ABSTRACT:The Fractions Skill Score (FSS) is a spatial verification metric routinely computed in the operational verification suite. It enables the comparison of forecasts of different resolutions against a common spatial truth (radar rainfall analyses) in such a way that high-resolution forecasts are not penalized for representativeness errors that arise from the 'double penalty' problem. Officially Met Office model precipitation forecast accuracy is monitored using the Equitable Threat Score (ETS) at gauge locations. These precipitation scores form part of a basket of measures assessing six surface parameters known as the UK index, which forms the basis for making decisions regarding model upgrades (especially over the UK). It is used to monitor the impact of continuous model improvements. This framework and the methodology underlying it, is less appropriate for high-resolution forecasts for reasons as described above. For precipitation forecasts in particular, a new framework for long-term monitoring is necessary and the FSS provides such a potential framework.This paper provides an objective critique of FSS results to date. It has been shown that the 'convection-permitting' (4 km) Unified Model (MetUM) forecasts are better than the 12 km MetUM (significant at the 5% level). The scale at which the models have sufficient practical skill is typically 10 km better for the high-resolution forecasts, and are better at forecasting afternoon convection exceeding 4 mm (6 h) −1 . The use of frequency (percentile) thresholds is recommended because of the implicit bias removal this approach provides, as any rain in a forecast period is treated as 'the event of interest'.

An improved stochastic kinetic energy backscatter scheme, version 2 (SKEB2) has been developed for the Met Office Global and Regional Ensemble Prediction System (MOGREPS). Wind increments at each model time step are derived from a streamfunction forcing pattern that is modulated by a locally diagnosed field of likely energy loss due to numerical smoothing and unrepresented convective sources of kinetic energy near the grid scale. The scheme has a positive impact on the root-mean-square error of the ensemble mean and spread of the ensemble. An improved growth rate of spread results in a better match with ensemble-mean forecast error at all forecast lead times, with a corresponding improvement in probabilistic forecast skill from a more realistic representation of model error. Other examples of positive impact include improved forecast blocking frequency and reduced forecast jumpiness. The paper describes the formulation of the SKEB2 and its assessment in various experiments.

The Met Office Global and Regional Ensemble Prediction System–Global (MOGREPS‐G) used an ensemble transform Kalman filter (ETKF) to perturb its initial conditions from its operational implementation in September 2008 until December 2019. In 2019, MOGREPS‐G became the first operational atmospheric ensemble to apply hybrid four‐dimensional ensemble variational data assimilation (En‐4DEnVar) to each of the 44 perturbed ensemble members. Other enhancements have also been added, including to the inflation used to improve ensemble spread. The combined impact of these changes on ensemble forecasts is overwhelmingly positive but initially more neutral for deterministic forecasts, which also use the ensemble to represent flow‐dependent forecast errors in their hybrid data assimilation updates. The latter result is not a surprise, because the deterministic forecast's hybrid data assimilation was initially weighted more strongly to the modelled stationary covariance component and not optimised to take full advantage of the upgraded ensemble. A subsequent operational upgrade in December 2020 has introduced shifting in addition to lagging to exploit the ensemble better in the deterministic forecast's hybrid data assimilation by including ensemble members from a previous cycle and also from adjacent forecast lead times to augment the ensemble without having to run additional forecasts. More weight has since been given to the ensemble in the deterministic forecast's hybrid data assimilation in May 2022. A key motive for adopting hybrid 4DEnVar in MOGREPS‐G is to reduce maintenance overheads by virtue of sharing much of the deterministic forecast system's data assimilation code. This also enables the ensemble to assimilate almost all observation types used by the deterministic forecast. The updated system also exploits parallelism better so as to be fast enough for operational use, despite assimilating more observations and being more computationally expensive than the Met Office's ETKF.