Convenors - Weijia Kuang, Craig Bishop
Co-convenor – Andrew Moore, Nick Iakovlev, Wansuo Duan, Alexandre Fournier and Daniel Lathrop
The fields of Earth system data assimilation and ensemble forecasting are confronted with both new and long standing challenges in probabilistic state estimation: (i) the identification and representation of systematic and stochastic aspects of model error; (ii) coupled models; (iii) non-Gaussian uncertainty distributions; (iv) ensemble forecast verification and postprocessing; (v) the use of multi-model and/or multi-resolution ensembles; (vi) achieving balanced initial states that are free of artificial transient waves (or oscillations) ; (vii) the use of analogue observation system simulation experiments with both numerical models and/or laboratory systems to validate and improve real state estimation schemes, and (viii) strategies for dealing with non-linear observation operators in Ensemble Kalman filters. This symposium will bring together atmospheric, oceanic, and solid Earth data assimilation and ensemble forecasting experts to jointly address the aforementioned challenges and create an exchange of ideas likely to advance Earth systemstate estimation across its many facets. Papers are invited on all aspects of data assimilation and ensemble forecasting for the ocean, atmosphere and solid Earth. Presentations pertaining to coupled model forecasting and coupled model data assimilation are particularly encouraged.
We also welcome papers aimed at increasing understanding of the fundamental limits of predictability. Such papers could include: analyses of the relevant initial error dynamics and model error physics, ideas for estimating error growth that limits predictability, methods that attempt to quantify the predictability of specific phenomena in the solid Earth, ocean or atmosphere.