Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Haas, Rabea; Pinto, Joaquim G.; Born, Kai (2014)
Publisher: American Geophysical Union
Languages: English
Types: Article
Windstorms are a main feature of the European climate and exert strong socioeconomic impacts. Large effort has been made in developing and enhancing models to simulate the intensification of windstorms, resulting footprints, and associated impacts. Simulated wind or gust speeds usually differ from observations, as regional climate models have biases and cannot capture all local effects. An approach to adjust regional climate model (RCM) simulations of wind and wind gust toward observations is introduced. For this purpose, 100 windstorms are selected and observations of 173 (111) test sites of the German Weather Service are considered for wind (gust) speed. Theoretical Weibull distributions are fitted to observed and simulated wind and gust speeds, and the distribution parameters of the observations are interpolated onto the RCM computational grid. A probability mapping approach is applied to relate the distributions and to correct the modeled footprints. The results are not only achieved for single test sites but for an area-wide regular grid. The approach is validated using root-mean-square errors on event and site basis, documenting that the method is generally able to adjust the RCM output toward observations. For gust speeds, an improvement on 88 of 100 events and at about 64% of the test sites is reached. For wind, 99 of 100 improved events and ~84% improved sites can be obtained. This gives confidence on the potential of the introduced approach for many applications, in particular those considering wind data.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Ágústsson, H., and H. Ólafsson (2009), Forecasting wind gusts in complex terrain, Meteorol. Atmos. Phys., 103, 173-185.
    • Born, K., P. Ludwig, and J. G. Pinto (2012), Wind gust estimation for Mid-European winter storms: Towards a probabilistic view, Tellus A, 64, 17,471, doi:10.3402/tellusa.v64i0.17471.
    • Brasseur, O. (2001), Development and application of a physical approach to estimating wind gusts, Mon. Weather Rev., 129, 5-25.
    • Buishand, A., and T. Brandsma (1997), Comparison of circulation classification schemes for predicting temperature and precipitation in the Netherlands, Int. J. Climatol., 17, 875-889.
    • Christensen, J., et al. (2007), Regional climate projections, in Climate Change, 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, chap. 11, pp. 847-940, Cambridge Univ. Press, Cambridge.
    • De Rooy, W. C., and K. Kok (2004), A combined physical-statistical approach for the downscaling of model wind speed, Wea. Forecast., 19, 485-495.
    • Dee, D. P., et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553-597.
    • Donat, M. G., G. C. Leckebusch, J. G. Pinto, and U. Ulbrich (2010), Examination of wind storms over Central Europe with respect to circulation weather types and NAO phases, Int. J. Climatol., 30, 1289-1300, doi:10.1002/joc.1982.
    • Fuentes, U., and D. Heimann (2000), An improved statistical-dynamical downscaling scheme and its application to the alpine precipitation climatology, Theor. Appl. Climatol., 65, 119-135.
    • Giorgi, F., and L. O. Mearns (1991), Approaches to the simulation of regional climate changes: A review, Rev. Geophys., 29, 191-216.
    • Glahn, H. R., and D. A. Lowry (1972), The use of Model Output Statistics (MOS) in objective weather forecasting, J. Appl. Meteorol., 11, 1203-1211.
    • Glahn, B., K. Gilbert, R. Cosgrove, D. P. Ruth, and K. Sheets (2009), The gridding of MOS, Wea. Forecast., 24, 520-529.
    • Goyette, S., M. Beniston, D. Caya, J. P. R. Laprise, and P. Jungo (2001), Numerical investigation of an extreme storm with the Canadian Regional Climate Model: The case study of windstorm VIVIAN, Switzerland, February 27, 1990, Clim. Dyn., 18, 145-178, doi:10.1007/s003820100166.
    • Goyette, S., O. Brasseur, and M. Beniston (2003), Application of a new wind gust parameterization: Multiscale case studies performed with the Canadian regional climate model, J. Geophys. Res., 108(D134374), doi:10.1029/2002JD002646.
    • Haas, R., and J. G. Pinto (2012), A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms, Geophys. Res. Lett., 39, L23804, doi:10.1029/2012GL054014.
    • Hanssen-Bauer, I., C. Achberger, R. E. Benestad, D. Chen, and E. J. Førland (2005), Statistical downscaling of climate scenarios over Scandinavia, Clim. Res., 29, 255-268.
    • Hewitson, B. C., and R. G. Crane (1996), Climate downscaling: Techniques and application, Clim. Res., 7, 85-95.
    • Jenkinson, A. F., and F. P. Collinson (1977), An initial climatology of gales over the North Sea, Synoptic Climatology Branch Memorandum, 62, Met. Office, Bracknell, U. K.
