70 citations · 102 across the 8 of their papers we have counts for
8 papers
Spatial forecast postprocessing: The Max-and-Smooth approach
Stefan Siegert, Ben Hooper, Joshua Lovegrove +2
Numerical weather forecasts can exhibit systematic errors due to simplifying model assumptions and computational approximations. Statistical postprocessing is a statistical approac…
Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models
Birgir Hrafnkelsson, Stefan Siegert, Raphaël Huser +2
With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayes…
Approximate Bayesian inference for analysis of spatio-temporal flood frequency data
Árni V. Johannesson, Stefan Siegert, Raphaël Huser +2
Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the gener…
Parameter uncertainty in forecast recalibration
Stefan Siegert, Philip G. Sansom, Robin Williams
Ensemble forecasts of weather and climate are subject to systematic biases in the ensemble mean and variance, leading to inaccurate estimates of the forecast mean and variance. To…
A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?
Stefan Siegert, David B. Stephenson, Philip G. Sansom +3
Predictability estimates of ensemble prediction systems are uncertain due to limited numbers of past forecasts and observations. To account for such uncertainty, this paper propose…
Evaluating ensemble forecasts by the Ignorance score -- Correcting the finite-ensemble bias
Stefan Siegert, Christopher A. T. Ferro, David B. Stephenson
This study considers the application of the Ignorance Score (also known as the Logarithmic Score) in the context of ensemble verification. In particular, we consider the case where…
Skill of data based predictions versus dynamical models -- case study on extreme temperature anomalies
Stefan Siegert, Jochen Broecker, Holger Kantz
We compare probabilistic predictions of extreme temperature anomalies issued by two different forecast schemes. One is a dynamical physical weather model, the other a simple data m…
Variance estimation for Brier Score decomposition
Stefan Siegert
The Brier Score is a widely-used criterion to assess the quality of probabilistic predictions of binary events. The expectation value of the Brier Score can be decomposed into the…