Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4941-2022
© 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/gmd-15-4941-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
28 Jun 2022
Model evaluation paper |
| 28 Jun 2022
| 28 Jun 2022
Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation
ARIA Technologies, 8 Rue de la Ferme, 92100 Boulogne-Billancourt, France
Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, IPSL & Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Pascal Yiou
Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, IPSL & Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Céline Déandreis
ARIA Technologies, 8 Rue de la Ferme, 92100 Boulogne-Billancourt, France
Soulivanh Thao
Laboratoire des Sciences du Climat et de l’Environnement, UMR 8212 CEA-CNRS-UVSQ, IPSL & Université Paris-Saclay, 91191 Gif-sur-Yvette, France
Related authors
Meriem Krouma, Riccardo Silini, and Pascal Yiou
Earth Syst. Dynam., 14, 273–290, https://doi.org/10.5194/esd-14-273-2023, https://doi.org/10.5194/esd-14-273-2023, 2023
Short summary
Short summary
We present a simple system to forecast the Madden–Julian Oscillation (MJO). We use atmospheric circulation as input to our system. We found a good-skill forecast of the MJO amplitude within 40 d using this methodology. Comparing our results with ECMWF and machine learning forecasts confirmed the good skill of our system.
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
Short summary
Short summary
We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
Davide Faranda, Lucas Taligrot, Pascal Yiou, and Nada Caud
EGUsphere, https://doi.org/10.5194/egusphere-2025-2222, https://doi.org/10.5194/egusphere-2025-2222, 2025
This preprint is open for discussion and under review for Geoscience Communication (GC).
Short summary
Short summary
We developed a free online game called ClimarisQ to help people better understand climate change and extreme weather. By playing the game, users learn how decisions about the environment, money, and public opinion affect future risks. We studied how players reacted and found that the game makes climate issues easier to grasp and encourages discussion. This shows that interactive tools like games can support learning and action on climate and environmental challenges.
Robin Noyelle, Davide Faranda, Yoann Robin, Mathieu Vrac, and Pascal Yiou
Weather Clim. Dynam., 6, 817–839, https://doi.org/10.5194/wcd-6-817-2025, https://doi.org/10.5194/wcd-6-817-2025, 2025
Short summary
Short summary
Properties of extreme meteorological and climatological events are changing under human-caused climate change. Extreme event attribution methods seek to estimate the contribution of global warming in the probability and intensity changes of extreme events. Here we propose a procedure to estimate these quantities for the flow analogue method, which compares the observed event to similar events in the past.
Laura Hasbini, Pascal Yiou, Laurent Boissier, and Arthur Perringaux
EGUsphere, https://doi.org/10.5194/egusphere-2025-3138, https://doi.org/10.5194/egusphere-2025-3138, 2025
Short summary
Short summary
Winter windstorms are the main natural hazard for Generali France. We present a method linking storm events to insurance claims, with a focus on clustered events (multiple storms hitting the same region within 96 h). These account for 85 % of losses since 1998 and include major events like Lothar and Klaus. Damaging storms are twice as likely to occur in clusters, underlining the need to account for their impact in risk, loss, and reinsurance modelling.
Ségolène Crossouard, Soulivanh Thao, Thomas Dubos, Masa Kageyama, Mathieu Vrac, and Yann Meurdesoif
EGUsphere, https://doi.org/10.5194/egusphere-2025-1418, https://doi.org/10.5194/egusphere-2025-1418, 2025
Short summary
Short summary
Current atmospheric models are limited by the computational time required for physical processes, known as physical parameterizations. To address this, we developed neural network-based emulators to replace these parameterizations in the IPSL climate model, using a simplified aquaplanet setup. We found that incorporating some physical knowledge, such as latent variables, into the learning process can improve predictions.
Ferran Lopez-Marti, Mireia Ginesta, Davide Faranda, Anna Rutgersson, Pascal Yiou, Lichuan Wu, and Gabriele Messori
Earth Syst. Dynam., 16, 169–187, https://doi.org/10.5194/esd-16-169-2025, https://doi.org/10.5194/esd-16-169-2025, 2025
Short summary
Short summary
Explosive cyclones and atmospheric rivers are two main drivers of extreme weather in Europe. In this study, we investigate their joint changes in future climates over the North Atlantic. Our results show that both the concurrence of these events and the intensity of atmospheric rivers increase by the end of the century across different future scenarios. Furthermore, explosive cyclones associated with atmospheric rivers last longer and are deeper than those without atmospheric rivers.
