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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/npg-2019-55
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-2019-55
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 30 Oct 2019

Submitted as: research article | 30 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).

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

Nina Schuhen Nina Schuhen
  • Norwegian Computing Center, P.O. Box 114 Blindern, 0314 Oslo, Norway

Abstract. With numerical weather prediction ensembles unable to produce sufficiently calibrated forecasts, statistical post-processing is needed to correct deterministic and probabilistic biases. Over the past decades, a number of methods addressing this issue have been proposed, with ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) among the most popular. They are able to produce skillful deterministic and probabilistic forecasts for a wide range of applications. These methods are usually applied to the newest model run as soon as it finished, before the entire forecast trajectory is issued. RAFT (rapid adjustment of forecast trajectories), a recently proposed novel approach, aims to improve these forecasts even further, utilizing the error correlation patterns between lead times. As soon as the first forecasts are verified, we start updating the remainder of the trajectory based on the newly gathered error information. As RAFT works particularly well in conjunction with other post-processing methods like EMOS and techniques designed to reconstruct the multivariate dependency structure like ensemble copula coupling (ECC), we look to identify the optimal combination of these methods. In our study, we apply multi-stage post-processing to wind speed forecasts from the UK Met Office's convective-scale MOGREPS-UK ensemble and analyze results for short-range forecasts at a number of sites in the UK and the Republic of Ireland.

Nina Schuhen
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Status: open (until 25 Dec 2019)
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Nina Schuhen
Nina Schuhen
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Latest update: 12 Nov 2019
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Short summary
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 about 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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