Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-1091-2024
© 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/gmd-17-1091-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
09 Feb 2024
Methods for assessment of models |
| 09 Feb 2024
| 09 Feb 2024
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
Marie-Adèle Magnaldo
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Sébastien Riette
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Christine Lac
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
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Théophane Costabloz, Frédéric Burnet, Christine Lac, Pauline Martinet, Julien Delanoë, Susana Jorquera, and Maroua Fathalli
Atmos. Chem. Phys., 25, 6539–6573, https://doi.org/10.5194/acp-25-6539-2025, https://doi.org/10.5194/acp-25-6539-2025, 2025
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This study documents vertical profiles of liquid water content (LWC) in fogs from in situ measurements collected during the SOFOG3D field campaign in 2019–2020. The analysis of 140 vertical profiles reveals a reverse trend in LWC, maximum values at ground decreasing with height, during stable conditions in optically thin fogs, evolving towards quasi-adiabatic characteristics when fogs become thick. These results offer new perspectives for better constraining fog numerical simulations.
Adrien Marcel, Sébastien Riette, Didier Ricard, and Christine Lac
EGUsphere, https://doi.org/10.5194/egusphere-2025-2504, https://doi.org/10.5194/egusphere-2025-2504, 2025
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This paper provides substantial consistent updates to the atmospheric boundary layer schemes of the AROME model, yet they can be used for both forecasting and climate modelling. The study employs a single-column model versus large eddy simulations comparison and uses a machine learning tool to calibrate parameterizations. The model's ability to simulate shallow clouds has been enhanced, especially for shallow precipitating cumulus and stratocumulus clouds.
Ghislain Picard and Quentin Libois
Geosci. Model Dev., 17, 8927–8953, https://doi.org/10.5194/gmd-17-8927-2024, https://doi.org/10.5194/gmd-17-8927-2024, 2024
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The Two-streAm Radiative TransfEr in Snow (TARTES) is a radiative transfer model to compute snow albedo in the solar domain and the profiles of light and energy absorption in a multi-layered snowpack whose physical properties are user defined. It uniquely considers snow grain shape flexibly, based on recent insights showing that snow does not behave as a collection of ice spheres but instead as a random medium. TARTES is user-friendly yet performs comparably to more complex models.
Zili He, Quentin Libois, Najda Villefranque, Hartwig Deneke, Jonas Witthuhn, and Fleur Couvreux
Atmos. Chem. Phys., 24, 11391–11408, https://doi.org/10.5194/acp-24-11391-2024, https://doi.org/10.5194/acp-24-11391-2024, 2024
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This study uses observations and simulations to analyze how cumulus clouds affect spacial solar radiation variability on the ground. Results show that the simulations reproduce the observations well and improve understanding of cloud impacts on radiation. The research also indicates that a few strategically placed sensors, capitalizing on measurement timing, can effectively measure these variations, aiding in the development of detailed weather prediction models.
Romilly Harris Stuart, Amaëlle Landais, Laurent Arnaud, Christo Buizert, Emilie Capron, Marie Dumont, Quentin Libois, Robert Mulvaney, Anaïs Orsi, Ghislain Picard, Frédéric Prié, Jeffrey Severinghaus, Barbara Stenni, and Patricia Martinerie
The Cryosphere, 18, 3741–3763, https://doi.org/10.5194/tc-18-3741-2024, https://doi.org/10.5194/tc-18-3741-2024, 2024
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Ice core δO2/N2 records are useful dating tools due to their local insolation pacing. A precise understanding of the physical mechanism driving this relationship, however, remain ambiguous. By compiling data from 15 polar sites, we find a strong dependence of mean δO2/N2 on accumulation rate and temperature in addition to the well-documented insolation dependence. Snowpack modelling is used to investigate which physical properties drive the mechanistic dependence on these local parameters.
