Articles | Volume 11, issue 2
https://doi.org/10.5194/ascmo-11-229-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
27 Nov 2025
| 27 Nov 2025
Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model
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Cited articles
Apputhurai, P. and Stephenson, A. G.: Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields, Environ. Ecol. Stat., 20, 667–677, https://doi.org/10.1007/s10651-013-0240-9, 2013. a, b
Atger, F.: Estimation of the reliability of ensemble-based probabilistic forecasts, Q. J. Roy. Meteor. Soc., 130, 627–646, https://doi.org/10.1256/qj.03.23, 2004. a
Ayer, M., Brunk, H. D., Ewing, G. M., Reid, W. T., and Silverman, E.: An empirical distribution function for sampling with incomplete information, Ann. Math. Stat., 26, 641–647, https://doi.org/10.1214/aoms/1177728423, 1955. a
Banerjee, S., Carlin, B. P., and Gelfand, A. E.: Hierarchical modeling and analysis for spatial data, Chapman and Hall/CRC, New York, ISBN 9780429205231, https://doi.org/10.1201/9780203487808, 2003. a
Baran, S. and Lakatos, M.: Clustering-based spatial interpolation of parametric postprocessing models, Weather Forecast., 39, 1591–1604, https://doi.org/10.1175/WAF-D-24-0016.1, 2024. a
