Remote sensing observations often have correlated errors, but the correlations are typically ignored in data assimilation for numerical weather prediction. The assumption of zero correlations is often used with data thinning methods, resulting in a loss of information, and reduction in analysis accuracy. As operational centres move towards higher-resolution forecasting, there is a requirement to retain data providing detail on appropriate scales. Thus an alternative approach to dealing with observation error correlations is needed. A popular diagnostic for estimating observation error correlations makes use of statistical averages of background and analysis innovations. In this talk we will discuss new mathematical results giving a theoretical understanding of the diagnostic, and also give some examples of how we have used the diagnostic with the UK Met Office operational system to diagnose spatial and interchannel error correlations for radar and SEVIRI data.
This talk was given on 3-6-15.