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DY: Fachverband Dynamik und Statistische Physik

DY 28: Data Analysis Methods and Modelling of Geophysical Systems

DY 28.3: Vortrag

Donnerstag, 29. März 2012, 15:30–15:45, MA 144

Predictability of temperature exceedance events by data-driven and physical-dynamical modeling — •Stefan Siegert and Holger Kantz — Max-Planck-Institut für Physik komplexer Systeme, Dresden, Germany

We present a predictability study of temperature in Hannover, Germany. We issue probabilistic predictions for the event that the value of the temperature exceeds a certain threshold on the next day. The forecast probabilities are generated via two different approaches. In the data-driven approach, an autoregressive model is used to generate the exceedance probability conditional on temperature measurements from the immediate past. In the physical modeling approach, an ensemble of runs generated by an atmospheric circulation model is used. Predictions issued by these two approaches are compared by proper skill scores. A decomposition of the skill scores is used to assess different forecast attributes, namely resolution and reliability. The main conclusion of the study is that the physical modeling approach is superior to the data-driven approach, but only after the model output has been corrected by statistical post-processing.