1. Some template text and images for presentations on the Advanced Data Assimilation Methods project Bethan Harris Assimila Ltd

2. Project Summary This initiative contains tasks in three different fields which will advance data assimilation methods for large and complex geophysical datasets. Advanced Data Assimilation Methods

3. Project Overview The first task investigates new methods of performing data assimilation in a non-linear and non-Gaussian framework, in order to increase the accuracy of forecasts of geophysical systems. Traditional data-assimilation methods rely on linearity and normality, while we know that geophysical models tend to be highly nonlinear, and also the relation between the model variables and observations can be nonlinear. Since we have little knowledge of nonlinear data assimilation in the very high-dimensional geophysical systems, the task is necessarily investigating entirely new approaches to the problem.

4. Project Overview continued The second task investigates the impacts of the description of model and observation errors on the solution to the data-assimilation problem. Specific work will focus on representativity errors in simple models, and on nonlinear measures of observation impact. We will also work on the determination of model errors by analysing ensemble forecasts of severe convective events.

5. Project Overview continued The third task will combine the knowledge gained from the first two tasks along with a new physical radiation transfer model in order to build an advanced scheme for retrieving snow values. These retrievals – which are a long-standing problem in data assimilation, hampered by extreme non-linearity and relatively infrequent observations – hold the potential to shed new light on environmental changes in the last 30 years.

6. Wordle of project summary

7. Wordle of project proposal

8. Project Structure Overview Advanced Data Assimilation Methods Task 1 Data-assimilation methods for nonlinear non-Gaussian and multi-scale problems Reading Task 2 Quantifying and representing uncertainty in models and observations at multiple scales Reading Task 3 Exploration of advanced data-assimilation schemes to retrieve new snow products Reading Task 4 Outreach Assimila Task 6 Management Assimila WP 1.5 (Reading) Methods to understand and treat model bias WP 1.4 (Reading) Variational DA experiments in the idealised coupled system WP1.1 (Reading) Improving nonlinear non- Gaussian data assimilation methods using particle filters WP1.2 (Reading) Testing the new particle filters on high-dimensional systems with large data sets WP1.3 (Warwick) Improving nonlinear non- Gaussian data-assimilation methods using MCMC WP 2.5 (Reading) Assess the impact of assumed and real error structures in observations in data assimilation WP 2.4 (Reading) Develop methods to infer the influence of satellite observations on data- assimilation problems WP2.1 (Reading) Perform (ensemble) experiments to quantify model errors WP2.2 (Reading) Assess sensitivity of the errors (eg covariance spread) to experimental setup WP2.3 (Reading) Carry out ensembles of model runs to unravel initial and model equation errors WP3.1 (Reading) Develop a physically-based model connecting grain-size distribution and outgoing micro-wave radiation WP3.2 (Reading) Develop a methodology for assimilation of microwave measurements for snow mass retrieval WP 4.5 (Assimila) Workshops and events WP 4.4 (Assimila) Training WP4.1 (Assimila) Planning and coordination WP4.2 (Assimila) WWW site WP4.3 (Assimila) Promotional material WP5.1 (Assimila) Project management WP5.2 (Reading) Scientific management

9. Design elements ESA Fonts available on request from bethan.harris@assimila.eu

10. Images – “Data” Source: http://pixabay.com/en/binary-code-binary-binary-system-63529 / Source: http://www.fotopedia.com/items/flickr-2665444580http://www.fotopedia.com/items/flickr-2665444580 Required reference: “photo by widdowquinn on Flickr” Source: http://pixabay.com/en/ball-http-www-crash-administrator-63527/ Source: http://pixabay.com/en/black-business-computer-computing-18295/

11. Images – “Observations” Source: http://www.fotopedia.com/items/flickr-6648960719http://www.fotopedia.com/items/flickr-6648960719 Required Reference: “photo by NASA Goddard Photo and Video on Flickr” Source: http://www.fotopedia.com/items/flickr-6818408270 Required reference: “photo by NASA's Marshall Space Flight Center on Flickr” Source: http://www.geograph.org.uk/photo/944298http://www.geograph.org.uk/photo/944298 Required Reference: “Copyright Thomas Nurgent (www.geograph.org.uk/profile/48267)” Source: http://www.fotopedia.com/items/flickr-6875674067http://www.fotopedia.com/items/flickr-6875674067 Required Reference: “photo by NASA Goddard Photo and Video on Flickr”

12. Images – “Models” Source: http://www.fotopedia.com/items/flickr-3334947628 Required Reference: “photo by Argonne National Laboratory on Flickr” Required reference: “International Space Innovation Centre” Source: http://de.fotopedia.com/items/flickr-2656065581http://de.fotopedia.com/items/flickr-2656065581 Required Reference: “photo by Erica_Marshall on Flickr” Source: http://www.flickr.com/photos/clintjcl/5552929311/in/photostream/http://www.flickr.com/photos/clintjcl/5552929311/in/photostream/ Required Reference: “photo by Rev. Xanatos Satanicos Bombasticos on Flickr”