IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p6913-d661448.html
   My bibliography  Save this article

Expected Recurrence of Extreme Winds in Northwestern Sahara and Associated Uncertainties

Author

Listed:
  • Elena García Bustamante

    (Departmento de Energía, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), 28040 Madrid, Spain)

  • J. Fidel González Rouco

    (Department Física de la Tierra, Astronomía y Astrofísica, Universidad Complutense de Madrid-Instituto de Geociencias (CSIC-UCM), 28040 Madrid, Spain)

  • Jorge Navarro

    (Departmento de Energía, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas (CIEMAT), 28040 Madrid, Spain)

  • Etor E. Lucio Eceiza

    (Institute of Meteorology, Freie Universtität Berlin, 12165 Berlin, Germany
    Deutsches Klimrechenzentrum Gmbh (DKRZ), 20146 Hamburg, Germany)

  • Cristina Rojas Labanda

    (Department Física de la Tierra, Astronomía y Astrofísica, Universidad Complutense de Madrid-Instituto de Geociencias (CSIC-UCM), 28040 Madrid, Spain)

Abstract

Estimating the probability of the occurrence of hazardous winds is crucial for their impact in human activities; however, this is inherently affected by the shortage of observations. This becomes critical in poorly sampled regions, such as the northwestern Sahara, where this work is focused. The selection of any single methodological variant contributes with additional uncertainty. To gain robustness in the estimates, we expand the uncertainty space by applying a large body of methodologies. The methodological uncertainty is constrained afterward by keeping only the reliable experiments. In doing so, we considerably narrow the uncertainty associated with the wind return levels. The analysis suggest that not necessarily all methodologies are equally robust. The highest 10-min speed (wind gust) for a return period of 50 years is about 45 ms − 1 (56 ms − 1 ). The intensity of the expected extreme winds is closely related to orography. The study is based on wind and wind gust observations that were collected and quality controlled for the specific purposes herein. We also make use of a 12-year high-resolution regional simulation to provide simulation-based wind return level maps that endorse the observation-based results. Such an exhaustive methodological sensitivity analysis with a long high-resolution simulation over this region was lacking in the literature.

Suggested Citation

  • Elena García Bustamante & J. Fidel González Rouco & Jorge Navarro & Etor E. Lucio Eceiza & Cristina Rojas Labanda, 2021. "Expected Recurrence of Extreme Winds in Northwestern Sahara and Associated Uncertainties," Energies, MDPI, vol. 14(21), pages 1-32, October.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6913-:d:661448
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/6913/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/21/6913/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bracale, Antonio & Carpinelli, Guido & De Falco, Pasquale, 2017. "A new finite mixture distribution and its expectation-maximization procedure for extreme wind speed characterization," Renewable Energy, Elsevier, vol. 113(C), pages 1366-1377.
    2. Efthimiou, G.C. & Kumar, P. & Giannissi, S.G. & Feiz, A.A. & Andronopoulos, S., 2019. "Prediction of the wind speed probabilities in the atmospheric surface layer," Renewable Energy, Elsevier, vol. 132(C), pages 921-930.
    3. Hans von Storch, 2010. "Climate models and modeling: an editorial essay," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 1(3), pages 305-310, May.
    4. Karvanen, Juha, 2006. "Estimation of quantile mixtures via L-moments and trimmed L-moments," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 947-959, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas W. Keelin & Bradford W. Powley, 2011. "Quantile-Parameterized Distributions," Decision Analysis, INFORMS, vol. 8(3), pages 206-219, September.
    2. Philippe Bernard & Najat El Mekkaoui De Freitas & Bertrand B. Maillet, 2022. "A financial fraud detection indicator for investors: an IDeA," Annals of Operations Research, Springer, vol. 313(2), pages 809-832, June.
    3. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
    4. Gourieroux, C. & Jasiak, J., 2008. "Dynamic quantile models," Journal of Econometrics, Elsevier, vol. 147(1), pages 198-205, November.
    5. Darolles, Serge & Gourieroux, Christian & Jasiak, Joann, 2009. "L-performance with an application to hedge funds," Journal of Empirical Finance, Elsevier, vol. 16(4), pages 671-685, September.
    6. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01467736, HAL.
    7. Karvanen, Juha & Nuutinen, Arto, 2008. "Characterizing the generalized lambda distribution by L-moments," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1971-1983, January.
    8. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Post-Print halshs-01467736, HAL.
    9. Cheng Peng & Stanislav Uryasev, 2023. "Factor Model of Mixtures," Papers 2301.13843, arXiv.org, revised Mar 2023.
    10. Andrea Bastianin, 2020. "Robust measures of skewness and kurtosis for macroeconomic and financial time series," Applied Economics, Taylor & Francis Journals, vol. 52(7), pages 637-670, February.
    11. Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    12. Di Nardo, E. & Guarino, G. & Senato, D., 2008. "Symbolic computation of moments of sampling distributions," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4909-4922, July.
    13. Karvanen, Juha & Kulathinal, Sangita & Gasbarra, Dario, 2009. "Optimal designs to select individuals for genotyping conditional on observed binary or survival outcomes and non-genetic covariates," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1782-1793, March.
    14. Guillaume Bernis & Nicolas Brunel & Antoine Kornprobst & Simone Scotti, 2017. "Stochastic Evolution of Distributions - Applications to CDS indices," Documents de travail du Centre d'Economie de la Sorbonne 17007, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    15. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    16. Gilleland, Eric & Katz, Richard W., 2016. "extRemes 2.0: An Extreme Value Analysis Package in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i08).
    17. Asquith, William H., 2014. "Parameter estimation for the 4-parameter Asymmetric Exponential Power distribution by the method of L-moments using R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 955-970.
    18. Delicado, P. & Goria, M.N., 2008. "A small sample comparison of maximum likelihood, moments and L-moments methods for the asymmetric exponential power distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1661-1673, January.
    19. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
    20. Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6913-:d:661448. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.