Articles | Volume 16, issue 21
https://doi.org/10.5194/amt-16-5305-2023
© Author(s) 2023. 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-16-5305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Deutscher Wetterdienst, Offenbach, Germany
Hans Ertel Centre for Weather Research, Ludwig-Maximilians-Universität, Munich, Germany
Leonhard Scheck
Hans Ertel Centre for Weather Research, Ludwig-Maximilians-Universität, Munich, Germany
Deutscher Wetterdienst, Offenbach, Germany
Christina Stumpf
Deutscher Wetterdienst, Offenbach, Germany
Christina Köpken-Watts
Deutscher Wetterdienst, Offenbach, Germany
Roland Potthast
Deutscher Wetterdienst, Offenbach, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-2473, https://doi.org/10.5194/egusphere-2025-2473, 2025
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ICON XPP is a newly developed Earth System model configuration based on the ICON modeling framework. It merges accomplishments from the recent operational numerical weather prediction model with well-established climate components for the ocean, land and ocean-biogeochemistry. ICON XPP reaches typical targets of a coupled climate simulation, and is able to run long integrations and large-ensemble experiments, making it suitable for climate predictions and projections, and for climate research.
Klaus Vobig, Roland Potthast, and Klaus Stephan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2876, https://doi.org/10.5194/egusphere-2024-2876, 2024
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We present a novel approach to targeted covariance inflation (TCI) which aims to improve the assimilation of 3D radar reflectivity and, eventually, the short-term forecast of reflectivity and precipitation.
Using an operational numerical weather prediction framework, our numerical results show that TCI makes the system accurately generate new reflectivity cells and significantly improves the fractional skill score of forecasts over lead times of up to six hours by up to 10 %.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
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Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
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Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Stefan Geiss, Leonhard Scheck, Alberto de Lozar, and Martin Weissmann
Atmos. Chem. Phys., 21, 12273–12290, https://doi.org/10.5194/acp-21-12273-2021, https://doi.org/10.5194/acp-21-12273-2021, 2021
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This study demonstrates the benefits of using both visible and infrared satellite channels to evaluate clouds in numerical weather prediction models. Combining these highly resolved observations provides significantly more and complementary information than using only infrared observations. The visible observations are particularly sensitive to subgrid water clouds, which are not well constrained by other observations.
Cited articles
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/ (last access: 6 October 2023), 2015. a
Baum, B. A., Soulen, P. F., Strabala, K. I., King, M. D., Ackerman, S. A., Menzel, W. P., and Yang, P.: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS: 2. Cloud thermodynamic phase, J. Geophys. Res.-Atmos., 105, 11781–11792, https://doi.org/10.1029/1999JD901090, 2000. a
Baum, B. A., Yang, P., Heymsfield, A. J., Platnick, S., King, M. D., Hu, Y.-X., and Bedka, S. T.: Bulk Scattering Properties for the Remote Sensing of Ice Clouds. Part II: Narrowband Models., J. Appl. Meteorol., 44, 1896–1911, https://doi.org/10.1175/JAM2309.1, 2005. a
Baum, B. A., Yang, P., Nasiri, S., Heidinger, A. K., Heymsfield, A., and Li, J.: Bulk Scattering Properties for the Remote Sensing of Ice Clouds. Part III: High-Resolution Spectral Models from 100 to 3250 cm−1, J. Appl. Meteorol. Clim., 46, 423, https://doi.org/10.1175/JAM2473.1, 2007. a
