Articles | Volume 24, issue 12
https://doi.org/10.5194/nhess-24-4225-2024
© 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/nhess-24-4225-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Nov 2024
Research article |
| 29 Nov 2024
| 29 Nov 2024
Statistical calibration of probabilistic medium-range Fire Weather Index forecasts in Europe
Stephanie Bohlmann
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
Related authors
Athena Augusta Floutsi, Holger Baars, Ronny Engelmann, Dietrich Althausen, Albert Ansmann, Stephanie Bohlmann, Birgit Heese, Julian Hofer, Thomas Kanitz, Moritz Haarig, Kevin Ohneiser, Martin Radenz, Patric Seifert, Annett Skupin, Zhenping Yin, Sabur F. Abdullaev, Mika Komppula, Maria Filioglou, Elina Giannakaki, Iwona S. Stachlewska, Lucja Janicka, Daniele Bortoli, Eleni Marinou, Vassilis Amiridis, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Boris Barja, and Ulla Wandinger
Atmos. Meas. Tech., 16, 2353–2379, https://doi.org/10.5194/amt-16-2353-2023, https://doi.org/10.5194/amt-16-2353-2023, 2023
Short summary
Short summary
DeLiAn is a collection of lidar-derived aerosol intensive optical properties for several aerosol types, namely the particle linear depolarization ratio, the extinction-to-backscatter ratio (lidar ratio) and the Ångström exponent. The data collection is based on globally distributed, long-term, ground-based, multiwavelength, Raman and polarization lidar measurements and currently covers two wavelengths, 355 and 532 nm, for 13 aerosol categories ranging from basic aerosol types to mixtures.
Stephanie Bohlmann, Xiaoxia Shang, Ville Vakkari, Elina Giannakaki, Ari Leskinen, Kari E. J. Lehtinen, Sanna Pätsi, and Mika Komppula
Atmos. Chem. Phys., 21, 7083–7097, https://doi.org/10.5194/acp-21-7083-2021, https://doi.org/10.5194/acp-21-7083-2021, 2021
Short summary
Short summary
Measurements of the multi-wavelength Raman polarization lidar PollyXT and a Halo Photonics StreamLine Doppler lidar have been combined with measurements of pollen type and concentration using a traditional pollen trap at the rural forest site in Vehmasmäki, Finland. Depolarization ratios were measured at three wavelengths. High depolarization ratios were detected during an event with high birch and spruce pollen concentrations and a wavelength dependence of the depolarization ratio was observed.
Ville Vakkari, Holger Baars, Stephanie Bohlmann, Johannes Bühl, Mika Komppula, Rodanthi-Elisavet Mamouri, and Ewan James O'Connor
Atmos. Chem. Phys., 21, 5807–5820, https://doi.org/10.5194/acp-21-5807-2021, https://doi.org/10.5194/acp-21-5807-2021, 2021
Short summary
Short summary
The depolarization ratio is a valuable parameter for aerosol categorization from remote sensing measurements. Here, we introduce particle depolarization ratio measurements at the 1565 nm wavelength, which is substantially longer than previously utilized wavelengths and enhances our capabilities to study the wavelength dependency of the particle depolarization ratio.
Xiaoxia Shang, Elina Giannakaki, Stephanie Bohlmann, Maria Filioglou, Annika Saarto, Antti Ruuskanen, Ari Leskinen, Sami Romakkaniemi, and Mika Komppula
Atmos. Chem. Phys., 20, 15323–15339, https://doi.org/10.5194/acp-20-15323-2020, https://doi.org/10.5194/acp-20-15323-2020, 2020
Short summary
Short summary
Measurements of the multi-wavelength Raman polarization lidar PollyXT have been combined with measurements of pollen type and concentration using a traditional pollen sampler at a rural forest site in Kuopio, Finland. The depolarization ratio was enhanced when there were pollen grains in the atmosphere, illustrating the potential of lidar to track pollen grains in the atmosphere. The depolarization ratio of pure pollen particles was assessed for birch and pine pollen using a novel algorithm.
