Evaluating errors due to unresolved scales in convection‐permitting numerical weather prediction

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast verification. The spatial and temporal scales represented by the observation and model may differ and this results in a scale mismatch error which may be biased and correlated. The aim of this paper is to investigate the structure of representation error in convection‐permitting NWP models for four meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high‐resolution data from the experimental Met Office London Model (approximately 300 m grid‐length) to simulate perfect observations and lower‐resolution model data. The scale mismatch error and its bias, variance and correlation are calculated from the perfect observation and low‐resolution model equivalents. Our new results show that the scale mismatch bias is significant in the boundary layer for temperature and specific humidity, whereas the variance is significant in the boundary layer for all analysed variables. Contrary to previous studies using low‐resolution (km‐scale) data, horizontal correlations are shown to be insignificant. However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation. Models and observations are quantitatively compared for the purposes of data assimilation and forecast verification; however, a scale mismatch error arises when these quantities represent different spatial and temporal scales. Using data from the experimental Met Office London Model, run at a resolution of approximately 300 m, we investigate this scale mismatch error and its bias, variance and correlation for four meteorological variables. Our new results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation.