Adaptive Sampling: theory and applications

• Sharan Majumdar

Room: 132:028 (assumes knowledge of EnKFs and the basic concept of adjoints)

Data assimilation of the solar cycle

• A. Sacha Brun
• Laurene Jouve
• Mausumi Dikpati

Room: 132:028

Things they didn't tell you last week: DA in Maths, Physics, and Decision Support

• Leonard Smith

Room: 132:028

Timescales and (in)predictability.

• Yukio KANEDA
• Olivier Talagrand
• Linus MAGNUSSON

Room: 132:028 TALAGRAND: About timescales for assimilation. KANEDA: On the limits of predicting turbulence. MAGNUSSON: On estimates of predictability.

Hurricane Predictability

• Sharan MAJUMDAR

Room: 132:028 MAJUMDAR: Hurricane Predictability

Data assimilation with uncertain static parameters; or Why it is hard to calibrate dynamical models.

• Jonathan ROUGIER

Room: 132:028 ROUGIER: Data assimilation with uncertain static parameters Abstract: Using the link between variational methods and maximum likelihood, I explore in a non-technical way the conditions under which data assimilation produces consistent estimators, and show that with a perfect model these conditions distinguish clearly between learning about the state vector and learning about the static parameters ('calibration'). Interestingly -- perhaps paradoxically -- the situation for calibration improves when the model is imperfect, and correctly modelled as such. These results are only a sketch in response to the discussions we have been having about imperfect models, and the pros and cons of stochastic models.

Why are there zonal flows on Jupiter

• Jonas Nycander (Dept. of Meteorology, Stockholm University)

Room: 132:028

Sequential variational method for data assimilation without tangent linear and adjoint model integrations

• Srdjan DOBRICIC

Room: 132:028 DOBRICIC: Sequential variational method for data assimilation without tangent linear and adjoint model integrations

Does model resolution matter for climate predictions?

• Shuting YANG

Room: 132:028 YANG: Does model resolution matter for climate predictions?

Systematic Strategies for Stochastic Climate Modeling

• Christian Franzke

Room: 132:028 FRANZKE: Systematic Strategies for Stochastic Climate Modeling The climate system has a wide range of temporal and spatial scales for important physical processes. Examples include convective activity with an hourly time scale, organized synoptic scale weather systems on a daily time scale, extra-tropical low-frequency variability on a time scale of 10 days to months, to decadal time scales of the coupled atmosphere-ocean system. An understanding of the processes acting on different spatial and temporal scales is important since all these processes interact with each other due to the nonlinearities in the governing equations. Most of the current problems in understanding and predicting the climate system stem from the multi-scale nature of the climate system in that all of the above processes interact with each other and the neglect and/or misrepresentation of some of the processes lead to systematic biases of the resolved processes and uncertainties in the climate response. A better understanding of the multi-scale nature of the climate system will be crucial in making more accurate and reliable weather and climate predictions. In my presentation I will discuss systematic strategies to derive stochastic models for climate prediction. The stochastic mode reduction strategy accounts systematically for the effect of the unresolved degrees of freedom and predicts the functional form of the effective reduced equations. These procedures extend beyond simple Langevin equations with additive noise by predicting nonlinear effective equations with both additive and multiplicative (state-dependent) noises. The stochastic mode reduction strategy predicts rigorously closed form stochastic models for the slow variables in the limit of infinite separation of time-scales.

Ensembles in Data Assimilation for Numerical Weather Prediction

• Jelena Bojarova

Room: 132:028

The beginnings of data assimilation in geomagnetism

• Andrew Jackson

Room: 132:028

Special Astrophysical Fluids Seminar: Some "Non"s in Turbulence -- Nonlinear, Nonlocal, Nonequilibrium

• Yukio KANEDA

Room: 132:028

spontaneous discussion on practical 3DVAR implementation with some introductory remarks by Srdjan Room: 132:028

A Bayesian Hierarchical Modelling approach to quantify wind forcing uncertainties in ocean ensemble forecasting

• Srdjan DOBRICIC

Room: 132:028 DOBRICIC: A Bayesian Hierarchical Modelling approach to quantify wind forcing uncertainties in ocean ensemble forecasting

Uncertainty in projections of climate change

• Jouni Räisänen

Room: 132:028

Parameter esimation indistinguable states

• Milena Cuellar

Room: 132:028 Cuellar: Parameter esimation indistinguable states

Float trajectory assimilation in the Mediterranean forecasting system

• Jenny Nilsson

Room: 132:028

An operational 3-4D-Var - practical aspects including the hybrid var/ensemble approach and performance

• Nils GUSTAFSSON

Room: 132:028

Sequential estimation as a dynamical systems problem, including parameter estimation

• Michael Ghil

Room: 132:028

Rising carbon dioxide, a never ending story?

• Ralph Keeling

Room: Aula Magna, Stockholm University <a href=http://www.science.su.se/content/1/c6/09/61/19/Poster.pdf>Poster</a>

Impact of stochastic perturbations on deterministic dynamical systems

• Valerio LUCARINI

Room: 132:028 LUCARINI: Impact of stochastic perturbations on deterministic dynamical systems

Informal discussion about solar irradiance variability etc

• Michael Ghil

Room: 132:028

Nowcasting via Gradient Descent: Moving from Theory towards Reality

• Hailiang Du

Room: 132:028

Thermodynamics of the climate system

• Valerio Lucarini

Room: Room C609, 6th floor http://www.misu.su.se/