Information - NP5.03 Model Error: Dynamics, correction and modelling
Event Information
The atmosphere and climate involve a number of nonlinear processes evolving on a wide range of space and time scales. Modelling their evolution implies the use of inherent simplifications (e.g. parameterizations) for processes that are believed to play a secondary role. These approximations are in turn a source of uncertainty that could affect the dynamical properties of the system under investigation. Modelling is even further complicated when these parameters are time dependent like in climate prediction (e.g. CO2 increase).
Nowadays, there is an increasing interest in investigating the dynamical properties of these inherent model errors, as well as in developing techniques that can improve the quality of the forecasts (both deterministic and probabilistic). One can distinguish two types of techniques: (i) Post processing techniques that allow to correct a posteriori the model forecasts like Model Output Statistics (MOS), Ensemble MOS (EMOS), Neural Networks etc, and (ii) the stochastic physics that could provide a better representation of the sub-grid scale processes.
In this session, we invite papers analyzing the sensitivity (and predictability) of atmospheric and climate simulations to model parameterizations and parameter variations (structural stability, model error dynamics…) in low-order, intermediate order or operational systems. We also invite papers dealing with forecasts corrections based on post-processing techniques or built-in stochastic schemes. Both theoretical and practical studies are welcome.
Preliminary List of Solicited Speakers
Co-Sponsorship
WE4
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