Camille Cadiou and Pascal Yiou
Weather Clim. Dynam., 6, 1–15, https://doi.org/10.5194/wcd-6-1-2025, https://doi.org/10.5194/wcd-6-1-2025, 2025
Short summary
Short summary
Extreme cold winter temperatures in Europe have huge societal impacts. This study focuses on extreme cold events, such as the winter of 1963 in France, which are expected to become rarer due to climate change. We use a light and efficient rare-event algorithm to simulate a large number of extreme cold winters over France to analyse their characteristics. We find that despite fewer occurrences, their intensity remains steady. We analyse prevailing atmospheric circulation during these events.
Camille Cadiou and Pascal Yiou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3473, https://doi.org/10.5194/egusphere-2024-3473, 2024
Short summary
Short summary
Cold extremes significantly affect healthcare and energy systems. Global warming is expected to reduce these extremes. Our study indeed shows that very intense cold spells will become nearly impossible in France by the end of the 21st century for high levels of warming. However, we demonstrate that events as intense as the 1985 cold spell in France may still occur in the near future. These events are linked to specific atmospheric patterns that bring cold air from high latitudes into Europe.
Sebastian Sippel, Clair Barnes, Camille Cadiou, Erich Fischer, Sarah Kew, Marlene Kretschmer, Sjoukje Philip, Theodore G. Shepherd, Jitendra Singh, Robert Vautard, and Pascal Yiou
Weather Clim. Dynam., 5, 943–957, https://doi.org/10.5194/wcd-5-943-2024, https://doi.org/10.5194/wcd-5-943-2024, 2024
Short summary
Short summary
Winter temperatures in central Europe have increased. But cold winters can still cause problems for energy systems, infrastructure, or human health. Here we tested whether a record-cold winter, such as the one observed in 1963 over central Europe, could still occur despite climate change. The answer is yes: it is possible, but it is very unlikely. Our results rely on climate model simulations and statistical rare event analysis. In conclusion, society must be prepared for such cold winters.
Davide Faranda, Gabriele Messori, Erika Coppola, Tommaso Alberti, Mathieu Vrac, Flavio Pons, Pascal Yiou, Marion Saint Lu, Andreia N. S. Hisi, Patrick Brockmann, Stavros Dafis, Gianmarco Mengaldo, and Robert Vautard
Weather Clim. Dynam., 5, 959–983, https://doi.org/10.5194/wcd-5-959-2024, https://doi.org/10.5194/wcd-5-959-2024, 2024
Short summary
Short summary
We introduce ClimaMeter, a tool offering real-time insights into extreme-weather events. Our tool unveils how climate change and natural variability affect these events, affecting communities worldwide. Our research equips policymakers and the public with essential knowledge, fostering informed decisions and enhancing climate resilience. We analysed two distinct events, showcasing ClimaMeter's global relevance.
Mathieu Vrac, Denis Allard, Grégoire Mariéthoz, Soulivanh Thao, and Lucas Schmutz
Earth Syst. Dynam., 15, 735–762, https://doi.org/10.5194/esd-15-735-2024, https://doi.org/10.5194/esd-15-735-2024, 2024
Short summary
Short summary
We aim to combine multiple global climate models (GCMs) to enhance the robustness of future projections. We introduce a novel approach, called "α pooling", aggregating the cumulative distribution functions (CDFs) of the models into a CDF more aligned with historical data. The new CDFs allow us to perform bias adjustment of all the raw climate simulations at once. Experiments with European temperature and precipitation demonstrate the superiority of this approach over conventional techniques.
Meriem Krouma, Riccardo Silini, and Pascal Yiou
Earth Syst. Dynam., 14, 273–290, https://doi.org/10.5194/esd-14-273-2023, https://doi.org/10.5194/esd-14-273-2023, 2023
Short summary
Short summary
We present a simple system to forecast the Madden–Julian Oscillation (MJO). We use atmospheric circulation as input to our system. We found a good-skill forecast of the MJO amplitude within 40 d using this methodology. Comparing our results with ECMWF and machine learning forecasts confirmed the good skill of our system.