Oskari Rockas, Pia Isolähteenmäki, Marko Laine, Anders V. Lindfors, Karoliina Hämäläinen, and Anton Laakso
EGUsphere, https://doi.org/10.5194/egusphere-2025-1745, https://doi.org/10.5194/egusphere-2025-1745, 2025
Short summary
Short summary
When ice buildup occurs on an energy infrastructure such as a power transmission line or a wind turbine, this can cause disturbances in energy production and transmission efficiency. In our study, we assessed how atmospheric icing conditions will change in the future climate in northern Europe. Towards the end of the century, the climate projections suggest a mostly decreasing trend in ice accumulation. However, the northern parts of Fennoscandia can locally experience increasing amounts of ice.
Olle Räty, Marko Laine, Ulpu Leijala, Jani Särkkä, and Milla M. Johansson
Nat. Hazards Earth Syst. Sci., 23, 2403–2418, https://doi.org/10.5194/nhess-23-2403-2023, https://doi.org/10.5194/nhess-23-2403-2023, 2023
Short summary
Short summary
We studied annual maximum sea levels in the Finnish coastal region. Our aim was to better quantify the uncertainty in them compared to previous studies. Using four statistical models, we found out that hierarchical models, which shared information on sea-level extremes across Finnish tide gauges, had lower uncertainty in their results in comparison with tide-gauge-specific fits. These models also suggested that the shape of the distribution for extreme sea levels is similar on the Finnish coast.
Athena Augusta Floutsi, Holger Baars, Ronny Engelmann, Dietrich Althausen, Albert Ansmann, Stephanie Bohlmann, Birgit Heese, Julian Hofer, Thomas Kanitz, Moritz Haarig, Kevin Ohneiser, Martin Radenz, Patric Seifert, Annett Skupin, Zhenping Yin, Sabur F. Abdullaev, Mika Komppula, Maria Filioglou, Elina Giannakaki, Iwona S. Stachlewska, Lucja Janicka, Daniele Bortoli, Eleni Marinou, Vassilis Amiridis, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Boris Barja, and Ulla Wandinger
Atmos. Meas. Tech., 16, 2353–2379, https://doi.org/10.5194/amt-16-2353-2023, https://doi.org/10.5194/amt-16-2353-2023, 2023
Short summary
Short summary
DeLiAn is a collection of lidar-derived aerosol intensive optical properties for several aerosol types, namely the particle linear depolarization ratio, the extinction-to-backscatter ratio (lidar ratio) and the Ångström exponent. The data collection is based on globally distributed, long-term, ground-based, multiwavelength, Raman and polarization lidar measurements and currently covers two wavelengths, 355 and 532 nm, for 13 aerosol categories ranging from basic aerosol types to mixtures.
Anu Kauppi, Antti Kukkurainen, Antti Lipponen, Marko Laine, Antti Arola, Hannakaisa Lindqvist, and Johanna Tamminen
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-328, https://doi.org/10.5194/amt-2021-328, 2021
Revised manuscript not accepted
Short summary
Short summary
We present a methodology in Bayesian framework for retrieving atmospheric aerosol optical depth and aerosol type from the pre-computed look-up tables (LUTs). Especially, we consider Bayesian model averaging and uncertainty originating from aerosol model selection and imperfect forward modelling. Our aim is to get more realistic uncertainty estimates. We have applied the methodology to TROPOMI/S5P satellite observations and evaluated the results against ground-based data from the AERONET.
