Articles | Volume 6, issue 1
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 45–60, 2020
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 45–60, 2020
11 May 2020
11 May 2020

Postprocessing ensemble forecasts of vertical temperature profiles

David Schoenach et al.

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Cited articles

Blandford, T. R., Humes, K. S., Harshburger, B. J., Moore, B. C., Walden, V. P., and Ye, H.: Seasonal and Synoptic Variations in Near-Surface Air Temperature Lapse Rates in a Mountainous Basin, J. Appl. Meteorol. Climatol., 47, 249–261,, 2008. a
Dabernig, M., Mayr, G. J., Messner, J. W., and Zeileis, A.: Spatial ensemble post-processing with standardized anomalies, Q. J. Roy. Meteor. Soc., 143, 909–916,, 2017. a, b, c, d, e
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ECMWF: Describing ECMWF’s forecasts and forecasting system, ECMWF Newsletter, 133, 11–13, 2012. a, b
Gebetsberger, M., Messner, J. W., Mayr, G. J., and Zeileis, A.: Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood, Mon. Weather Rev., 146, 4323–4338,, 2018. a
Short summary
State-of-the-art statistical methods are applied to postprocess an ensemble of numerical forecasts for vertical profiles of air temperature. These profiles are important tools in weather forecasting as they show the stratification and the static stability of the atmosphere. Flexible regression models combined with the multi-dimensionality of the data lead to better calibration and representation of uncertainty of the vertical profiles.