Cheikh Dione, Martial Haeffelin, Frédéric Burnet, Christine Lac, Guylaine Canut, Julien Delanoë, Jean-Charles Dupont, Susana Jorquera, Pauline Martinet, Jean-François Ribaud, and Felipe Toledo
Atmos. Chem. Phys., 23, 15711–15731, https://doi.org/10.5194/acp-23-15711-2023, https://doi.org/10.5194/acp-23-15711-2023, 2023
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This paper documents the role of thermodynamics and turbulence in the fog life cycle over southwestern France. It is based on a unique dataset collected during the SOFOG3D field campaign in autumn and winter 2019–2020. The paper gives a threshold for turbulence driving the different phases of the fog life cycle and the role of advection in the night-time dissipation of fog. The results can be operationalised to nowcast fog and improve short-range forecasts in numerical weather prediction models.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Ian Boutle, Wayne Angevine, Jian-Wen Bao, Thierry Bergot, Ritthik Bhattacharya, Andreas Bott, Leo Ducongé, Richard Forbes, Tobias Goecke, Evelyn Grell, Adrian Hill, Adele L. Igel, Innocent Kudzotsa, Christine Lac, Bjorn Maronga, Sami Romakkaniemi, Juerg Schmidli, Johannes Schwenkel, Gert-Jan Steeneveld, and Benoît Vié
Atmos. Chem. Phys., 22, 319–333, https://doi.org/10.5194/acp-22-319-2022, https://doi.org/10.5194/acp-22-319-2022, 2022
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Fog forecasting is one of the biggest problems for numerical weather prediction. By comparing many models used for fog forecasting with others used for fog research, we hoped to help guide forecast improvements. We show some key processes that, if improved, will help improve fog forecasting, such as how water is deposited on the ground. We also showed that research models were not themselves a suitable baseline for comparison, and we discuss what future observations are required to improve them.
Olivier Nuissier, Fanny Duffourg, Maxime Martinet, Véronique Ducrocq, and Christine Lac
Atmos. Chem. Phys., 20, 14649–14667, https://doi.org/10.5194/acp-20-14649-2020, https://doi.org/10.5194/acp-20-14649-2020, 2020
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This present article demonstrates how numerical simulations with very high horizontal resolution (150 m) can contribute to better understanding the key physical processes (turbulence and microphysics) that lead to Mediterranean heavy precipitation.
Cited articles
ACCORD: http://www.umr-cnrm.fr/accord/, last access: 11 January 2024. a
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, 2008. a
Ahlgrimm, M. and Forbes, R.: The Impact of Low Clouds on Surface Shortwave Radiation in the ECMWF Model, Mon. Weather Rev., 140, 3783–3794, https://doi.org/10.1175/mwr-d-11-00316.1, 2012. a, b, c
Amodei, M. and Stein, J.: Deterministic and fuzzy verification methods for a hierarchy of numerical models, Meteorol. Appl., 16, 191–203, https://doi.org/10.1002/met.101, 2009. a
Antoine, S., Honnert, R., Seity, Y., Vié, B., Burnet, F., and Martinet, P.: Evaluation of an improved AROME configuration for fog forecasts during the SOFOG3D campaign, Weather Forecast., 38, 1605–1620, https://doi.org/10.1175/WAF-D-22-0215.1, 2023. a, b
Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., de Pison, F. M., and Antonanzas-Torres, F.: Review of photovoltaic power forecasting, Sol. Energy, 136, 78–111, https://doi.org/10.1016/j.solener.2016.06.069, 2016. a, b, c, d
Barrett, A. I., Hogan, R. J., and Forbes, R. M.: Why are mixed-phase altocumulus clouds poorly predicted by large-scale models? Part 2. Vertical resolution sensitivity and parameterization, J. Geophys. Res.-Atmos., 122, 9927–9944, https://doi.org/10.1002/2016jd026322, 2017. a
