A pragmatic strategy for implementing spatially correlated observation errors in an operational system: An application to Doppler radial winds

Recent research has shown that high‐resolution observations, such as Doppler radar radial winds, exhibit spatial correlations. High‐resolution observations are routinely assimilated into convection‐permitting numerical weather prediction models assuming their errors are uncorrelated. To avoid violating this assumption, observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of full, correlated, error statistics. Some operational centres have introduced satellite inter‐channel observation‐error correlations and obtained improved analysis accuracy and forecast skill scores. Here we present a strategy for implementing spatially correlated observation errors in an operational system. We then provide the first demonstration of the practical feasibility of incorporating spatially correlated Doppler radial wind error statistics in the Met Office numerical weather prediction system. Inclusion of correlated Doppler radial winds error statistics has little impact on the computation cost of the data assimilation system, even with a fourfold increase in the number of Doppler radial winds observations assimilated. Using the correlated observation‐error statistics with denser observations produces increments with shorter length‐scales than the control. Initial forecast trials show a neutral to positive impact on forecast skill overall, notably for quantitative precipitation forecasts. There is potential to improve forecast skill by optimizing the use of Doppler radial winds and applying the technique to other observation types. Research has shown that Doppler radar radial winds (DRWs) have spatially correlated observation errors; however, DRWs are routinely assimilated into convection‐permitting numerical weather prediction models with a severely reduced density and assuming uncorrelated errors. We develop an approach that enables the introduction of full, correlated, error statistics; consequently observations with higher spatial density can be assimilated. Results show that the use of correlated error statistics has positive impact on the data assimilation solution without detriment to the computation time.