Synoptic weather patterns conducive to lightning-ignited wildfires in Catalonia
Meteorological Service of Catalonia, Generalitat de Catalunya,
Barcelona 08029, Spain
Lightning Research Group, Technical University of Catalonia, Terrassa 08222, Spain
Juan Carlos Peña
Meteorological Service of Catalonia, Generalitat de Catalunya,
Barcelona 08029, Spain
Fluvalps-PaleoRisk Research Group, Department of Geography,
University of Barcelona, Barcelona 08001, Spain
Xavier Soler
Meteorological Service of Catalonia, Generalitat de Catalunya,
Barcelona 08029, Spain
Montse Aran
Meteorological Service of Catalonia, Generalitat de Catalunya,
Barcelona 08029, Spain
Núria Pérez-Zanón
Earth Sciences Department - Earth System Services Group, Barcelona
Supercomputing Center, Barcelona 08034, Spain
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Cited articles
Amraoui, M., Liberato, M. L. R., Calado, T. J., DaCamara, C. C., Pinto Coelho,
L., Trigo, R. M., and Gouveia, C. M.: Fire activity over Mediterranean Europe
based on information from Meteosat-8, For. Ecol. Manag., 294, 62–75, 2013.
Anderson, K. R.: A model to predict lightning-caused fire occurrences, Int.
J. Wildland Fire, 11, 163–172, https://doi.org/10.1071/WF02001, 2002.
Anderson, G. and Klugmann, D.: A European lightning density analysis using 5 years of ATDnet data, Nat. Hazards Earth Syst. Sci., 14, 815–829, https://doi.org/10.5194/nhess-14-815-2014, 2014.
Aran, M., Sairouni, A., Miro, J., Moré, J., Toda, J., and Cunillera, J.: The impact of
two assimilation techniques in two tornadoes cases. Synoptic and mesoscale
diagnosis of a tornado event in Castellcir, Catalonia, on 18th October 2005,
9th EGU Plinius, Conference on Mediterranean Storms, Varenna, Italy, 10–13 September 2007.
Aran, M., Peña, J. C., and Torà, M.: Atmospheric circulation patterns
associated with hail events in Lleida (Catalonia), Atmos. Res., 100,
428–438, https://doi.org/10.1016/j.atmosres.2010.10.029, 2011.
Aran, M., Peña, J. C., Pineda, N., Soler, X., and Pérez-Zanón, N.:
Ten-year lightning patterns in Catalonia using Principal Component Analysis,
European Conference on Severe Storms, Wiener Neustadt, Austria, 14–18 September 2015.
Benson, R. P., Roads, J. O., and Wiese, D. R.: Climatic and weather factors
affecting fire occurrence and behaviour, in: Developments in Environment Science 8, edited by: Bytnerowicz, A., Arbaugh, M., Riebau, A., and Andersen, C., 37–59, https://doi.org/10.1016/S1474-8177(08)00002-8,
2009.
Campins, J., Jansá, A., Benech, B., Koffi, E., and Bessemoulin, P.: PYREX
Observation and Model Diagnosis of the Tramontane Wind, Meteorol. Atmos.
Phys., 56, 209–228, https://doi.org/10.1007/BF01030138, 1995.
Cattell, R. B.: The scree-test for the number of the factors, Multivar.
Behav. Res., 1, 245–276,
https://doi.org/10.1207/s15327906mbr0102_10, 1966.
Costafreda-Aumedes, S., Cardil, A., Molina, D., Daniel, S., Mavsar, R., and
Vega-Garcia, C.: Analysis of factors influencing deployment of fire
suppression resources in Spain using artificial neural networks, iForest Biogeosciences For., 9, 138–145, https://doi.org/10.3832/ifor1329-008, 2016.
Diab, R. D., Preston-Whyte, R. A., and Washington, R.: Distribution of rainfall by
synoptic type over Natal, South Africa, Int. J. Climatol., 11, 877–888,
https://doi.org/10.1002/joc.3370110806, 1991.
Dowdy, A. J. and Mills, G. A.: Characteristics of lightning-attributed wildland
fires in south-east Australia, Int. J. Wildland Fire, 21, 521–524,
https://doi.org/10.1071/WF10145, 2012.
Duane, A. and Brotons, L.: Synoptic weather conditions and changing fire regimes
in a Mediterranean environment, Agr. Forest Meteorol., 253–254, 190–202,
2018.
Duff, T. J., Keane, R. E., Penman, T. D., and Tolhurst, K. G.: Revisiting wildland
fire fuel quantification methods: the challenge of understanding a dynamic,
biotic entity, Forests, 8, 351, https://doi.org/10.3390/f8090351, 2017.