Bendix, J., Thies, B., Cermak, J., and Nauß, T.: Ground Fog Detection from Space Based on MODIS Daytime Data – A Feasibility Study, Weather Forecast., 20, 989–1005, https://doi.org/10.1175/WAF886.1, 2005. a
Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D13205, https://doi.org/10.1029/2008JD011115, 2009. a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Hansen-Sass, B., Homleid, M., Hortal, M., Ivarsson, K., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Muñoz, D. S., Subias, A., Tijm, S., Toll, V., Yang, X., and Køltzow, M. Ø.: The HARMONIE–AROME model configuration in the ALADIN–HIRLAM NWP system, Mon. Weather Rev., 145, 1919–1935, 2017. a
Betti, A., Blanc, P., David, M., Saint-Drenan, Y.-M., Driesse, A., Freeman, J., Fritz, R., Gueymard, C., Habte, A., Höller, R., Huang, J., Kazantzidis, A., Kleissl, J., Köhler, C., Landelius, T., Lara-Fanego, V., Lorenz, E., Lauret, P., Martin, L., Mehos, M., Meyer, R., Myers, D., Nielsen, K. P., Perez, R., Peruchena, C. F., Polo, J., Renné, D., Ramírez, L., Remund, J., Ruiz-Arias, J. A., Sengupta, M., Silva, M., Spieldenner, D., Stoffel, T., Suri, M., Wilbert, S., Wilcox, S., Vignola, F., Wang, P., Xie, Y., and Zarzalejo, L. F.: Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications: Third Edition, Tech. rep., International Energy Agency, https://iea-pvps.org/key-topics/best-practices-handbook-for-the-collection-and-use-of-solar-resource-data-for-solar-energy-applications-third-edition/ (last access: 31 January 2024), 2021. a, b, c, d, e
Bougeault, P. and Lacarrere, P.: Parameterization of orography-induced turbulence in a mesobeta–scale model, Mon. Weather Rev., 117, 1872–1890, 1989. a
Bozzo, A., Benedetti, A., Flemming, J., Kipling, Z., and Rémy, S.: An aerosol climatology for global models based on the tropospheric aerosol scheme in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 13, 1007–1034, https://doi.org/10.5194/gmd-13-1007-2020, 2020. a
Brooks, M. E., Hogan, R. J., and Illingworth, A. J.: Parameterizing the difference in cloud fraction defined by area and by volume as observed with radar and lidar, J. Atmos. Sci., 62, 2248–2260, 2005. a
Brousseau, P., Seity, Y., Ricard, D., and Léger, J.: Improvement of the forecast of convective activity from the AROME-France system, Q. J. Roy. Meteor. Soc., 142, 2231–2243, https://doi.org/10.1002/qj.2822, 2016. a
Chiriaco, M., Dupont, J.-C., Bastin, S., Badosa, J., Lopez, J., Haeffelin, M., Chepfer, H., and Guzman, R.: ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite, Earth Syst. Sci. Data, 10, 919–940, https://doi.org/10.5194/essd-10-919-2018, 2018. a
Chu, Y., Li, M., Pedro, H. T., and Coimbra, C. F.: A network of sky imagers for spatial solar irradiance assessment, Renew. Energ., 187, 1009–1019, https://doi.org/10.1016/j.renene.2022.01.032, 2022. a
Copernicus Atmosphere Monitoring Service: Copernicus Atmosphere Data Store, https://atmosphere.copernicus.eu/ last access: 11 January 2024 a
Cros, S., Badosa, J., Szantaï, A., and Haeffelin, M.: Reliability Predictors for Solar Irradiance Satellite-Based Forecast, Energies, 13, 5566, https://doi.org/10.3390/en13215566, 2020. a
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme allowing for mesoscale and large-eddy simulations, Q. J. Roy. Meteor. Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000. a
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Deventer, W. V., Horan, B., and Stojcevski, A.: Forecasting of photovoltaic power generation and model optimization: A review, Renew. Sustain. Energ. Rev., 81, 912–928, https://doi.org/10.1016/j.rser.2017.08.017, 2018. a, b, c, d
