EGU General Assembly 2007
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Nonlinear Processes in Geosciences
Nonlinear Processes:in Geosciences
NP2 Empirical Modeling
NP3 Scale, Scaling and Nonlinear Variability
NP4 Nonlineaar time-series analysis
NP5 Predictability
NP6 Turbulence and Nonlinear Waves
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  List of Accepted Contributions - NP5.01 Quantifying predictability

Please, click Abstract Number to find the corresponding abstract as PDF file; if necessary, download Adobe Acrobat Reader 4.0 first to open the file. Any abstract may be freely reproduced for non-commercial, scientific purposes; however, the moral right of the author(s) to be identified as the author(s) of such abstracts is asserted.

Young, R.; Read, P. L.
Intrinsic predictability measures of baroclinic chaos and quasi-periodic flow in the rotating annulus

Ruessink, B.G.
Predictability Experiments of Nearshore Bathymetry using a Process-based Numerical Model

Rivière, O.; Lapeyre, G.; Talagrand, O.
Nonlinear moist sensitivity of baroclinic systems

Beretta, G.P.; Felletti, F.
Boulders expectation in glacial till tunneling: a transition probability geostatistical approach.

Vannitsem, S.; Nicolis, C.
Dynamical properties of model output statistics forecasts

Rabier, F; Gauthier, P; Langland, R
Objectives of the THORPEX working group on data assimilation and observing strategies for high impact weather forecast improvements

Hallerberg, S.; Kantz, H.
When are extreme events the better predictable, the more extreme they are?

Hachay, O.
A new method for estimation of the stability station of rock massive by their outworking in deep mines.

Macor, J.; Schertzer, D.; Lovejoy, S.
Multifractal predictability of short-time forecast

Andrianova, A.; Binter, R.; Smith, L.A.
Benchmarks for Weather Forecasts in the medium range and beyond.

Smith, L.A.; Du, H.; Binter, R.; Broecker, J.; Clarke, L.
A framework for investigating: "How large should an ensemble be?"

Machete, R. L.; Broecker, J. ; Kilminster, D.; Smith, L. A.; Moroz, I. M.
Quantifying Predictability using Multiple Ensembles Models under different Models: Limitations on the value of Probabilitic Forecasting

Binter, R.; Broecker, J.; Penzer, J.; Smith, L.A.
Contrasting methods of ensemble interpretation

Doblas-Reyes, F. J.; Weisheimer, A.; Berner, J.; Palmer, T. N.
Model error reduction in ensemble seasonal predictions with stochastic parametrizations

Martín, A.; Homar, V.; Fita, LL.; Gutiérrez, J.M.; Rodríguez, M.A.; Primo, C.
Geometric vs classical breding of vectors: Application to hazardous weather in the Western Mediterranean

Broecker, J.; Smith, L. A.
On the relative value of a High Resolution Forecast in an Ensemble Prediction System

Broecker, J.; Smith, L. A.
Scoring Probabilistic Forecasts: The Importance of Being Proper

Broecker, J.; Smith, L. A.
Increasing the Reliability of Reliability Diagrams

Ghil, M.; Chekroun, M.; Simonnet, E.
Robust estimates of climate change and the generalization of structural stability

Ngan, K.; Bartello, P.; Straub, D.N.
Predictability of rotating stratified turbulence

Reynolds, C.; Teixeira, J.; McLay, J.; Bishop, C.
Stochastic parameterizations: Impact on short-term perturbation growth and ensemble prediction.

Hou, D.; Toth, Z.
A stochastic perturbation scheme for representing model related uncertainty in ensemble forecasting

Son, J.-H.; Hou, D.; Toth, Z.
An anaysis of different bias-correction algorithms in a synthetic environment

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