Stephanie Bohlmann, Xiaoxia Shang, Ville Vakkari, Elina Giannakaki, Ari Leskinen, Kari E. J. Lehtinen, Sanna Pätsi, and Mika Komppula
Atmos. Chem. Phys., 21, 7083–7097, https://doi.org/10.5194/acp-21-7083-2021, https://doi.org/10.5194/acp-21-7083-2021, 2021
Short summary
Short summary
Measurements of the multi-wavelength Raman polarization lidar PollyXT and a Halo Photonics StreamLine Doppler lidar have been combined with measurements of pollen type and concentration using a traditional pollen trap at the rural forest site in Vehmasmäki, Finland. Depolarization ratios were measured at three wavelengths. High depolarization ratios were detected during an event with high birch and spruce pollen concentrations and a wavelength dependence of the depolarization ratio was observed.
Ville Vakkari, Holger Baars, Stephanie Bohlmann, Johannes Bühl, Mika Komppula, Rodanthi-Elisavet Mamouri, and Ewan James O'Connor
Atmos. Chem. Phys., 21, 5807–5820, https://doi.org/10.5194/acp-21-5807-2021, https://doi.org/10.5194/acp-21-5807-2021, 2021
Short summary
Short summary
The depolarization ratio is a valuable parameter for aerosol categorization from remote sensing measurements. Here, we introduce particle depolarization ratio measurements at the 1565 nm wavelength, which is substantially longer than previously utilized wavelengths and enhances our capabilities to study the wavelength dependency of the particle depolarization ratio.
Anna Shcherbacheva, Tracey Balehowsky, Jakub Kubečka, Tinja Olenius, Tapio Helin, Heikki Haario, Marko Laine, Theo Kurtén, and Hanna Vehkamäki
Atmos. Chem. Phys., 20, 15867–15906, https://doi.org/10.5194/acp-20-15867-2020, https://doi.org/10.5194/acp-20-15867-2020, 2020
Short summary
Short summary
Atmospheric new particle formation and cluster growth to aerosol particles is an important field of research, in particular due to the climate change phenomenon. Evaporation rates are very difficult to account for but they are important to explain the formation and growth of particles. Different quantum chemistry (QC) methods produce substantially different values for the evaporation rates. We propose a novel approach for inferring evaporation rates of clusters from available measurements.
Xiaoxia Shang, Elina Giannakaki, Stephanie Bohlmann, Maria Filioglou, Annika Saarto, Antti Ruuskanen, Ari Leskinen, Sami Romakkaniemi, and Mika Komppula
Atmos. Chem. Phys., 20, 15323–15339, https://doi.org/10.5194/acp-20-15323-2020, https://doi.org/10.5194/acp-20-15323-2020, 2020
Short summary
Short summary
Measurements of the multi-wavelength Raman polarization lidar PollyXT have been combined with measurements of pollen type and concentration using a traditional pollen sampler at a rural forest site in Kuopio, Finland. The depolarization ratio was enhanced when there were pollen grains in the atmosphere, illustrating the potential of lidar to track pollen grains in the atmosphere. The depolarization ratio of pure pollen particles was assessed for birch and pine pollen using a novel algorithm.
