Improvement of the AEMET operational methodology for the estimation of areas with wind gusts

DOI: 10.3369/tethys.2013.10.04

Tethys no. 10 pp.: 35 - 44

Abstract

The CCS (the Spanish Insurance Compensation Consortium) is the national agency that provides insurance coverage against weather events that involve an extraordinary risk. One of the extraordinary risks covered by the CCS refers to extraordinary wind, defined as wind with gusts exceeding 120 km h-1. For about two years, the operational procedure performed in AEMET (the Spanish Meteorological Agency) for estimating the areas with maximum wind gusts has been using the technique of universal kriging interpolation based on observational data. External variables involved in the interpolation are the ground elevation, distance to the sea and the HIRLAM 0.05 model output of maximum gust field. The aim of the procedure is to delineate areas with maximum wind gusts that exceed 120 km h-1. During previous research focused on the study of the accuracy given by the introduction of the HIRLAM model for this estimation technique, various validation analyses were conducted. These validations show a systematic negative bias for the estimation of high values of maximum gust, which implies an underestimation of the gusts through the operational procedure. This paper presents a new method of interpolation that provides a significant improvement. The bias is reduced by approximately 60% for stations that have maximum wind speeds of more than 80 km h-1. The new methodology combines two interpolation fields. The first is obtained by applying the current operational method and includes all observational data. The second is obtained similarly, but using only the observation values of meteorological stations that have high values of maximum gust. The combination of both fields is based on a weighting given at each grid point, which depends on the overall density of the observations by region.

References

  • - Burrough, P. A. and McDonnell, R. A., 1998: Principles of Geographical Information Systems, Oxford University Press, 333, ISBN-13 978-0-19-823365-7, ISBN-10 0-19-823365-5.
  • - Consorcio de Compensación de Seguros, 2012: Recopilación Legislativa, Edición de febrero de 2012, 202.
  • - Elosua, P., 2011: Introducción al entorno R, Universidad del País Vasco, 102, ISBN: 978-84-9860-497-9.
  • - García-Legaz, C. and Valero, F., 2003: Riesgos Climáticos e Impacto Ambiental, Ed. Complutense de Madrid, 356, ISBN: 84-7491-711-5.
  • - R Core Team, 2013: R: A language and environment for statistical computing, Technical report, R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org.
  • - Samper, F. J. and Carrera, J., 1990: Geoestadística. Aplicaciones a la hidrología subterránea, Ed. Complutense de Madrid, 484, ISBN: 84-404-6045-7.
  • - Venables,W. N. and Ripley, B. D., 2005: Modern Applied Statistics with R, Springer-Verlag, 106, ISBN: 3900051-127.
  • - Venables, W. N. and Smith, D. M., 2012: An Introduction to R, R Foundation for Statistical Computing, Vienna, Austria, 95, ISBN: 3-900051-12-7.


Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License


Indexed in Scopus, Thomson-Reuters Emerging Sources Citation Index (ESCI), Scientific Commons, Latindex, Google Scholar, DOAJ, ICYT (CSIC)

Partially funded through grants CGL2007-29820-E/CLI, CGL2008-02804-E/, CGL2009-07417-E and CGL2011-14046-E of the Spanish Ministry of Science and Innovation