Evett, R. R., Mohrle, C. R., Hall, B. L., Brown, T. J., and Stephens, S. L.: The
effect of monsoonal atmospheric moisture on lightning fire ignitions in
southwestern North America, Agr. Forest Meteorol., 14, 1478–1487,
2008.
Flannigan, M. D. and Wotton, B. M: Lightning-ignited forest fires in north
western Ontario, Can. J. For. Res., 21, 277–287, 1991.
Ganteaume, A., Camia, A., Jappiot, M., San-Miguel-Ayanz, J., Long-Fournel,
M., and Lampin, C.: A review of the main driving factors of forest fire ignition
over Europe, Environ. Manage., 51, 651–662, 2013.
García-Ortega, E., Trobajo, M. T., López, L., and Sánchez, J. L.: Synoptic patterns associated with wildfires caused by lightning in Castile and Leon, Spain, Nat. Hazards Earth Syst. Sci., 11, 851–863, https://doi.org/10.5194/nhess-11-851-2011, 2011.
GENCAT: Base de dades d'incendis forestals. Servei de Prevenció
d'Incendis Forestals, Departament d'Agricultura, Ramaderia, Pesca i
Alimentació, Generalitat de Catalunya, [Wildfire Database, Government of Catalonia], http://agricultura.gencat.cat/ca/ambits/medi-natural/incendis-forestals/dades-incendis/ (last access: 30 May 2022), 2021.
Haklander, A. J. and Van Delden, A.: Thunderstorm Predictors and their Forecast Skill
for the Netherlands, Atmos. Res., 67-68, 273,
https://doi.org/10.1016/S0169-8095(03)00056-5, 2003.
Hall, B. L.: Precipitation associated with lightning-ignited wildfires in
Arizona and New Mexico, Int. J. Wildland Fire, 16, 242–254,
2007.
Hall, B. L.: Fire ignitions related to radar reflectivity patterns in Arizona
and New Mexico, Int. J. Wildland Fire, 17, 317–327, 2008.
Jain, P., Wang, X., and Flannigan, M. D.: Trend analysis of fire season length and
extreme fire weather in North America between 1979 and 2015, Int. J. Wildland Fire, 26, 1009–1020, 2018.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-Year Reanalysis Project, B. Amer. Meteorol. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kassomenos, P.: Synoptic circulation control on wild fire occurrence, Phys. Chem. Earth, 35, 544–552, 2010.
Keeley, J. E. and Syphard, A. D.: Large California wildfires: 2020 fires in
historical context, Fire Ecology, 17, 22,
https://doi.org/10.1186/s42408-021-00110-7, 2021.
Kotroni, V. and Lagouvardos, K.: Lightning in the Mediterranean and its
relation with sea-surface temperature, Environ. Res. Lett., 11, 3,
https://doi.org/10.1088/1748-9326/11/3/034006, 2016.
Larjavaara, M., Pennanen, J., and Tuomi, T.: Lightning that ignites forest fires
in Finland, Agric. For. Meteorol., 132, 171–180, 2005.
Latham, D. and Williams, E.: Lightning and forest fires, in: Forest fires: Behavior and Ecological Effects, edited by: Johnson, E. A. and Miyanishi, K., Academic Press, Inc., San Diego,
https://doi.org/10.1016/B978-012386660-8/50013-1, 2001.
Martin-Vide, J., Sanchez-Lorenzo, A., Lopez-Bustins, J. A., Cordobilla, M. J., Garcia-Manuel, A., and Raso, J. M.: Torrential rainfall in northeast of the Iberian Peninsula: synoptic patterns and WeMO influence, Adv. Sci. Res., 2, 99–105, https://doi.org/10.5194/asr-2-99-2008, 2008.
Mateo, J., Ballart, D., Brucet, C., Aran, M., and Bech, J.: Heavy rain and a
tornado outbreak during the pass of a squall line over Catalonia, Atmos.
Res., 93, 131–146, 2008.
McQueen, J.: Some methods for classification and analysis of multivariate
observations, in: Proceedings of the Fifth Berkeley Symposium on
Mathematical Statistics and Probability, Vol. 1, Statistical Laboratory, University of California 21 June–18 July 1965 and 27 December 1965–7 January 1966, University of California Press, Berkeley, California, 281–297, 1967.
Michailidou, C., Maheras, P., Arseni-Papadimititriou, A., Kolyva-Machera,
F., and Anagnostopoulou, C. A.: Study of weather types at Athens and Thessaloniki
and their relationship to circulation types for the cold-wet period, Part
II: discriminant analysis, Theor. Appl. Climatol., 97, 179,
https://doi.org/10.1007/s00704-008-0058-9, 2009.