Cited articles
Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M., and García-Herrera, R.: The Hot Summer of 2010: Redrawing the Temperature Record Map of Europe, Science, 332, 220–224, https://doi.org/10.1126/science.1201224, 2011. a
Bougeault, P., Toth, Z., Bishop, C., Brown, B., Burridge, D., Chen, D. H., Ebert, B., Fuentes, M., Hamill, T. M., Mylne, K., Nicolau, J., Paccagnella, T., Park, Y.-Y., Parsons, D., Raoult, B., Schuster, D., Dias, P. S., Swinbank, R., Takeuchi, Y., Tennant, W., Wilson, L., and Worley, S.: The THORPEX Interactive Grand Global Ensemble, B. Am. Meteorol. Soc., 91, 1059–1072, https://doi.org/10.1175/2010BAMS2853.1, 2010. a
Bremnes, J. B.: Probabilistic Forecasts of Precipitation in Terms of Quantiles Using NWP Model Output, Mon. Weather Rev., 132, 338–347, https://doi.org/10.1175/1520-0493(2004)132<0338:PFOPIT>2.0.CO;2, 2004. a
Cannon, A. J.: Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables, Clim. Dynam., 50, 31–49, https://doi.org/10.1007/s00382-017-3580-6, 2018. a
de Groot, W. J., Field, R. D., Brady, M. A., Roswintiarti, O., and Mohamad, M.: Development of the Indonesian and Malaysian Fire Danger Rating Systems, Mitig. Adapt. Strat. Gl., 12, 165–180, https://doi.org/10.1007/s11027-006-9043-8, 2007. a
De Rigo, D., Libertà, G., Houston Durrant, T., Vivancos, T. A., and San-Miguel-Ayanz, J.: Forest fire danger extremes in Europe under climate change: variability and uncertainty, Research report, Publications Office of the European Union, https://doi.org/10.2760/13180, 2017. a
Di Giuseppe, F., Pappenberger, F., Wetterhall, F., Krzeminski, B., Camia, A., Libertá, G., and San-Miguel, J.: The Potential Predictability of Fire Danger Provided by Numerical Weather Prediction, J. Appl. Meteorol. Clim., 55, 2469–2491, https://doi.org/10.1175/JAMC-D-15-0297.1, 2016. a, b
Di Giuseppe, F., Vitolo, C., Krzeminski, B., Barnard, C., Maciel, P., and San-Miguel, J.: Fire Weather Index: the skill provided by the European Centre for Medium-Range Weather Forecasts ensemble prediction system, Nat. Hazards Earth Syst. Sci., 20, 2365–2378, https://doi.org/10.5194/nhess-20-2365-2020, 2020. a
ECMWF: TIGGE Data Retrieval, ECMWF [data set], https://apps.ecmwf.int/datasets/data/tigge/, last access: 18 November 2024a.
ECMWF: Meteorological Archival and Retrieval System (MARS), https://apps.ecmwf.int/mars-catalogue/, last access: 9 November 2024b.
EFFIS: The European Forest Fire Information System- User Guide to EFFIS applications, available at: https://effis-gwis-cms.s3-eu-west-1.amazonaws.com/apps/effis.viewer/userguide.pdf (last access: 11 September 2024), 2020. a
EFFIS: EFFIS Annual Statistics for Greece, https://effis.jrc.ec.europa.eu/apps/effis.statistics/estimates/GRC, last access: 27 February 2024. a
Faiola, A. and Labropoulou, E.: How wildfires are threatening the Mediterranean way of life, The Washington Post, https://www.washingtonpost.com/world/2023/09/02/greece-fires-2023-rhodes/ (last access: 13 November 2024), 2023. a
Fortin, V., Abaza, M., Anctil, F., and Turcotte, R.: Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?, J. Hydrometeorol., 15, 1708 – 1713, https://doi.org/10.1175/JHM-D-14-0008.1, 2014. a
Gneiting, T., Raftery, A. E., Westveld, A. H., and Goldman, T.: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Mon. Weather Rev., 133, 1098–1118, https://doi.org/10.1175/MWR2904.1, 2005. a, b
Hagedorn, R., Hamill, T. M., and Whitaker, J. S.: Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part I: Two-Meter Temperatures, Mon. Weather Rev., 136, 2608–2619, https://doi.org/10.1175/2007MWR2410.1, 2008. a
