Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction
By L. Bao, Tilmann Gneiting, E. Grimit, Peter Guttorp, Adrian E. Raftery. 2010
88 citations recorded by the source corpus.
Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/cccf4a0844fa678bd4f9310ce8b7035f06fa09e0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Thordis Linda Thorarinsdottir
- Sebastian Lerch
- Nina Schuhen
- Michael Scheuerer
- Tilmann Gneiting
- Petra Friederichs
- D. Wilks
- W. Ehm
In Environmental Engineering
- Thordis Linda Thorarinsdottir
- Sebastian Lerch
- Nina Schuhen
- Tilmann Gneiting
- Michael Scheuerer
- Marc G. Genton
- Michael L. Stein
- Emilio Porcu
In Global and Planetary Change
- Sebastian Lerch
- Thordis Linda Thorarinsdottir
- Nina Schuhen
- Tilmann Gneiting
- Michael Scheuerer
- Marc G. Genton
- Petra Friederichs
- Michael L. Stein
Citations in the corpus
- Ensemble Model Output Statistics for Wind Vectors (2012, background): “Until very recently, wind speed and wind direction have been addressed individually in statistical postprocessing without taking dependencies into account (Bao et al. 2010; Sloughter et al. 2010; Thorarinsdottir and Gneiting 2010; Thorarinsdottir and Johnson 2012).”
- Comparison of non-homogeneous regression models for probabilistic wind speed forecasting (2013, methodology): “The NR method has been extended to wind gusts (Thorarinsdottir and Johnson, 2012) and a BMA approach for wind direction is proposed in Bao et al. (2010). Pinson (2012), Schuhen et al. (2012) and Sloughter et al. (2013) study statistical postprocessing of bivariate wind vector ensembles. Hourly avera”