1. © Crown copyright Met Office Breakout 2 How can nonlinear PDE work be exploited to improve the long-term accuracy of weather forecast models? Exeter Workshop 1 st April 2009

2. © Crown copyright Met Office Topics Discussed The following topics (amongst others) were discussed: Where is the bottleneck? Dynamics Physics Data assimilation Hierarchy of models

3. © Crown copyright Met Office Where is the bottleneck? Model = resolved (dry dynamics) + unresolved (physics) + data assimilation (reality) Which is holding us back?

4. © Crown copyright Met Office Dynamics

5. © Crown copyright Met Office Dynamics Discrete conservation properties: Conserve mass, unavailable energy, … Dissipate enstrophy as well? Structures as well as TKE spectra: AMR would help.

6. © Crown copyright Met Office Dynamics: Use of SG etc Theoretical results available: eg. long-range predictability SG informed design of UM dynamical core

7. © Crown copyright Met Office Physics

8. © Crown copyright Met Office Physics: Coupling to Dynamics Physics often not expressed as PDEs! Turbulence modelling approach to convection. Grid dependence necessitates extensive retuning when changing resolution. Bad news for AMR. No-man’s land of intermediate resolution. Incorporate filter scale in well-defined way. No spectral gap: could have several filter scales. Believable and unbelievable scales. Do physics at believable scales (Lander & Hoskins).

9. © Crown copyright Met Office Physics: Coupling to Dynamics ( contd ) Parametrization serves two roles: Represents subgrid physics and corrects resolved dynamics (BL winds, spurious over- turning)

10. © Crown copyright Met Office Physics: Approaches to Parametrization Do physics on a finer grid: ECMWF found this beneficial Superparametrization: a CRM in each GCM grid-box Amalgamate parametrizations for smoother physics: eg. BL+shallow convection Automatic parameter estimation? Avoid pitfall of compensating errors.

11. © Crown copyright Met Office Physics: Going Stochastic Could make deterministic forecast more robust, as well as improving statistics. But forecast error may go up as well as down. Mimics physical upscale transfer of energy. Not just a correction for model dissipation.

12. © Crown copyright Met Office Physics: Ensembles Pay-off to be found between resolution and size of ensemble. Climate people well aware of this! What are limits of predictability? Change of resolution affects forcings (eg. orography) as well as truncation – difficult to untangle. An optimistic note: Smaller scales could be controlled by larger scales – better predictability.

13. © Crown copyright Met Office Assimilation & Models

14. © Crown copyright Met Office Data Assimilation Puts information into model on pre-determined spatial scale. Could physics be made to do likewise? Could physics parameters be determined by data assimilation? Time-dependent physics parameters. Lack of smoothness is a problem! All models are wrong: Could use nudging to enforce balance. Use already made of PV inversion: pseudo-obs.

15. © Crown copyright Met Office Use of Models Hierarchy of models may be used to improve understanding of phenomena (eg. MJO) Beware that phenomena may be complex – not captured by simple ODE models. Could better use of observations be made to improve models? Parametrizations are developed with close attention to obs data. Are we resolving the important scales? Might just have to wait for bigger computers…

16. © Crown copyright Met Office Questions and Comments