Dubus, L., Brayshaw, D. J., Huertas-Hernando, D., Radu, D., Sharp, J., Zappa, W., and Stoop, L. P.: Towards a future-proof climate database for European energy system studies, Environ. Res. Lett., 17, 121001, https://doi.org/10.1088/1748-9326/aca1d3, 2022. a
Engdahl, B. J. K., Thompson, G., and Bengtsson, L.: Improving the representation of supercooled liquid water in the HARMONIE-AROME weather forecast model, Tellus A, 72, 1–18, 2020. a
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon. Weather Rev., 142, 3425–3445, https://doi.org/10.1175/mwr-d-13-00325.1, 2014. a
Fouquart, Y. and Bonnel, B.: Computations of solar heating of the Earth's atmosphere: A new parameterization, Beitraege zur Physik der Atmosphaere, 53, 35–60, 1980. a
Gueymard, C. A.: Cloud and albedo enhancement impacts on solar irradiance using high-frequency measurements from thermopile and photodiode radiometers. Part 1: Impacts on global horizontal irradiance, Sol. Energ., 153, 755–765, 2017. a
Hogan, R. J., Francis, P., Flentje, H., Illingworth, A., Quante, M., and Pelon, J.: Characteristics of mixed-phase clouds. I: Lidar, radar and aircraft observations from CLARE'98, Q. J. Roy. Meteor. Soc., 129, 2089–2116, https://doi.org/10.1256/rj.01.208, 2003. a
ICARE On-line Data Archive, https://www.icare.univ-lille.fr/, last access: 11 January 2024. a
Illingworth, A. J., Hogan, R. J., O'Connor, E., Bouniol, D., Brooks, M. E., Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a
International Energy Agency: Etat du photovolatïque en France, Tech. rep., Agence de l'Environnement et de la Maîtrise de l'Energie and International Energy Agency, 2019. a
Kosmopoulos, P. G., Kazadzis, S., Taylor, M., Athanasopoulou, E., Speyer, O., Raptis, P. I., Marinou, E., Proestakis, E., Solomos, S., Gerasopoulos, E., Amiridis, V., Bais, A., and Kontoes, C.: Dust impact on surface solar irradiance assessed with model simulations, satellite observations and ground-based measurements, Atmos. Meas. Tech., 10, 2435–2453, https://doi.org/10.5194/amt-10-2435-2017, 2017. a
Köhler, C., Steiner, A., Saint-Drenan, Y.-M., Ernst, D., Bergmann-Dick, A., Zirkelbach, M., Bouallègue, Z. B., Metzinger, I., and Ritter, B.: Critical weather situations for renewable energies – Part B: Low stratus risk for solar power, Renew. Energ., 101, 794–803, https://doi.org/10.1016/j.renene.2016.09.002, 2017. a
Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C., Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F., Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P., Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F., Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R., Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.: Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, 2018. a
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P., Redelsperger, J. L., Richard, E., and de Arellano, J. V.-G.: The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations, Ann. Geophys., 16, 90–109, https://doi.org/10.1007/s00585-997-0090-6, 1998. a
Li, J.-L., Forbes, R., Waliser, D., Stephens, G., and Lee, S.: Characterizing the radiative impacts of precipitating snow in the ECMWF integrated forecast system global model, J. Geophys. Res.-Atmos., 119, 9626–9637, 2014a. a
Li, J.-L., Lee, W.-L., Waliser, D., David Neelin, J., Stachnik, J. P., and Lee, T.: Cloud-precipitation-radiation-dynamics interaction in global climate models: A snow and radiation interaction sensitivity experiment, J. Geophys. Res.-Atmos., 119, 3809–3824, 2014b. a
Li, J.-L. F., Xu, K.-M., Lee, W.-L., Jiang, J. H., Fetzer, E., Stephens, G., Wang, Y.-H., and Yu, J.-Y.: Exploring Radiation Biases Over the Tropical and Subtropical Oceans Based on Treatments of Frozen-Hydrometeor Radiative Properties in CMIP6 Models, J. Geophys. Res.-Atmos., 127, e2021JD035976, https://doi.org/10.1029/2021JD035976, 2022. a