Millán, M. M., Estrela, M. J., and Badenas, C.: Meteorological Processes
Relevant to Forest Fire Dynamics on the Spanish Mediterranean Coast, J.
Appl. Meteorol., 37, 83–100, 1998.
Miller, R. C.: Notes on analysis and severe storm forecasting procedures of
the Air Force, Global Weather Central Tech Report 200 (Revised), AWS, USAF
Headquarters, 1972.
Morin, A. A., Albert-Green, A., Woolford, D. G., and Martell, D. L.: The use of
survival analysis methods to model the control time of forest fires in
Ontario, Can. J. For. Res., 24, 964–973, 2015.
Nauslar, N. J., Kaplan, M. L., Wallmann, J., and Brown, T. J.: A forecast procedure
for dry thunderstorms, J. Oper. Meteorol., 1, 200–214, https://doi.org/10.15191/nwajom.2013.0117, 2013.
Oliveras, I., Gracia, M., Moré, G., and Retana, J.: Factors influencing the
pattern of fire severities in a large wildfire under extreme meteorological
conditions in the Mediterranean basin, Int. J. Wildland Fire, 18, 755–764,
https://doi.org/10.1071/WF08070, 2009.
Pascual, R. and Callado, A.: Meso-analysis of recurrent convergence zones in the
north-eastern Iberian Peninsula, Proceedings of the Second European Conference on Radar Meteorology (ERAD), Delft, Netherlands, 18–22 November 2002, 59–64, 2002.
Peña, J. C., Aran, M., Cunillera, J., and Amaro, J.: Atmospheric circulation patterns associated with strong wind events in Catalonia, Nat. Hazards Earth Syst. Sci., 11, 145–155, https://doi.org/10.5194/nhess-11-145-2011, 2011.
Peña, J. C., Aran, M., Raso, J. M., Pérez-Zanó
n, N.:Principal : Principal sequence pattern
analysis of episodes of excess mortality due to heat in the Barcelona
metropolitan area, Int. J. Biometeorol., 59, 435–446, https://doi.org/10.1007/s00484-014-0857-x, 2015.
Pereira, M. G., Trigo, R. M., DaCamara, C. C., Pereira, J. M. C., and Leite, S. M.:
Synoptic patterns associated with large summer forest fires in Portugal,
Agric. For. Meteorol., 129, 11–25, 2005.
Pérez-Invernón, F. J., Huntrieser, H., Soler, S., Gordillo-Vázquez, F. J., Pineda, N., Navarro-González, J., Reglero, V., Montanyà, J., van der Velde, O., and Koutsias, N.: Lightning-ignited wildfires and long continuing current lightning in the Mediterranean Basin: preferential meteorological conditions, Atmos. Chem. Phys., 21, 17529–17557, https://doi.org/10.5194/acp-21-17529-2021, 2021.
Pineda, N. and Montanyà, J.: Lightning detection Spain: the particular case
of Catalonia, in: Lightning:
Principles Instruments and Applications, edited by: Betz, H.-D., Schumann, U., and Laroche, P., Springer, Netherlands, 161–185, ISBN 978-1-4020-9079-0, 2009.
Pineda, N. and Rigo, T.: The rainfall factor in lightning-ignited wildfires in
Catalonia, Agr. Forest Meteorol., 239, 249–263,
https://doi.org/10.1016/j.agrformet.2017.03.016, 2017.
Pineda, N., Rigo, T., Bech, J., and Soler, X.: Lightning and precipitation
relationship in summer thunderstorms: Case studies in the North Western
Mediterranean region, Atmos. Res., 85, 159–170,
https://doi.org/10.1016/j.atmosres.2006.12.004, 2007.
Pineda, N., Soler, X., and Vilaclara, E.: Aproximació a la climatologia de
llamps aCatalunya, Nota d'estudi del Servei Meteorològic de Catalunya, Generalitat de Catalunya B-7024-2011, 73, ISBN 9788439387282, 2011 (in Catalan).
Pineda, N., Montanyà, J., and van der Velde O. A.: Characteristics of lightning
related to wildfire ignitions in Catalonia, Atmos. Res., 135–136, 380–387, 2014.
Pineda, N., Altube, P., Alcasena, F. J., Casellas, E., San Segundo, H., and Montanyà, J.: Characterizing the holdover phase of lightning-ignited
wildfires in Catalonia, Agr. Forest Meteorol., in review: 2022.
Podur, J., Martell, D. L., and Csillag, F.: Spatial patterns of lightning caused
forestfires in Ontario, 1976–1998, Ecol. Modell., 164, 1–20, 2003.