Bormann, N., Lawrence, H., and Farnan, J.: Global observing system experiments in the ECMWF assimilation system, ECMWF Technical Memorandum 839, ECMWF, https://doi.org/10.21957/sr184iyz, 2019. a
Coopman, Q., Hoose, C., and Stengel, M.: Detection of Mixed-Phase Convective Clouds by a Binary Phase Information From the Passive Geostationary Instrument SEVIRI, J. Geophys. Res.-Atmos., 124, 5045–5057, https://doi.org/10.1029/2018JD029772, 2019. a
Eresmaa, R. and McNally, A. P.: NWP SAF 137L Profile Data, NWP SAF [data set], https://nwp-saf.eumetsat.int/site/software/atmospheric-profile-data (last access: 6 October 2023), 2016. a
Errico, R. M., Bauer, P., and Mahfouf, J.-F.: Issues Regarding the Assimilation of Cloud and Precipitation Data, J. Atmos. Sci., 64, 3785–3798, https://doi.org/10.1175/2006JAS2044.1, 2007. a
EUMETSAT: NWP SAF RTTOV v13, NWP SAF [code], https://nwp-saf.eumetsat.int/site/software/rttov/rttov-v13/, last access: 6 October 2023. a
Eyre, J. R., Bell, W., Cotton, J., English, S. J., Forsythe, M., Healy, S. B., and Pavelin, E. G.: Assimilation of satellite data in numerical weather prediction. Part II: Recent years, Q. J. Roy. Meteor. Soc., 148, 521–556, https://doi.org/10.1002/qj.4228, 2022. a
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu, E. H., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N., Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data assimilation at operational weather forecasting centres, Q. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202, 2018. a, b
Geer, A. J., Migliorini, S., and Matricardi, M.: All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud, Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019, 2019. a
Geiss, S., Scheck, L., de Lozar, A., and Weissmann, M.: Understanding the model representation of clouds based on visible and infrared satellite observations, Atmos. Chem. Phys., 21, 12273–12290, https://doi.org/10.5194/acp-21-12273-2021, 2021. a, b, c
Goodfellow, I. J., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, Cambridge, MA, USA, 781 pp., ISBN-10: 0262035618, ISBN-13: 978-0262035613, http://www.deeplearningbook.org (last access: 6 October 2023), 2016. a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Roy. Meteor. Soc., 144, 1218–1256, https://doi.org/10.1002/qj.3179, 2018. a
Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large-eddy simulations over Germany using ICON: A comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2016. a
Hu, G., Dance, S. L., Bannister, R. N., Chipilski, H. G., Guillet, O., Macpherson, B., Weissmann, M., and Yussouf, N.: Progress, challenges, and future steps in data assimilation for convection-permitting numerical weather prediction: Report on the virtual meeting held on 10 and 12 November 2021, Atmos. Sci. Lett., 24, e1130, https://doi.org/10.1002/asl.1130, 2022. a
Li, J., Geer, A. J., Okamoto, K., Otkin, J. A., Liu, Z., Han, W., and Wang, P.: Satellite All-sky Infrared Radiance Assimilation: Recent Progress and Future Perspectives, Adv. Atmos. Sci., 39, 9–21, https://doi.org/10.1007/s00376-021-1088-9, 2022. a
Marshak, A. and Davis, A.: 3D radiative transfer in cloudy atmospheres, Springer Science & Business Media, 2005. a
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and Parameterization of Effective Radius of Droplets in Warm Stratocumulus Clouds, J. Atmos. Sci., 51, 1823–1842, https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2, 1994. a, b
McFarquhar, G. M., Iacobellis, S., and Somerville, R. C. J.: SCM Simulations of Tropical Ice Clouds Using Observationally Based Parameterizations of Microphysics, J. Climate, 16, 1643–1664, https://doi.org/10/ds4n48, 2003. a, b, c
Mie, G.: Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen, Ann. Phys., 330, 377–445, https://doi.org/10.1002/andp.19083300302, 1908. a
Nagao, T. M. and Suzuki, K.: Temperature-Independent Cloud Phase Retrieval From Shortwave-Infrared Measurement of GCOM-C/SGLI With Comparison to CALIPSO, Earth Space Sci., 8, e2021EA001912, https://doi.org/10.1029/2021EA001912, 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
