Improving high impact weather forecasts via an international comparison of ObServation error Correlations in data Assimilation (OSCA)

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Approximately 4 million properties in the UK are at risk from surface-water flooding which occurs when heavy rainfall overwhelms the drainage capacity of the local area. In the future, as a result of climate change, the frequency and intensity of severe weather events, such as storms and floods, is likely to increase. Accurate forecasts of severe weather provide significant benefit, allowing households and businesses to take mitigating action and emergency services to mobilize resources. Numerical weather forecasts are obtained by evolving forward the current atmospheric state using computational techniques that solve equations describing atmospheric motions and other physical processes. The current atmospheric state is estimated by a sophisticated mathematical technique known as data assimilation. Data assimilation blends previous forecasts with new atmospheric observations, weighted by their respected uncertainties. The uncertainty in the observations is not well understood, and currently up to 80% of observations are not used in the assimilation because these uncertainties cannnot be properly quantified and accounted for. Working in partnership with the UK Met Office, we have recently demonstrated in the NERC FRANC: Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection project (NE/K008900/1), that it is now feasible to estimate spatial statistics for observation uncertainty. Our previous work in idealized systems has shown that better accounting for these errors in the assimilation is expected to provide significant forecast improvement. There are still a number of fundamental questions to address before the benefits can be realized in operational forecasts. This proposal to the NERC International Opportunities fund will add value to the work carried out in FRANC, by supporting access to international observation data, numerical weather prediction models and assimilation systems. We will build a new collaboration with the Deutscher Wetterdienst (German Weather Service), and compare observation error statistics for Doppler radar wind data from Deutscher Wetterdienst with those from the UK Met Office. By considering the similarities and differences between the operational forecasting systems, and attributing these to features in the observation error statistics, we will obtain a detailed knowledge of the error sources. By carrying out theoretical and idealized studies and comparing their results with the statistics from the operational systems, we will gain understanding of the impact of differences in the assimilation systems on the diagnostic used to estimate the observation error statistics. In turn, this should allow the observation errors to be reduced, and therefore more of the expensively acquired observations to be utilised, rather than discarded. Ultimately, understanding the observation uncertainty will result in improved forecasts of severe weather events.