Hamill, T. M. and Colucci, S. J.: Verification of Eta–RSM Short-Range Ensemble Forecasts, Mon. Weather Rev., 125, 1312–1327, https://doi.org/10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2, 1997. a
Hamill, T. M., Whitaker, J. S., and Wei, X.: Ensemble Reforecasting: Improving Medium-Range Forecast Skill Using Retrospective Forecasts, Mon. Weather Rev., 132, 1434–1447, https://doi.org/10.1175/1520-0493(2004)132<1434:ERIMFS>2.0.CO;2, 2004. a
Hamill, T. M., Hagedorn, R., and Whitaker, J. S.: Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation, Mon. Weather Rev., 136, 2620–2632, https://doi.org/10.1175/2007MWR2411.1, 2008. 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
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., 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.: Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service (C3S) Data Store (CDS) [data set], https://doi.org/10.24381/cds.143582cf, 2017. a, b
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
Ibebuchi, C. C. and Abu, I.-O.: Characterization of temperature regimes in Western Europe, as regards the summer 2022 Western European heat wave, Clim. Dynam., 61, 3707–3720, https://doi.org/10.1007/s00382-023-06760-4, 2023. a
Ionita, M., Tallaksen, L. M., Kingston, D. G., Stagge, J. H., Laaha, G., Van Lanen, H. A. J., Scholz, P., Chelcea, S. M., and Haslinger, K.: The European 2015 drought from a climatological perspective, Hydrol. Earth Syst. Sci., 21, 1397–1419, https://doi.org/10.5194/hess-21-1397-2017, 2017. a
Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., Cofiño, A. S., Di Luca, A., Faria, S. H., Gorodetskaya, I. V., Hauser, M., Herrera, S., Hennessy, K., Hewitt, H. T., Jones, R. G., Krakovska, S., Manzanas, R., Martínez-Castro, D., Narisma, G. T., Nurhati, I. S., Pinto, I., Seneviratne, S. I., van den Hurk, B., and Vera, C. S.: An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets, Earth Syst. Sci. Data, 12, 2959–2970, https://doi.org/10.5194/essd-12-2959-2020, 2020. a, b
Messner, J. W., Mayr, G. J., and Zeileis, A.: Heteroscedastic Censored and Truncated Regression with crch, The R Journal, 8, 173–181, https://doi.org/10.32614/RJ-2016-012, 2016. a
Nocedal, J. and Wright, S. J.: Quasi-Newton Methods, 135–163, Springer New York, New York, NY, https://doi.org/10.1007/978-0-387-40065-5_6, 2006. a
Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M.: Using Bayesian Model Averaging to Calibrate Forecast Ensembles, Mon. Weather Rev., 133, 1155–1174, https://doi.org/10.1175/MWR2906.1, 2005. a
Rodrigues, M., Cunill Camprubí, A., Balaguer-Romano, R., Coco Megía, C. J., Castañares, F., Ruffault, J., Fernandes, P. M., and Resco de Dios, V.: Drivers and implications of the extreme 2022 wildfire season in Southwest Europe, Sci. Total Environ., 859, 160320, https://doi.org/10.1016/j.scitotenv.2022.160320, 2023. a
Roulston, M. S. and Smith, L. A.: Combining dynamical and statistical ensembles, Tellus A, 55, 16–30, https://doi.org/10.3402/tellusa.v55i1.12082, 2003. a
Rousi, E., Fink, A. H., Andersen, L. S., Becker, F. N., Beobide-Arsuaga, G., Breil, M., Cozzi, G., Heinke, J., Jach, L., Niermann, D., Petrovic, D., Richling, A., Riebold, J., Steidl, S., Suarez-Gutierrez, L., Tradowsky, J. S., Coumou, D., Düsterhus, A., Ellsäßer, F., Fragkoulidis, G., Gliksman, D., Handorf, D., Haustein, K., Kornhuber, K., Kunstmann, H., Pinto, J. G., Warrach-Sagi, K., and Xoplaki, E.: The extremely hot and dry 2018 summer in central and northern Europe from a multi-faceted weather and climate perspective, Nat. Hazards Earth Syst. Sci., 23, 1699–1718, https://doi.org/10.5194/nhess-23-1699-2023, 2023. a