Okamoto, K.: Evaluation of IR radiance simulation for all-sky assimilation of Himawari-8/AHI in a mesoscale NWP system, Q. J. Roy. Meteor. Soc., 143, 1517–1527, https://doi.org/10.1002/qj.3022, 2017. a
Otkin, J. A. and Potthast, R.: Assimilation of All-Sky SEVIRI Infrared Brightness Temperatures in a Regional-Scale Ensemble Data Assimilation System, Mon. Weather Rev., 147, 4481–4509, https://doi.org/10.1175/MWR-D-19-0133.1, 2019. a
Sakradzija, M., Senf, F., Scheck, L., Ahlgrimm, M., and Klocke, D.: Local impact of stochastic shallow convection on clouds and precipitation in the tropical Atlantic, Mon. Weather Rev., 148, 5041–5062, https://doi.org/10.1175/MWR-D-20-0107.1, 2020. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a, b, c
Saunders, R., Hocking, J., Turner, E., Havemann, S., Geer, A., Lupu, C., Vidot, J., Chambon, P., Köpken-Watts, C., Scheck, L., Stiller, O., Stumpf, C., and Borbas, E.: RTTOV-13: Science and Validation Report, Tech. Rep. NWPSAF-MO-TV-046, EUMETSAT, https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov13/rttov13_svr.pdf (last access: 6 October 2023), 2020. a, b, c
Scheck, L.: FORNADO, GitLab [code], https://gitlab.com/LeonhardScheck/fornado (last access: 6 October 2023), 2021b. a
Scheck, L., Weissmann, M., and Mayer, B.: Efficient Methods to Account for Cloud-Top Inclination and Cloud Overlap in Synthetic Visible Satellite Images, J. Atmos. Ocean. Tech., 35, 665–685, https://doi.org/10/gdc287, 2018. a, b, c
Scheck, L., Weissmann, M., and Bach, L.: Assimilating Visible Satellite Images for Convective-Scale Numerical Weather Prediction: A Case-Study, Q. J. Roy. Meteor. Soc., 146, 3165–3186, https://doi.org/10.1002/qj.3840, 2020. a, b
Schröttle, J., Weissmann, M., Scheck, L., and Hutt, A.: Assimilating Visible and Infrared Radiances in Idealized Simulations of Deep Convection, Mon. Weather Rev., 148, 4357–4375, https://doi.org/10.1175/MWR-D-20-0002.1, 2020. a
Stamnes, K., Tsay, S.-C., Jayaweera, K., and Wiscombe, W.: Numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media, Appl. Optics, 27, 2502–2509, https://doi.org/10.1364/AO.27.002502, 1988. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas, V., Biercamp, J., Brueck, M., Brune, S., Buehler, S. A., Burkhardt, U., Cioni, G., Costa-surós, M., Crewell, S., Crüger, T., Deneke, H., Friederichs, P., Henken, C. C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N., Köhler, M., van Laar, T. W., Li, P., Löhnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C., Naumann, A. K., Peters, K., Poll, S., Quaas, J., Röber, N., Rochetin, N., Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski, M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J., Tessiot, O., Vercauteren, N., Vial, J., Voigt, A., and Zängl, G.: The Added Value of Large-Eddy and Storm-Resolving Models for Simulating Clouds and Precipitation, J. Meteorol. Soc. Jpn. Ser. II, 98, 395–435, https://doi.org/10.2151/jmsj.2020-021, 2020. a
Valmassoi, A., Keller, J. D., Kleist, D. T., English, S., Ahrens, B., Ďurán, I. B., Bauernschubert, E., Bosilovich, M. G., Fujiwara, M., Hersbach, H., Lei, L., Löhnert, U., Mamnun, N., Martin, C. R., Moore, A., Niermann, D., Ruiz, J. J., and Scheck, L.: Current challenges and future directions in data assimilation and reanalysis, B. Am. Meteorol. Soc., 104, E756–E767, https://doi.org/10.1175/BAMS-D-21-0331.1, 2022. a
Wyser, K.: The Effective Radius in Ice Clouds, J. Climate, 11, 1793–1802, https://doi.org/10/dphsv7, 1998. a, b, c
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc., 141, 563–579, 2015. a
Zhou, Y., Liu, Y., and Liu, C.: A machine learning-based method to account for 3D Short-Wave radiative effects in 1D satellite observation operators, J. Quant. Spectrosc. Ra., 275, 107891, https://doi.org/10.1016/j.jqsrt.2021.107891, 2021. a
Short summary
Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Near-infrared satellite images have information on clouds that is complementary to what is...