Davide Faranda, Stella Bourdin, Mireia Ginesta, Meriem Krouma, Robin Noyelle, Flavio Pons, Pascal Yiou, and Gabriele Messori
Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, https://doi.org/10.5194/wcd-3-1311-2022, 2022
Short summary
Short summary
We analyze the atmospheric circulation leading to impactful extreme events for the calendar year 2021 such as the Storm Filomena, Westphalia floods, Hurricane Ida and Medicane Apollo. For some of the events, we find that climate change has contributed to their occurrence or enhanced their intensity; for other events, we find that they are unprecedented. Our approach underscores the importance of considering changes in the atmospheric circulation when performing attribution studies.
Miriam D'Errico, Flavio Pons, Pascal Yiou, Soulivanh Tao, Cesare Nardini, Frank Lunkeit, and Davide Faranda
Earth Syst. Dynam., 13, 961–992, https://doi.org/10.5194/esd-13-961-2022, https://doi.org/10.5194/esd-13-961-2022, 2022
Short summary
Short summary
Climate change is already affecting weather extremes. In a warming climate, we will expect the cold spells to decrease in frequency and intensity. Our analysis shows that the frequency of circulation patterns leading to snowy cold-spell events over Italy will not decrease under business-as-usual emission scenarios, although the associated events may not lead to cold conditions in the warmer scenarios.
Linh N. Luu, Robert Vautard, Pascal Yiou, and Jean-Michel Soubeyroux
Earth Syst. Dynam., 13, 687–702, https://doi.org/10.5194/esd-13-687-2022, https://doi.org/10.5194/esd-13-687-2022, 2022
Short summary
Short summary
This study downscales climate information from EURO-CORDEX (approx. 12 km) output to a higher horizontal resolution (approx. 3 km) for the south of France. We also propose a matrix of different indices to evaluate the high-resolution precipitation output. We find that a higher resolution reproduces more realistic extreme precipitation events at both daily and sub-daily timescales. Our results and approach are promising to apply to other Mediterranean regions and climate impact studies.
Pascal Yiou and Nicolas Viovy
Earth Syst. Dynam., 12, 997–1013, https://doi.org/10.5194/esd-12-997-2021, https://doi.org/10.5194/esd-12-997-2021, 2021
Short summary
Short summary
This paper presents a model of tree ruin as a response to drought hazards. This model is inspired by a standard model of ruin in the insurance industry. We illustrate how ruin can occur in present-day conditions and the sensitivity of ruin and time to ruin to hazard statistical properties. We also show how tree strategies to cope with hazards can affect their long-term reserves and the probability of ruin.
Peter Pfleiderer, Aglaé Jézéquel, Juliette Legrand, Natacha Legrix, Iason Markantonis, Edoardo Vignotto, and Pascal Yiou
Earth Syst. Dynam., 12, 103–120, https://doi.org/10.5194/esd-12-103-2021, https://doi.org/10.5194/esd-12-103-2021, 2021
Short summary
Short summary
In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely due to an extremely warm December 2015 and abnormally high precipitation during the following spring season. Using stochastic weather generators we investigate how severe the metrological conditions leading to the crop loss could be in current climate conditions. We find that December temperatures were close to the plausible maximum but that considerably wetter springs would be possible.
Mathieu Vrac and Soulivanh Thao
Geosci. Model Dev., 13, 5367–5387, https://doi.org/10.5194/gmd-13-5367-2020, https://doi.org/10.5194/gmd-13-5367-2020, 2020
Short summary
Short summary
We propose a multivariate bias correction (MBC) method to adjust the spatial and/or inter-variable properties of climate simulations, while also accounting for their temporal dependences (e.g., autocorrelations).
It consists on a method reordering the ranks of the time series according to their multivariate distance to a reference time series.
Results show that temporal correlations are improved while spatial and inter-variable correlations are still satisfactorily corrected.
Cited articles
Ardilouze, C., Specq, D., Batté, L., and Cassou, C.: Flow dependence of wintertime subseasonal prediction skill over Europe, Weather Clim. Dynam., 2, 1033–1049, https://doi.org/10.5194/wcd-2-1033-2021, 2021. a, b
Atencia, A. and Zawadzki, I.: A comparison of two techniques for generating
nowcasting ensembles. Part I: Lagrangian ensemble technique, Mon.