San-Miguel-Ayanz, J., Schulte, E., Schmuck, G., Camia, A., Strobl, P., Libertà, G., Giovando, C., Boca, R., Sedano, F., Kempeneers, P., McInerney, D., Withmore, C., de Oliveira, S. S., Rodrigues, M., Durrant, T., Corti, P., Oehler, F., Vilar, L., and Amatulli, G.: Comprehensive Monitoring of Wildfires in Europe: The European Forest Fire Information System (EFFIS), in: Approaches to Managing Disaster, edited by: Tiefenbacher, J., chap. 5, IntechOpen, Rijeka, https://doi.org/10.5772/28441, 2012. a
San-Miguel-Ayanz, J., Durrant, T., Boca, R., Libertà, G., Branco, A., de Rigo, D., Ferrari, D., Maianti, P., Vivancos, T. A., Pfeiffer, H., Loffler, P., Nuijten, D., Leray, T., and Jacome Felix Oom, D.: Forest Fires in Europe, Middle East and North Africa 2018, Tech. Rep. EUR 29856 EN, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/1128, JRC117883, 2019. a
San-Miguel-Ayanz, J., Durrant, T., Boca, R., Maianti, P., Libertà, G., Artes Vivancos, T., Jacome Felix Oom, D., Branco, A., De Rigo, D., Ferrari, D., Pfeiffer, H., Grecchi, R., Nuijten, D., Onida, M., and Löffler, P.: Forest Fires in Europe, Middle East and North Africa 2020, Tech. Rep. EUR 30862 EN, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/216446, JRC126766, 2021. a
Sibley, A. M.: Wildfire outbreaks across the United Kingdom during summer 2018, Weather, 74, 397–402, https://doi.org/10.1002/wea.3614, 2019. a
Skacel, J., Kahn, M., and Hovet, J.: Czech and German firefighters battle blaze in national park, Reuters, https://www.reuters.com/business/environment/czech-german-firefighters-battle-blaze-national-park-2022-07-26/ (last access: 13 November 2024), 2022. a
Thorarinsdottir, T. L. and Gneiting, T.: Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression, J. R. Stat. Soc. A Stat., 173, 371–388, https://doi.org/10.1111/j.1467-985X.2009.00616.x, 2010. a
Thorarinsdottir, T. L. and Johnson, M. S.: Probabilistic Wind Gust Forecasting Using Nonhomogeneous Gaussian Regression, Mon. Weather Rev., 140, 889–897, https://doi.org/10.1175/MWR-D-11-00075.1, 2012. a
Turco, M., Jerez, S., Augusto, S., Tarín-Carrasco, P., Ratola, N., Jiménez-Guerrero, P., and Trigo, R. M.: Climate drivers of the 2017 devastating fires in Portugal, Sci. Rep., 9, 13886, https://doi.org/10.1038/s41598-019-50281-2, 2019. a
Weigel, A. P.: Ensemble Forecasts, in: Forecast Verification, chap. 8, 141–166, John Wiley & Sons, Ltd, https://doi.org/10.1002/9781119960003.ch8, 2011. a
Wilks, D. S. and Vannitsem, S.: Chapter 1 – Uncertain Forecasts From Deterministic Dynamics, 1–13, Elsevier, https://doi.org/10.1016/B978-0-12-812372-0.00001-7, 2018. a
Worsnop, R. P., Scheuerer, M., Giuseppe, F. D., Barnard, C., Hamill, T. M., and Vitolo, C.: Probabilistic Fire Danger Forecasting: A Framework for Week-Two Forecasts Using Statistical Postprocessing Techniques and the Global ECMWF Fire Forecast System (GEFF), Weather Forecast., 36, 2113–2125, https://doi.org/10.1175/WAF-D-21-0075.1, 2021. a
Short summary
Probabilistic ensemble forecasts of the Canadian Forest Fire Weather Index (FWI) can be used to estimate the possible wildfire risk but require post-processing to provide accurate and reliable predictions. This article presents a calibration method using non-homogeneous Gaussian regression to statistically post-process FWI forecasts up to 15 d. Calibration improves the forecast especially at short lead times and in regions with high fire risk.
Probabilistic ensemble forecasts of the Canadian Forest Fire Weather Index (FWI) can be used to...
Altmetrics
Final-revised paper
Preprint