Spanish Extreme Winds and Their Relationships with Atlantic Large-Scale Atmospheric Patterns

  1. Pascual, Alvaro 1
  2. Valero, Francisco 1
  3. Martín, Maria Luisa 3
  4. García-Legaz, Carlos 2
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Agencia Estatal de Meteorología
    info

    Agencia Estatal de Meteorología

    Madrid, España

    ROR https://ror.org/04kxf1r09

  3. 3 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

Erakutsi afiliazioak +
Aldizkaria:
American Journal of Climate Change

ISSN: 2167-9495,  2167-9509

Argitalpen urtea: 2013

Alea: 02

Zenbakia: 03

Orrialdeak: 23-35

Mota: Artikulua

DOI: 10.4236/AJCC.2013.23A003 GOOGLE SCHOLAR lock_openSarbide irekia editor

Beste argitalpen batzuk: American Journal of Climate Change

Garapen Iraunkorreko Helburuak

Laburpena

The purpose of this work is to review procedures to obtain relationships between wind and large-scale atmosphericfields, with special emphasis on extreme situation results. Such relationships are obtained by using different methodsand techniques such as wind cumulative probability functions and composite maps. The analyses showed differentmean atmospheric situations associated with the different wind patterns, in which strong atmospheric gradients can berelated to moderate to strong winds in Spain. Additionally, a statistical downscaling analog model, developed by theauthors, is used for diagnosing large-scale atmospheric circulation patterns and subsequently estimating extreme windprobabilities. From an atmospheric circulation pattern set obtained by multivariate methodology applied to a large-scaleatmospheric circulation field, estimations of wind fields, particularly extreme winds, are obtained by means of the analogs methodology. Deterministic and probabilistic results show that gust behaviour is quite better approximated thanmean wind speed, in general. The model presents some underestimations except for strong winds. Moreover, the modelshows better probabilistic wind results over the Spanish northern area, highlighting that the atmospheric situationscoming from the Atlantic Ocean are better recovered to predict mean wind and gusts in the Northern Peninsula.

Erreferentzia bibliografikoak

  • “Swiss Re,” 2000. http://www.swissre.com/about_us/art_architecture/Swiss_Re_Next.html
  • 10.1002/j.1477-8696.2001.tb06540.x
  • 10.5194/nhess-10-97-2010
  • 10.1175/1520-0450(1987)026<1124:IOSATW>2.0.CO;2
  • 10.1175/1520-0477(1987)068<1111:RTAOLW>2.0.CO;2
  • 10.1016/S0167-6105(97)00018-4
  • M. Gaya, J. Amaro, M. Aran and M. C. Llasat, “Preliminary Results of the Societal Impact Research Group of MEDEX: The Request Database (2000-2002) of Two Meteorological Services,” Proceedings of 9th EGS Plinius Conference, Nisosia, 2008, p. 12.
  • J. Amaro, M. Aran, L. Barberia and M. C. Llasat, “The Strong Wind Event of 24th January 2009 in Catalonia: A Social Impact Analysis,” Proceedings of 10th EGS Plinius Conference, Barcelona, 2009, p. 10.
  • 10.5194/nhess-11-971-2011
  • 10.1002/we.401
  • 10.1002/we.284
  • 10.1002/for.1194
  • 10.1016/j.apenergy.2008.10.009
  • 10.1016/j.atmosres.2010.07.002
  • 10.1007/978-3-662-04313-4_31
  • 10.1007/s00382-003-0382-9
  • 10.1002/joc.993
  • 10.1002/joc.2127
  • 10.5194/nhess-11-191-2011
  • 10.1016/j.atmosres.2010.08.023
  • 10.1029/JC095iC01p00679
  • 10.1029/JC095iC07p11425
  • 10.1175/1520-0477(1997)078<0637:PASOF>2.0.CO;2
  • 10.1007/s00585-997-0494-3
  • 10.1175/1520-0469(1969)26<636:APARBN>2.0.CO;2
  • 10.1007/978-0-387-21606-5
  • 10.3354/cr024199
  • K. Fraedrich, C. C. Raible and F. Sielmann, “Analog Ensemble Forecasting of Tropical Cyclone Tracks in the Australian Region,” Weather and Forecasting, Vol. 18, 2003, pp. 3-11.
  • 10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2
  • 10.1177/030913339702100403
  • H. M. Dool van den, “Searching for Analogs, How Long Must We Wait?” Tellus, Vol. 46A, 1994, pp. 314-324.
  • A. Pascual, M. L. Martin, F. Valero, D. Santos-Munoz,, A. Morata and M. Y. Luna, “Development of an Analogous Model for Wind Prediction Using Principal Components,” The SAFEWIND Workshop, Oldemburg, 2010, 22 p. www.safewind.eu
  • 10.5194/adgeo-16-49-2008
  • 10.1002/joc.1726
  • A. J. Simmons and J. K. Gibson, “The ERA-40 Project Plan,” ERA-40 Project Report Series No. 1. ECMWF, Reading, 2000.
  • R. W. Preisendorfer, “Principal Component Analysis in Meteorology and Oceanography,” Elsevier Science Publishers BV, Amsterdam, 1998.
  • 10.1016/j.atmosres.2012.10.033
  • H. B. Bluestein, “Synoptic Dynamic Meteorology in Midlatitudes. Vol. II. Observations and Theory of Weather Systems,” Oxford University Press, Oxford, 1993.
  • 10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2
  • 10.1002/met.283
  • 10.1111/j.1600-0870.2011.00545.x
  • 10.1016/j.atmosres.2012.01.011
  • A. S. Cofino, “Técnicas Estadísticas y Neuronales de Agrupamiento Adaptativo Para la Predicción Probabilística de Fenómenos Meteorológicos Locales. Aplicación en el Corto Plazo y en la Predicción Estacional,” Tesis Doctoral, Universidad de Cantabria, Cantabria, 2004.
  • 10.1175/1520-0493(2004)132<2169:CMFSDI>2.0.CO;2
  • 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2