    • Jones, P. D., M. Hulme, and K. R. Briffa (1993), A comparison of Lamb circulation types with an objective classification scheme, Int. J. Climatol., 13, 655-663, doi:10.1002/joc.3370130606.
    • Jones, P. D., C. Harpham, and K. R. Briffa (2012), Lamb weather types derived from reanalysis products, Int. J. Climatol., 33, 1129-1139, doi:10.1002/joc.3498.
    • Justus, C. G., W. R. Hargraves, A. Mikhail, and D. Graber (1978), Methods for estimating wind speed distributions, J. Appl. Meteorol., 17, 350-353.
    • Kim, J.-W., J.-T. Chang, N. L. Baker, D. S. Wilks, and W. L. Gates (1984), The statistical problem of climate inversion: Determination of the relationship between local and large-scale climate, Mon. Weather Rev., 112, 2069-2077.
    • Klawa, M., and U. Ulbrich (2003), A model for the estimation of storm losses and the identification of severe winter storms in Germany, Nat. Hazards Earth Syst. Sci., 3, 725-732.
    • Klein, W. H., and H. R. Glahn (1974), Forecasting local weather by means of Model Output Statistics, Bull. Am. Meteorol. Soc., 55, 1217-1227.
    • Lamb, H. H. (1972), British Isles Weather Types and a Register of the Daily Sequence of Circulation Patterns 1981-1971, Geophys. Mem., vol. 116, p. 85, HMSO, London.
    • Maraun, D., et al. (2010), Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.
    • Meehl, G. A., et al. (2007), Global climate projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, pp. 747-845, Cambridge Univ. Press, U. K.
    • Michelangeli, P.-A., M. Vrac, and H. Loukos (2009), Probabilistic downscaling approaches: Application to wind cumulative distribution functions, Geophys. Res. Lett., 36, L11708, doi:10.1029/2009GL038401.
    • Pinto, J. G., C. P. Neuhaus, A. Krüger, and M. Kerschgens (2009), Assessment of the wind gust estimates method in mesoscale modelling of storm events over West Germany, Meteorol. Z., 18, 495-506.
    • Pinto, J. G., C. P. Neuhaus, G. C. Leckebusch, M. Reyers, and M. Kerschgens (2010), Estimation of wind storm impacts over West Germany under future climate conditions using a statistical-dynamical downscaling approach, Tellus A, 62, 188-201.
    • Pinto, J. G., M. K. Karremann, K. Born, P. M. Della-Marta, and M. Klawa (2012), Loss potentials associated with European windstorms under future climate conditions, Clim. Res., 54, 1-20.
    • Pryor, S. C., and R. J. Barthelmie (2010), Climate change impacts on wind energy: A review, Renew. Sust. Energy Rev., 14, 430-437.
    • Pryor, S. C., J. T. Schoof, and R. J. Barthelmie (2005), Empirical downscaling of wind speed probability distributions, J. Geophys. Res., 110, D19109, doi:10.1029/2005JD005899.
    • Rockel, B., A. Will, and A. Hense (2008), The regional climate model COSMO-CLM (CCLM), Meteorol. Z., 17, 347-348.
    • Schulz, J. P. (2008), Revision of the turbulent gust diagnostics in the COSMO model, COSMO Newsl., 8, 17-22.
    • Thorarinsdottir, T. L., and T. Gneiting (2010), Probabilistic forecasts of wind speed: Ensemble Model Output Statistics by using heteroscedastic censored regression, J. R. Statist. Soc. Series A, 173, 371-388.
    • Trigo, R. M., and C. C. DaCamara (2000), Circulation weather types and their influence on the precipitation regime in Portugal, Int. J. Climatol., 20, 1559-1581.
    • Verkaik, J. W. (2000), Evaluation of two gustiness models for exposure correction calculations, J. Appl. Meteorol., 39, 1613-1626.
    • Wieringa, J. (1973), Gust factors over open water and built-up country, Boundary Layer Meteorol., 3, 424-441.
    • Wilby, R. L., and T. M. L. Wigley (1997), Downscaling general circulation model output: A review of methods and limitations, Prog. Phys. Geogr., 21, 530-548.
    • Wilby, R. L., T. M. L. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, and D. S. Wilks (1998), Statistical downscaling of GCM output: A comparison of methods, Water Resour. Res., 34, 2995-3008.
    • World Meteorological Organization (WMO) (2008), Guide to meteorological instruments and methods of observation, WMO-No. 8, WMO, Geneva, Switzerland.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article