Representing Model Error in the AWFA Joint Mesoscale Ensemble by a Stochastic Kinetic Backscatter Scheme
Judith Berner, NCAR, Boulder, CO; and A. Fournier, S. Y. Ha, J. Hacker, and C. Snyder
A common problem in probabilistic forecasting is that most ensemble
systems tend to be underdispersive, thus leading to overconfident
uncertainty estimates and an underestimation of extreme weather
events. This underdispersivness might arise in part from a
misrepresentation of unresolved subgrid-scale processes and can thus
be remedied by stochastic parameterizations.
Here we present results from a stochastic kinetic backscatter scheme
implemented into the AWFA Joint Mesoscale Ensemble (short-range WRF
ensemble forecasting system).
In comparison to the ensemble system without stochastic parameterization,
the backscatter scheme improves the spread-error-relationship and leads to
better probabilistic skill scores, especially for extreme events.
It is also discussed how the ensemble with backscatter scheme performs
in relation to a multi-physics ensemble, where each ensemble member
uses a combination of different physical parameterizations.
Session 4A, Ensemble Forecasting Part I
Monday, 1 June 2009, 4:00 PM-5:30 PM, Grand Ballroom East
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