Weather Rev., 142, 4036–4052, 2014. a
Blanchet, J., Stalla, S., and Creutin, J.-D.: Analogy of multiday sequences of
atmospheric circulation favoring large rainfall accumulation over the
French Alps, Atmos. Sci. Lett., 19, e809,
https://doi.org/10.1002/asl.809, 2018. a
Cassou, C., Minvielle, M., Terray, L., and Périgaud, C.: A
statistical–dynamical scheme for reconstructing ocean forcing in the
Atlantic. Part I: weather regimes as predictors for ocean surface
variables, Clim. Dynam., 36, 19–39, https://doi.org/10.1007/s00382-010-0781-7,
2011. a
Eckmann, J.-P. and Ruelle, D.: Ergodic theory of chaos and strange attractors,
in: The Theory of Chaotic Attractors, pp. 273–312, Springer Nature, ISBN 978-1-4419-2330-1, https://doi.org/10.1007/978-0-387-21830-4_17, 1985. a
Faranda, D., Messori, G., and Yiou, P.: Dynamical proxies of North Atlantic predictability and extremes, Sci. Rep., 7, 41278, https://doi.org/10.1038/srep41278, 2017. a
Ferro, C. A. T.: A probability model for verifying deterministic forecasts of
extreme events, Weather Forecast., 22, 1089–1100, https://doi.org/10.1175/WAF1036.1, 2007. a, b
Gabriel, K. R. and Neumann, J.: A Markov chain model for daily rainfall
occurrence at Tel Aviv, Q. J. Roy. Meteor.
Soc., 88, 90–95, 1962. a
Ghil, M., Chekroun, M., and Simonnet, E.: Climate dynamics and fluid mechanics:
Natural variability and related uncertainties, Physica D, 237, 2111–2126,
https://doi.org/10.1016/j.physd.2008.03.036, 2008. a
Haiden, T., Janousek, M., Bidlot, J., Buizza, R., Ferranti, L., Prates, F., and
Vitart, F.: Evaluation of ECMWF forecasts, including the 2018 upgrade,
European Centre for Medium Range Weather Forecasts, https://doi.org/10.21957/ldw15ckqi, 2018. a, b, c, d
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006, J. Geophys. Res., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008. a, b
Hempelmann, N., Ehbrecht, C., Alvarez-Castro, C., Brockmann, P., Falk, W.,
Hoffmann, J., Kindermann, S., Koziol, B., Nangini, C., Radanovics, S.,
Vautard, R., and Yiou, P.: Web processing service for climate impact and
extreme weather event analyses. Flyingpigeon (Version 1.0), Comput.
Geosci., 110, 65–72, https://doi.org/10.1016/j.cageo.2017.10.004, 2018. a
Hersbach, H.: Decomposition of the Continuous Ranked Probability Score
for Ensemble Prediction Systems, Weather Forecast., 15, 559–570, https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a, b
Jézéquel, A., Yiou, P., Radanovics, S., and Vautard, R.: Analysis of the
exceptionally warm December 2015 in France using flow analogues, B. Am. Meteorol. Soc., 99, S76–S79, 2018. a
Kimoto, M.: Studies of Climate Variability Using General Circulation
Models, in: Earth Planets and Space, TERRAPUB, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.534.3517&rank=1 (last access: 18 March 2021), 2001. a
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R., and Fiorino, M.: The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation, B. Am. Meteorol. Soc., 82, 247–268, http://www.jstor.org/stable/26215517 (last access: 27 February 2022), 2001. a
Klein Tank, A. M. G., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée,
G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C.,
Heino, R., Bessemoulin, P., Müller-Westermeier, G., Tzanakou, M., Szalai,
S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M.,
Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F. V., Forland, E.,
Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T.,
Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A.,
Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, Int. J. Climatol., 22,
1441–1453, https://doi.org/10.1002/joc.773, 2002. a, b, c
Krouma, M.: Assessment of stochastic weather forecast based on analogs of circulation, Zenodo [code and data set], https://doi.org/10.5281/zenodo.4524562, 2021. a
Lorenz, E. N.: Atmospheric Predictability as Revealed by Naturally
Occurring Analogues, J. Atmos. Sci., 26, 636–646, 1969. a
Mastrantonas, N., Herrera‐Lormendez, P., Magnusson, L., Pappenberger, F., and
Matschullat, J.: Extreme precipitation events in the Mediterranean:
Spatiotemporal characteristics and connection to large‐scale atmospheric
flow patterns, Int. J. Climatol., 41, 2710–2728,
https://doi.org/10.1002/joc.6985, 2021. a
Palmer, T. N.: Predicting uncertainty in forecasts of weather and climate,
Rep. Prog. Phys., 63, 71–116, https://doi.org/10.1088/0034-4885/63/2/201, 2000. a
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a, b, c
Platzer, P., Yiou, P., Naveau, P., Filipot, J.-F., Thiébaut, M., and Tandeo,
P.: Probability Distributions for Analog-To-Target Distances, J.
