Probabilistic predictions: interpretation, consistency check, and skill scores

  • Holger Kantz (Max-Planck-Institut für Physik komplexer Systeme, Dresden)
A3 01 (Sophus-Lie room)


Inherent uncertainties in models and initial conditions of complex systems render deterministic predictions useless - they are most certainly wrong. A fair assessment of the uncertainty of the future given the (lack of) knowledge of a complex system and its current state is possible by probabilistic predictions. The forecast product is a probability distribution which is supposed to characterize our knowledge about the future. Evidently, in practice also such predicted probabilities suffer from inaccuracies, i.e., they have to be validated by forecast/observation pairs. In this talk, I will use the example of temperature forecasts to illustrate the concept, methods for verification, and difficulties of this approach.

11.02.02 22.04.20

Complex Systems Seminar

MPI for Mathematics in the Sciences Live Stream

Katharina Matschke

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