Our goal is to develop the theory of data assimilation, particularly methods to treat uncertainty in the data and models, so that our work underpins other applications within NCEO and our partner agencies
Partners and Customers: Met Office/Hadley Centre, European Centre for Medium Range Weather Forecasting (ECMWF), Defra (Department for Food and Rural Affairs)
EO data come from a wide variety of sources, they are distributed non-uniformly in space and time and all have different kinds of distortions affecting them – from environmental factors to engineering and orbital distortions. Scientists and other users need their data to be a quality controlled, digital rendition of the real world on a regional or global grid for analysis and prediction. Data assimilation – the technique of combining optimally information from models with diverse observations achieves this. It’s a technique at the heart of weather and seasonal to decadal climate forecasting, and one of central importance for a variety of applications within NCEO. We will provide optimal estimates of the evolving state of a system and generate statistics on observation and model mismatches, which enable flaws to be identified in both. We are able to estimate unknown parameters in models so that we can analyse quantitatively how proposed new systems will work before they are built.
Our key challenges are:
