Name: Predictability + School on Data Assimilation
Start: 2011-04-26T09:00:00+02:00
End: 2011-05-27T19:00:00+02:00
Location: Nordita

Monday, April 25, 2011
Tuesday, April 26, 2011
Wednesday, April 27, 2011
Thursday, April 28, 2011
Friday, April 29, 2011
Saturday, April 30, 2011
Sunday, May 1, 2011

Monday, May 2, 2011
10:30 AM Adaptive Sampling: theory and applications - Sharan Majumdar
Adaptive Sampling: theory and applications
- Sharan Majumdar
10:30 AM - 11:15 AM
Room: 132:028 (assumes knowledge of EnKFs and the basic concept of adjoints)
3:30 PM Data assimilation of the solar cycle - Laurene Jouve Mausumi Dikpati A. Sacha Brun
Data assimilation of the solar cycle
- Laurene Jouve
- Mausumi Dikpati
- A. Sacha Brun
3:30 PM - 4:30 PM
Room: 132:028
Tuesday, May 3, 2011
10:30 AM Things they didn't tell you last week: DA in Maths, Physics, and Decision Support - Leonard Smith
Things they didn't tell you last week: DA in Maths, Physics, and Decision Support
- Leonard Smith
10:30 AM - 10:50 AM
Room: 132:028
2:00 PM Timescales and (in)predictability. - Yukio KANEDA Linus MAGNUSSON Olivier Talagrand
Timescales and (in)predictability.
- Yukio KANEDA
- Linus MAGNUSSON
- Olivier Talagrand
2:00 PM - 3:00 PM
Room: 132:028 TALAGRAND: About timescales for assimilation. KANEDA: On the limits of predicting turbulence. MAGNUSSON: On estimates of predictability.
Wednesday, May 4, 2011
1:30 PM Hurricane Predictability - Sharan MAJUMDAR
Hurricane Predictability
- Sharan MAJUMDAR
1:30 PM - 1:50 PM
Room: 132:028 MAJUMDAR: Hurricane Predictability
Thursday, May 5, 2011
10:30 AM Data assimilation with uncertain static parameters; or Why it is hard to calibrate dynamical models. - Jonathan ROUGIER
Data assimilation with uncertain static parameters; or Why it is hard to calibrate dynamical models.
- Jonathan ROUGIER
10:30 AM - 10:50 AM
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.
Friday, May 6, 2011
11:15 AM Why are there zonal flows on Jupiter - Jonas Nycander (Dept. of Meteorology, Stockholm University)
Why are there zonal flows on Jupiter
- Jonas Nycander (Dept. of Meteorology, Stockholm University)
11:15 AM - 12:05 PM
Room: 132:028
Saturday, May 7, 2011
Sunday, May 8, 2011