Data Assimilation Meetings at Reading

Date Meeting type Speakers
20 May 2015
Maths 314
Internal speaker Joanne Waller (University of Reading)
Diagnosing spatial and inter channel observation error statistics for Doppler radar radial wind and SEVIRI observations (pdf)
With the development of convection permitting numerical weather prediction the efficient use of high resolution observations, such as Doppler radar radial winds and SEVIRI radiances, in data assimilation is becoming increasingly important. These observations are now routinely assimilated in operational systems, though to avoid violating the assumption of uncorrelated observation errors it is necessary to reduce the density of the observations. This is achieved both by the use of superobservations and observation thinning. Taking into account the full, potentially correlated, error statistics will allow the quantity of observations used to be increased and may improve the impact that the observations have in the assimilation. In this work we use a diagnostic that makes use of statistical averages of background and analysis innovations to calculate observation error statistics. Spatial error covariances are calculated for the Doppler radar radial winds and spatial and inter-channel error statistics for SEVIRI radiances that are assimilated into the Met Office 1.5km model.

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