J11.1
The indistinguishable states approach to probabilistic forecasting
Kevin Judd, UCAR/NRL/University of Western Australia, Perth, WA, Australia; and L. Smith
Even with a perfect model there exist states that are indistinguishable given any amount of previous observations if there is any error in the observations. These indistinguishable states are easily computed given a shadowing trajectory. The concept easily generalizes to situations with imperfect models. The ideal ensemble for probabilistic forecasting is a random sample of indistinguishable states. The principles are illustrated using the Navy Operational Global Atmosphere System NOGAPS. We may also discuss targeted observations from a indistinguishable states perspective. .
Joint Session 11, Probabilistic Forecasting/Ensembles: Part IV (Joint between the Symposium on Forecasting the Weather and Climate of the Atmosphere and Ocean and the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction) (ROOM 6A)
Wednesday, 14 January 2004, 4:00 PM-5:30 PM, Room 6A
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