Measuring Theoretical and Actual Observation Influence in the Met Office UKV: Application to Doppler Radial Winds

• Published in: Geophysical research letters 2020-12, Vol.47 (24), p.n/a
• Main Authors: Fowler, A. M., Simonin, D., Waller, J. A.
• Format: Article
• Language: English
• Subjects: Atmospheric models; convective‐scale forecasting; Data assimilation; Data collection; Doppler sonar; Instruments; Mathematical models; Measuring instruments; Numerical models; Numerical weather forecasting; Optimization; Resolution; Weather forecasting; Winds
• ISSN: 0094-8276; 1944-8007
• DOI: 10.1029/2020GL091110

In numerical weather prediction it is important to objectively measure the value of the observations assimilated. However, methods such as the forecast sensitivity to observation impact and observing system experiments are difficult to apply to convective scale data assimilation (DA) systems such as the Met Office's UK Variable‐resolution model (UKV). We develop a new method to estimate the influence of the observations on the analysis, acknowledging that the influence depends not only on the uncertainty in the observations and prior, but how well these are prescribed in the assimilation. Monitoring both the actual and theoretical observation influence can flag observations that are being assimilated incorrectly and quantify the harm caused to the analysis. By applying these new estimates of the observation influence to the assimilation of Doppler Radial Winds in the UKV system, we demonstrate their ability, along with expert knowledge, to inform the optimization of both the observation network and DA system. Plain Language Summary When forecasting the weather, it is essential to regularly combine (assimilate) numerical models of the atmosphere with observations to ensure that the forecasts stay in line with reality. At the Met Office ∼45,000 observations coming from a myriad of instruments are used every hour to constrain high‐resolution forecasts over the United Kingdom. Within this work we develop a new method to quantify how valuable different types of observations are for constraining the forecast along with a metric to assess if they are being assimilated correctly. It is demonstrated how the combination of these two metrics can be used to guide changes to the observing network and assimilation system. Key Points The most impactful observations could be the most harmful if they are not assimilated correctly A simple method is developed to estimate simultaneously the influence of the observations and the optimality The metrics are illustrated on the Met Office's UKV assimilation of Doppler Radial Winds