Articles | Volume 27, issue 2
Nonlin. Processes Geophys., 27, 239–252, 2020
https://doi.org/10.5194/npg-27-239-2020

Special issue: Advances in post-processing and blending of deterministic...

Nonlin. Processes Geophys., 27, 239–252, 2020
https://doi.org/10.5194/npg-27-239-2020
Research article
23 Apr 2020
Research article | 23 Apr 2020

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

Julian Steinheuer and Petra Friederichs

Related authors

A new scanning scheme and flexible retrieval for mean winds and gusts from Doppler lidar measurements
Julian Steinheuer, Carola Detring, Frank Beyrich, Ulrich Löhnert, Petra Friederichs, and Stephanie Fiedler
Atmos. Meas. Tech., 15, 3243–3260, https://doi.org/10.5194/amt-15-3243-2022,https://doi.org/10.5194/amt-15-3243-2022, 2022
Short summary

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere | Techniques: Theory
A waveform skewness index for measuring time series nonlinearity and its applications to the ENSO–Indian monsoon relationship
Justin Schulte, Frederick Policelli, and Benjamin Zaitchik
Nonlin. Processes Geophys., 29, 1–15, https://doi.org/10.5194/npg-29-1-2022,https://doi.org/10.5194/npg-29-1-2022, 2022
Short summary
Empirical evidence of a fluctuation theorem for the wind mechanical power input into the ocean
Achim Wirth and Bertrand Chapron
Nonlin. Processes Geophys., 28, 371–378, https://doi.org/10.5194/npg-28-371-2021,https://doi.org/10.5194/npg-28-371-2021, 2021
Short summary
Recurrence analysis of extreme event-like data
Abhirup Banerjee, Bedartha Goswami, Yoshito Hirata, Deniz Eroglu, Bruno Merz, Jürgen Kurths, and Norbert Marwan
Nonlin. Processes Geophys., 28, 213–229, https://doi.org/10.5194/npg-28-213-2021,https://doi.org/10.5194/npg-28-213-2021, 2021
Beyond univariate calibration: verifying spatial structure in ensembles of forecast fields
Josh Jacobson, William Kleiber, Michael Scheuerer, and Joseph Bellier
Nonlin. Processes Geophys., 27, 411–427, https://doi.org/10.5194/npg-27-411-2020,https://doi.org/10.5194/npg-27-411-2020, 2020
Short summary
On fluctuating momentum exchange in idealised models of air–sea interaction
Achim Wirth
Nonlin. Processes Geophys., 26, 457–477, https://doi.org/10.5194/npg-26-457-2019,https://doi.org/10.5194/npg-26-457-2019, 2019
Short summary

Cited articles

Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Baran, S. and Lerch, S.: Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting, Q. J. Roy. Meteor. Soc., 141, 2289–2299, https://doi.org/10.1002/qj.2521, 2015. a
Bentzien, S. and Friederichs, P.: Decomposition and graphical portrayal of the quantile score, Q. J. Roy. Meteor. Soc., 140, 1924–1934, https://doi.org/10.1002/qj.2284, 2014. a, b
Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S., and Steinke, S.: Towards a high-resolution regional reanalysis for the European CORDEX domain, Q. J. Roy. Meteor. Soc., 141, 1–15, https://doi.org/10.1002/qj.2486, 2014. a, b, c
Born, K., Ludwig, P., and Pinto, J. G.: Wind gust estimation for Mid-European winter storms: towards a probabilistic view, Tellus A, 64, 17471, https://doi.org/10.3402/tellusa.v64i0.17471, 2012. a
Download
Short summary
Many applications require wind gust estimates at very different atmospheric altitudes, such as in the wind energy sector. However, numerical weather prediction models usually only derive estimates for gusts at 10 m above the land surface. We present a statistical model that gives the hourly peak wind speed. The model is trained based on a weather reanalysis and observations from the Hamburg Weather Mast. Reliable predictions are derived at up to 250 m, even at unobserved intermediate levels.