Logothetis, S.-A., Salamalikis, V., Wilbert, S., Remund, J., Zarzalejo, L. F., Xie, Y., Nouri, B., Ntavelis, E., Nou, J., Hendrikx, N., Visser, L., Sengupta, M., Pó, M., Chauvin, R., Grieu, S., Blum, N., van Sark, W., and Kazantzidis, A.: Benchmarking of solar irradiance nowcast performance derived from all-sky imagers, Renew. Energ., 199, 246–261, https://doi.org/10.1016/j.renene.2022.08.127, 2022. a
Long, C. N. and Ackerman, T. P.: Identification of clear skies from broadband pyranometer measurements and calculation of downwelling shortwave cloud effects, J. Geophys. Res.-Atmos., 105, 15609–15626, https://doi.org/10.1029/2000jd900077, 2000. a
Long, C. N., Ackerman, T. P., Gaustad, K. L., and Cole, J. N. S.: Estimation of fractional sky cover from broadband shortwave radiometer measurements, J. Geophys. Res., 111, D11204, https://doi.org/10.1029/2005jd006475, 2006. a, b
Lucas-Picher, P., Brisson, E., Caillaud, C., Alias, A., Nabat, P., Lemonsu, A., Poncet, N., Hernandez, V. E. C., Michau, Y., Doury, A., Monteiro, D., and Somot, S.: Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over northwestern Europe, Research Square [preprint], https://doi.org/10.21203/rs.3.rs-1393181/v1, 2022. a, b
Magnaldo, M.-A., Libois, Q., Riette, S., and Lac, C.: AROME forecasts of surface shortwave downward radiation for year 2020, Zenodo [data set], https://doi.org/10.5281/zenodo.7928622, 2023. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
Météo-France: Météo France Données Publiques, https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=298&id_rubrique=32, last access: 11 January 2024. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, 1997. a
Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and Untch, A.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling, J. Geophys. Res., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009. a
Nouri, B., Kuhn, P., Wilbert, S., Hanrieder, N., Prahl, C., Zarzalejo, L., Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Cloud height and tracking accuracy of three all sky imager systems for individual clouds, Sol. Energ., 177, 213–228, https://doi.org/10.1016/j.solener.2018.10.079, 2019a. a
Nouri, B., Wilbert, S., Segura, L., Kuhn, P., Hanrieder, N., Kazantzidis, A., Schmidt, T., Zarzalejo, L., Blanc, P., and Pitz-Paal, R.: Determination of cloud transmittance for all sky imager based solar nowcasting, Sol. Energ., 181, 251–263, https://doi.org/10.1016/j.solener.2019.02.004, 2019b. a
NWC SAF Documentation, http://www.nwcsaf.org, last access: 11 January 2024. a
Perez, R., Lorenz, E., Pelland, S., Beauharnois, M., Knowe, G. V., Hemker, K., Heinemann, D., Remund, J., Müller, S. C., Traunmüller, W., Steinmauer, G., Pozo, D., Ruiz-Arias, J. A., Lara-Fanego, V., Ramirez-Santigosa, L., Gaston-Romero, M., and Pomares, L. M.: Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe, Sol. Energ., 94, 305–326, https://doi.org/10.1016/j.solener.2013.05.005, 2013. a
Pergaud, J., Masson, V., Malardel, S., and Couvreux, F.: A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction, Bound.-Lay. Meteorol., 132, 83–106, https://doi.org/10.1007/s10546-009-9388-0, 2009. a
Pfister, G., McKenzie, R., Liley, J., Thomas, A., Forgan, B., and Long, C. N.: Cloud coverage based on all-sky imaging and its impact on surface solar irradiance, J. Appl. Meteorol. Clim., 42, 1421–1434, 2003. a
Raza: On recent advances in PV output power forecast, Sol. Energ., 136, 125–144, https://doi.org/10.1016/j.solener.2016.06.073, 2016. a, b
