Atmos., 121, 4907–4932, https://doi.org/10.1002/2015JD024456, 2016b. a
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F.-L., Yost, C. R., Smith, W. L., Heck, P. W., Arduini, R. F., Bedka, S. T., Yi, Y., Hong, G., Jin, Z., Painemal, D., Palikonda, R., Scarino, B. R., Spangenberg, D. A., Smith, R. A., Trepte, Q. Z., Yang, P., and Xie, Y.: CERES MODIS Cloud Product Retrievals for Edition 4 – Part I: Algorithm Changes, IEEE T. Geosci. Remote, 59, 2744–2780, https://doi.org/10.1109/TGRS.2020.3008866, 2021. a
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990. 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, https://doi.org/10.2514/6.2012-1295798, 2012. a
Pfreundschuh, S.: quantnn, Version v0.0.4dev, Zenodo [code], https://doi.org/10.5281/zenodo.7127652, 2022. 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., Eriksson, P., Buehler, S. A., Brath, M., Duncan, D., Larsson, R., and Ekelund, R.: Synergistic radar and radiometer retrievals of ice hydrometeors, Atmos. Meas. Tech., 13, 4219–4245, https://doi.org/10.5194/amt-13-4219-2020, 2020. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote, 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017. a
Raspaud, M., Hoese, D., Lahtinen, P., Finkensieper, S., Holl, G., Dybbroe, A., Proud, S., Meraner, A., Zhang, X., Joro, S., Feltz, J., Roberts, W., Ørum Rasmussen, L., Méndez, J. H. B., Zhu, Y., BENR0, strandgren, Daruwala, R., Jasmin, T., Kliche, C., Barnie, T., Sigurðsson, E., Garcia, R. K., Leppelt, T., ColinDuff, Egede, U., LTMeyer, Itkin, M., Goodson, R., and jkotro: pytroll/satpy: Version 0.29.0, Zenodo [code], https://doi.org/10.5281/zenodo.4904606, 2021. a, b, c
Roebeling, R. A., Feijt, A. J., and Stammes, P.: Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17, J. Geophys. Res., 111, D20210, https://doi.org/10.1029/2005JD006990, 2006. 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, https://doi.org/10.1007/978-3-319-24574-4_28,
ISBN: 978-3-319-24574-4, 2015. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: An introduction to Meteosat second generation (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a
Schmid, J.: The SEVIRI instrument, in: Proceedings of the 2000 EUMETSAT meteorological satellite data user's conference, Bologna, Italy,
29 May–2 June 2000, 13–32, https://www-cdn.eumetsat.int/files/2020-04/pdf_ten_msg_seviri_instrument.pdf (last access: 30 September 2022), 2000. a
Stephens, G. L.: Radiation Profiles in Extended Water Clouds. II: Parameterization Schemes, J. Atmos. Sci., 35, 2123–2132, https://doi.org/10.1175/1520-0469(1978)035<2123:RPIEWC>2.0.CO;2, 1978. a, b
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
Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, 2017.
a
Waliser, D. E., Li, J.-L. F., Woods, C. P., Austin, R. T., Bacmeister, J., Chern, J., Del Genio, A., Jiang, J. H., Kuang, Z., Meng, H., Minnis, P., Platnick, S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K., Tompkins, A. M., Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate model challenge with signs and expectations of progress, J. Geophys. Res., 114, D00A21, https://doi.org/10.1029/2008JD010015, 2009. a
Walther, A. and Heidinger, A. K.: Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x, J. Appl. Meteorol. Clim., 51, 1371–1390, https://doi.org/10.1175/JAMC-D-11-0108.1, 2012. a
WMO: Observing Systems Capability Analysis and Review Tool, Satellite: Meteosat-9, WMO, https://space.oscar.wmo.int/satellites/view/meteosat_9, last access: 3 May 2022. a
Wolf, R.: LRIT/HRIT Global Specification, CGMS 03 2.6, Coordination Group for Meteorological Satellites, https://www.cgms-info.org/wp-content/uploads/2021/10/pdf_cgms_03.pdf (last access: 30 September 2022) 1999. a
Yin, J. and Porporato, A.: Diurnal Cloud Cycle Biases in Climate Models, Nat. Commun., 8, 2269, https://doi.org/10.1038/s41467-017-02369-4, 2017. a
Yost, C. R., Minnis, P., Sun-Mack, S., Chen, Y., and Smith, W. L.: CERES MODIS Cloud Product Retrievals for Edition 4 – Part II: Comparisons to CloudSat and CALIPSO, IEEE T. Geosci. Remote, 59, 3695–3724, https://doi.org/10.1109/TGRS.2020.3015155, 2021. a
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.
Geostationary satellites continuously image a given location on Earth, a feature that satellites...
