English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

Skill of Data-based Predictions versus Dynamical Models: A Case Study on Extreme Temperature Anomalies

MPS-Authors
/persons/resource/persons184960

Siegert,  Stefan
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

/persons/resource/persons184377

Bröcker,  Jochen
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

/persons/resource/persons145742

Kantz,  Holger
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

Fulltext (public)

1312.4323.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Siegert, S., Bröcker, J., & Kantz, H. (2015). Skill of Data-based Predictions versus Dynamical Models: A Case Study on Extreme Temperature Anomalies. In M. Chavez, M. Ghil, & J. Urrutia‐Fucugauchi (Eds.), Extreme Events: Observations, Modeling, and Economics (pp. 35-50). Somerset: Wiley-Blackwell. doi:10.1002/9781119157052.ch4.


Cite as: http://hdl.handle.net/21.11116/0000-0001-6359-C
Abstract
This chapter considers extreme events as short‐lived large deviations from a system's normal state. It compares the probabilistic predictions of extreme temperature anomalies issued by two different forecast schemes. One is a dynamical physical weather model, and the other is a simple data model. These two types of predictions are evaluated by proper skill scores and receiver operating characteristic (ROC) analysis, respectively. The chapter considers events as being extreme whenever they are in the uppermost or lowermost range of values for a given quantity. It discusses below extreme temperature anomalies, that is, large deviations of the surface temperature from its climatological average for the corresponding day of the year, which are to a good approximation Gaussian distributed. The chapter considers the performance of predictors for the temperature anomaly to overcome a given threshold on the following day for all possible threshold values. The target is the prediction of weather extremes.