Rieger, D., Steiner, A., Bachmann, V., Gasch, P., Förstner, J., Deetz, K., Vogel, B., and Vogel, H.: Impact of the 4 April 2014 Saharan dust outbreak on the photovoltaic power generation in Germany, Atmos. Chem. Phys., 17, 13391–13415, https://doi.org/10.5194/acp-17-13391-2017, 2017. a, b, c
Riette, S. and Lac, C.: A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model, Bound.-Lay. Meteorol., 160, 269–297, https://doi.org/10.1007/s10546-016-0146-9, 2016. a
Réseau de transport d'électricité: Panorama de l'électricité renouvelable, Tech. rep., Agence ORE and Enedis and Réseau de transport d'électricité and Syndicat des énergies renouvelables, 2021. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F., Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model, Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a, b
Stein, J. and Stoop, F.: Neighborhood-Based Contingency Tables Including Errors Compensation, Mon. Weather Rev., 147, 329–344, https://doi.org/10.1175/MWR-D-17-0288.1, 2019. a
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L’ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten years of actively observing the Earth system, B. Am. Meteorol. Soc., 99, 569–581, 2018. a
Sun, W., Videen, G., Kato, S., Lin, B., Lukashin, C., and Hu, Y.: A study of subvisual clouds and their radiation effect with a synergy of CERES, MODIS, CALIPSO, and AIRS data, J. Geophys. Res.-Atmos., 116, D22207, https://doi.org/10.1029/2011JD016422, 2011. a
Taufour, M., Vié, B., Augros, C., Boudevillain, B., Delanoë, J., Delautier, G., Ducrocq, V., Lac, C., Pinty, J.-P., and Schwarzenböck, A.: Evaluation of the two-moment scheme LIMA based on microphysical observations from the HyMeX campaign, Q. J. Roy. Meteorol. Soc., 144, 1398–1414, 2018. a
Tegen, I., Hollrig, P., Chin, M., Fung, I., Jacob, D., and Penner, J.: Contribution of different aerosol species to the global aerosol extinction optical thickness: Estimates from model results, J. Geophys. Res.-Atmos., 102, 23895–23915, https://doi.org/10.1029/97JD01864, 1997. a, b
Tuononen, M., O'Connor, E. J., and Sinclair, V. A.: Evaluating solar radiation forecast uncertainty, Atmos. Chem. Phys., 19, 1985–2000, https://doi.org/10.5194/acp-19-1985-2019, 2019. a, b, c, d
Wagner, T. J. and Kleiss, J. M.: Error characteristics of ceilometer-based observations of cloud amount, J. Atmos. Ocean. Tech., 33, 1557–1567, 2016. a
Weverberg, K. V., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H.-Y., Zhang, C., Xie, S., Tang, Q., Gustafson, W. I., Qian, Y., Berg, L. K., Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole, J., Merryfield, W., Lee, W.-S., Cheruy, F., Mellul, L., Wang, Y.-C., Johnson, K., and Thieman, M. M.: CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains, J. Geophys. Res.-Atmos., 123, 3612–3644, https://doi.org/10.1002/2017JD027188, 2018. a, b, c
Wild, M.: Global dimming and brightening: A review, J. Geophys. Res.-Atmos., 114, D00D16, https://doi.org/10.1029/2008JD011470, 2009. a
Wissmeier, U., Buras, R., and Mayer, B.: paNTICA: A Fast 3D Radiative Transfer Scheme to Calculate Surface Solar Irradiance for NWP and LES Models, J. Appl. Meteorol. Clim., 52, 1698–1715, https://doi.org/10.1175/jamc-d-12-0227.1, 2013. a
Wurtz, J., Bouniol, D., Vié, B., and Lac, C.: Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign, Q. J. Roy. Meteor. Soc., 147, 2796–2817, https://doi.org/10.1002/qj.4100, 2021. a
Wurtz, J., Bouniol, D., and Vié, B.: Improvements to the parametrization of snow in AROME in the context of ice crystal icing, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.4437, 2023. a, b
Short summary
With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
With the worldwide development of the solar energy sector, the need for reliable solar radiation...
