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

A M Fowler,J A Waller,D Simonin

doi:10.1029/2020gl091110

A M Fowler, J A Waller + Show 1 more

Open Access

https://doi.org/10.1029/2020gl091110

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Abstract

AbstractIn 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.

Highlights

The Met Office's UK Variable-resolution model (UKV) system (Tang et al, 2013) aims to provide detailed short-range forecasts over the UK region
In numerical weather prediction it is important to objectively measure the value of the observations assimilated. 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 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

Summary

Introduction

The Met Office's UK Variable-resolution model (UKV) system (Tang et al, 2013) aims to provide detailed short-range forecasts over the UK region. If an ensemble is available, the problem of a lack of suitable adjoint can be addressed by the ensemble FSOI (EFSOI) proposed by Kalnay et al (2012) This has been applied to the Deutscher Wetterdienst's (DWD) convective-scale limited area forecasting model COSMO (Consortium for Small-scale Modelling) in combination with the localized ensemble transform Kalman filter KENDA (Kilometre-scale Ensemble Data Assimilation) by Sommer and Weissmann (2016) and subsequently Necker et al (2018), the problem of needing to validate against observations remains. In an optimal system (i.e., the statistics of the data uncertainty are correctly specified, and the observation operator is close to linear) the total degrees of freedom is given by E[J(xa)] = p (the number of observations) (Bennett et al, 1993; Talagrand, 1999), and the OI is equivalent to the degrees of freedom for signal. OI saturating as the number of observations is increased could be a useful indicator of the amount of redundancy in the observations; in a suboptimal system this may be a misleading interpretation

Theoretical Observation Influence

Actual Observation Influence

Application to DRWs in the UKV

Summary and Conclusions