Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

2018 ◽  
Vol 54 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Keunhee Han ◽  
JunTae Choi ◽  
Chansoo Kim
2021 ◽  
Vol 53 ◽  
pp. 93-108
Author(s):  
Mailiu Díaz ◽  
Orietta Nicolis ◽  
Julio César Marín ◽  
Sándor Baran

2013 ◽  
Vol 22 (3) ◽  
pp. 273-282 ◽  
Author(s):  
Sándor Baran ◽  
András Horányi ◽  
Dóra Nemoda

2021 ◽  
Author(s):  
Sam Allen ◽  
Gavin Evans ◽  
Piers Buchanan ◽  
Frank Kwasniok

<p>Changes in the North Atlantic Oscillation (NAO) heavily influence the weather across the UK and the rest of Europe. Due to an imperfect reconstruction of the polar jet stream and associated pressure systems, there is reason to believe that errors in numerical weather prediction models may also depend on the prevailing behaviour of the NAO. To address this, information regarding the NAO is incorporated into statistical post-processing methods through a regime-dependent mixture model, which is then applied to wind speed forecasts from the Met Office's global ensemble prediction system, MOGREPS-G. The mixture model offers substantial improvements upon conventional post-processing methods when the wind speed depends strongly on the NAO, but the additional complexity of the model can hinder forecast performance in other instances. A measure of regime-dependency is thus defined that can be used to differentiate between situations when the numerical model output is, and is not, expected to benefit from regime-dependent post-processing. Implementing the regime-dependent mixture model only when this measure exceeds a certain threshold is found to further improve predictive performance, while also producing more accurate forecasts of extreme wind speeds.</p>


2011 ◽  
Vol 20 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Conor P. Sweeney ◽  
Peter Lynch ◽  
Paul Nolan

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 172859-172868
Author(s):  
Zhengwei Ma ◽  
Sensen Guo ◽  
Gang Xu ◽  
Saddam Aziz

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