Ensemble covariances for coupled atmosphere-ocean data assimilation

Data assimilation is the process of initializing a computer model forecast using the latest observational data. As part of efforts to improve forecasting on all time ranges (including weather, seasonal and decadal timescales) the Met Office are developing a system to initialize the atmosphere and ocean together using observations of both systems. In order to do this properly it is essential
to characterize the errors in the forecast before the observations are used. These can be estimated from a set of forecasts generated from different plausible starting conditions, but in practice it is impossible to perform enough forecasts to calculate the statistics correctly. Methods to take account of this do exist separately for atmosphere and ocean models, but it is not yet clear how to do this for the coupled atmosphere-ocean system. In particular it is necessary to allow for the fact that horizontal spatial scales in the atmosphere are an order of magnitude greater than those in the ocean. In this project we will develop new methods for estimating the errors in coupled atmosphere-ocean forecasts that take account of these different physical scales in the two fluids.

This studentship is a joint project with the Met Office. The student will spend some time working at the Met Office over the lifetime of the project as part of the Met Office Studentship Scheme. The student will also have the opportunity to attend training courses on data assimilation organized by the ECMWF and by the Data Assimilation Research Centre at Reading and advanced training modules on the modelling of the atmosphere and oceans provided under the SCENARIO Doctoral Training Programme.

The project is supervised by Amos Lawless (University of Reading), and co-supervised by Nancy Nichols (University of Reading) and Matthew Martin (Met Office).

The full project description is available at: http://www.met.reading.ac.uk/nercdtp/home/available

A video is also available at https://youtu.be/s3fIPrcldeM