Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-105-2024
https://doi.org/10.5194/ascmo-10-105-2024
02 Sep 2024
 | 02 Sep 2024

Parametric model for post-processing visibility ensemble forecasts

Ágnes Baran and Sándor Baran

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Cited articles

Baran, Á and Baran, S.: A two-step machine learning approach to statistical post-processing of weather forecasts for power generation, Q. J. Roy. Meteor. Soc., 150, 1029–1047. https://doi.org/10.1002/qj.4635, 2024. a, b, c
Baran, Á., Lerch, S., El Ayari, M., and Baran, S.: Machine learning for total cloud cover prediction, Neural. Comput. Appl., 33, 2605–2620, https://doi.org/10.1007/s00521-020-05139-4, 2021. a, b
Baran, S. and Baran, Á.: Calibration of wind speed ensemble forecasts for power generation, Időjárás, 125, 609–624, https://doi.org/10.28974/idojaras.2021.4.4, 2021. a
Baran, S. and Lakatos, M.: Statistical post-processing of visibility ensemble forecasts, Meteorol. Appl., 30, e2157, https://doi.org/10.1002/met.2157, 2023. a, b, c, d
Baran, S. and Lerch, S.: Mixture EMOS model for calibrating ensemble forecasts of wind speed, Environmetrics, 27, 116–130, https://doi.org/10.1002/env.2380, 2016. a
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Short summary
The paper proposes a novel parametric model for statistical post-processing of visibility ensemble forecasts; investigates various approaches to parameter estimation; and, using two case studies, provides a detailed comparison with the existing state-of-the-art forecasts. The introduced approach consistently outperforms both the raw ensemble forecasts and the reference parametric post-processing method.
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