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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 27, issue 1
Nonlin. Processes Geophys., 27, 35–49, 2020
https://doi.org/10.5194/npg-27-35-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Nonlin. Processes Geophys., 27, 35–49, 2020
https://doi.org/10.5194/npg-27-35-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Feb 2020

Research article | 06 Feb 2020

Order of operation for multi-stage post-processing of ensemble wind forecast trajectories

Nina Schuhen

Related subject area

Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere | Techniques: Theory
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Nonlin. Processes Geophys., 27, 307–327, https://doi.org/10.5194/npg-27-307-2020,https://doi.org/10.5194/npg-27-307-2020, 2020
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Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems
Courtney Quinn, Terence J. O'Kane, and Vassili Kitsios
Nonlin. Processes Geophys., 27, 51–74, https://doi.org/10.5194/npg-27-51-2020,https://doi.org/10.5194/npg-27-51-2020, 2020
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Cited articles

Anderson, J. L.: A method for producing and evaluating probabilistic forecasts from ensemble model integrations, J. Climate, 9, 1518–1530, https://doi.org/10.1175/1520-0442(1996)009<1518:amfpae>2.0.co;2, 1996. a
Barnes, C., Brierley, C. M., and Chandler, R. E.: New approaches to postprocessing of multi-model ensemble forecasts, Q. J. Roy. Meteor. Soc., 145, 3479–3498, https://doi.org/10.1002/qj.3632, 2019. a
Ben Bouallègue, Z., Heppelmann, T., Theis, S. E., and Pinson, P.: Generation of scenarios from calibrated ensemble forecasts with a dual-ensemble copula-coupling approach, Mon. Weather Rev., 144, 4737–4750, https://doi.org/10.1175/mwr-d-15-0403.1, 2016. a
Bröcker, J.: Evaluating raw ensembles with the continuous ranked probabilty score, Q. J. Roy. Meteor. Soc., 138, 1611–1617, https://doi.org/10.1002/qj.1891, 2012. a
Buizza, R.: Ensemble forecasting and the need for calibration, in: Statistical postprocessing of ensemble forecasts, edited by: Vannitsem, S., Wilks, D. S., and Messner, J. W., chap. 2, 15–48, Elsevier, Amsterdam, Netherlands, https://doi.org/10.1016/B978-0-12-812372-0.00002-9, 2018. a
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We present a new way to adaptively improve weather forecasts by incorporating last-minute observation information. The recently measured error, together with a statistical model, gives us an indication of the expected future error of wind speed forecasts, which are then adjusted accordingly. This new technique can be especially beneficial for customers in the wind energy industry, where it is important to have reliable short-term forecasts, as well as providers of extreme weather warnings.
We present a new way to adaptively improve weather forecasts by incorporating last-minute...
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