Atmos. Sci., 78, 3317–3335, https://doi.org/10.1175/JAS-D-20-0382.1, 2021. a
Richardson, C. W.: Stochastic simulation of daily precipitation, temperature,
and solar radiation, Water Resour. Res., 17, 182–190,
https://doi.org/10.1029/WR017i001p00182, 1981. a
Ruelle, D.: Ergodic theory of differentiable dynamical systems, Publications Mathématiques de l'IHÉS, Tome, 50, 27–58, http://www.numdam.org/item/PMIHES_1979__50__27_0/ (last access: 19 June 2022), 1979. a
Scaife, A. A., Arribas, A., Blockley, E., Brookshaw, A., Clark, R. T.,
Dunstone, N., Eade, R., Fereday, D., Folland, C. K., and Gordon, M.: Skillful
long‐range prediction of European and North American winters,
Geophys. Res. Lett., 41, 2514–2519, 2014. a
Sivillo, J. K., Ahlquist, J. E., and Toth, Z.: An ensemble forecasting primer,
Weather Forecast., 12, 809–818, https://doi.org/10.1175/1520-0434(1997)012<0809:AEFP>2.0.CO;2, 1997. a
Todorovic, P. and Woolhiser, D. A.: A stochastic model of n-day precipitation,
J. Appl. Meteorol., 14, 17–24, 1975. a
Toth, Z.: Intercomparison of Circulation Similarity Measures, Mon. Weather
Rev., 119, 55–64, https://doi.org/10.1175/1520-0493(1991)119<0055:IOCSM>2.0.CO;2, 1991. a
Toth, Z. and Kalnay, E.: Ensemble forecasting at NCEP and the breeding
method, Mon. Weather Rev., 125, 3297–3319, https://doi.org/10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO;2, 1997. a
Türkes, M., Sümer, U., and Kiliç, G.: Persistence and periodicity in the
precipitation series of Turkey and associations with 500 hPa geopotential
heights, Clim. Res., 21, 59–81, https://doi.org/10.3354/cr021059, 2002. a
van den Dool, H. M.: Empirical Methods in Short-Term Climate
Prediction, Oxford University Press, Oxford, ISBN-13 9780199202782,
https://doi.org/10.1093/oso/9780199202782.001.0001, 2007. a
van der Wiel, K., Bloomfield, H. C., Lee, R. W., Stoop, L. P., Blackport, R.,
Screen, J. A., and Selten, F. M.: The influence of weather regimes on
European renewable energy production and demand, Environ. Res.
Lett., 14, 094010, https://doi.org/10.1088/1748-9326/ab38d3, 2019.
a
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C.,
Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J.,
Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I.,
Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A.,
Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W.,
Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D.,
Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R., and Zhang, L.: The
Subseasonal to Seasonal (S2S) Prediction Project Database,
B. Am. Meteorol. Soc., 98, 163–173,
https://doi.org/10.1175/BAMS-D-16-0017.1, 2017. a, b
von Storch, H. and Zwiers, F. W.: Statistical Analysis in Climate
Research, Cambridge University Press, Cambridge, ISBN 9780511612336,
https://doi.org/10.1017/CBO9780511612336, 2001. a
Wilks, D.: Statistical Methods in the Atmospheric Sciences: An Introduction, Elsevier’s Science & Technology Rights Department in Oxford, UK, ISBN 978-0-12-751966-1, 1995. a
Xoplaki, E., Luterbacher, J., Burkard, R., Patrikas, I., and Maheras, P.:
Connection between the large-scale 500 hPa geopotential height fields and
precipitation over Greece during wintertime, Clim. Res., 14,
129–146, https://doi.org/10.3354/cr014129, 2000. a
Yiou, P., Goubanova, K., Li, Z. X., and Nogaj, M.: Weather regime dependence of extreme value statistics for summer temperature and precipitation, Nonlin. Processes Geophys., 15, 365–378, https://doi.org/10.5194/npg-15-365-2008, 2008. a
Yiou, P., Salameh, T., Drobinski, P., Menut, L., Vautard, R., and Vrac, M.:
Ensemble reconstruction of the atmospheric column from surface pressure using
analogues, Clim. Dynam., 41, 1333–1344, https://doi.org/10.1007/s00382-012-1626-3,
2013. a, b
Short summary
We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at...