Poelman, D. R., Schulz, W., Diendorfer, G., and Bernardi, M.: The European lightning location system EUCLID – Part 2: Observations, Nat. Hazards Earth Syst. Sci., 16, 607–616, https://doi.org/10.5194/nhess-16-607-2016, 2016.
Pyne, S. J., Andrews, P. L., and Laven, R. D.: Introduction to Wildland Fire, John Wiley and Sons, New York, ISBN 139780471549130, 1996.
Rasilla, D. F., Garcia-Codron, J. C., Carracedo, V., and Diego, C.: Circulation
patterns, wildfire risk and wildfire occurrence at continental Spain, Phys.
Chem. Earth, 35, 553–560, 2010.
Resco de Dios, V. R., Camprubí, À. C., Pérez-Zanón, N., Peña, J. C., Martínez del Castillo, E., Rodrigues, M., Yao, Y., Yebra, M., Vega-García, C., and Boer, M. M.: Convergence in critical fuel
moisture and fire weather thresholds associated with fire activity in the
pyroregions of Mediterranean Europe, Sci. Total Environ., 806, 4, https://doi.org/10.1016/j.scitotenv.2021.151462, 2022.
Richman, M. B.: Rotation of Principal Components, J. Clim. 6, 293–335, 1986.
Rorig, M. L. and Ferguson, S. A.: Characteristics of lightning and wildland fire
ignition in the Pacific Northwest, J. Appl. Meteorol., 38, 1565–1575, 1999.
Rorig, M. L., McKay, S. J., Ferguson, S. A., and Werth, P.: Model-generated
predictions of dry thunderstorm potential, J. Appl. Meteorol. Clim., 46,
605–614, 2007.
San-Miguel-Ayanz, J., Moreno, J. M., and Camia, A.: Analysis of large fires in
European Mediterranean landscapes: lessons learned and perspectives, Forest
Ecol. Manag., 294, 11–22, 2013.
Sioutas, M. V. and Floucas, H. A.: Hailstorms in Northern Greece: synoptic
patterns and thermodynamic environment, Theor. Appl. Climatol., 75, 189–202,
https://doi.org/10.1007/s00704-003-0734-8, 2003.
Skinner, W. R., Flannigan, M. D., Stocks, B. J., Martell, D. L., Wotton, B.
M., Todd, J. B., and Bosch, E. M. A.: 500 hPa synoptic wildland fire climatology
for large Canadian forest fires, 1959–1996, Theor. Appl. Climatol., 71,
157-169, 2002.
Soler, A., Pineda, N., San Segundo, H., Bech, J., and Montanya, J.:
Characterisation of thunderstorms that caused lightning-ignited wildfires,
Int. J. Wildland Fire, 30, 954–970, https://doi.org/10.1071/WF21076, 2021.
Thorndike, R. L.: Who belongs in the family?, Psychometrika, 18, 267–276,
https://doi.org/10.1007/BF02289263, 1953.
van Wagtendonk, J. W. and Cayan, D. R.: Temporal and Spatial Distribution of
Lightning Strikes in California in Relation to Large-Scale Weather Patterns,
Fire Ecol., 4, 34–56, 2008.
Ward, J. H.: Hierarchical grouping to optimize an objective function, J. Am.
Stat. Assoc., 58, 236–244, https://doi.org/10.1080/01621459.1963.10500845, 1963.
Wastl, C., Schunk, C., Lüpke, M., Cocca, G., Conedera, M., Valese, E., and Menzel, A.: Large-scale weather types, forest fire danger, and wildfire
occurrence in the Alps, Agr. Forest Meteorol., 168, 15–25, 2013.
Westerling, A. L., Hidalgo, H. G., Cayan, D. R., and Swetnam, T.: Warming and earlier spring
increase western US forest wildfire activity, Science, 313, 940–943,
https://doi.org/10.1126/science.1128834, 2006.
Wotton, B. M. and Martell, D. L.: A lightning fire occurrence model for Ontario,
Can. J. For. Res., 35, 1389–1401, https://doi.org/10.1139/x05-071, 2005.
Zhang, Y., Moges, S., and Block, P.: Optimal cluster analysis for objective
regionalization of seasonal precipitation in regions of high
spatial–temporal variability: application to Western Ethiopia, J. Climate,
29, 3697–3717, https://doi.org/10.1175/JCLI-D-15-0582.1, 2016.
Short summary
Wildfire origins can be related to human activity or to natural phenomena, like lightning. Under favourable environmental conditions, lightning ignitions can develop into a fire. In the present study, we analyse the kind of weather that favours wildfires ignited by lightning in Catalonia. We have found that most fires occur under three types of weather. These results help to improve our understanding of lightning fires and are of great assistance to wildfire management agencies.
Wildfire origins can be related to human activity or to natural phenomena, like lightning. Under...