Designing future Met Office regional ensemble prediction systems: the impact of initial and later boundary conditions on spread-skill relationship for heavy precipitation forecasts.

Many factors need to be considered when designing limited-area ensemble models to forecast at short spatio-temporal scales. However, since the main motivation to run them is to capture the inherent forecast uncertainty at small spatio-temporal scales, especially for severe weather cases, representing such uncertainty is key for producing skillful probabilistic forecasts. Arguably, initial and lateral boundary conditions are a major source of uncertainty for limited-area ensemble forecasts. Many strategies have been explored to initialise convective-scale ensembles but a configuration that is objectively better than others has not been found yet, whereas lateral boundary conditions are provided by a coarser ensemble. Also, limited attention has been given to the size of domains for limited-area models. A domain that is too small could prevent the nested model to generate finer scale and more physically realistic structures which arise from local surface forcing.

To this end, at the Met Office, we have started to run some tests to better understand the sensitivity of our convective-scale ensembles to the presence of high-resolution information at initialisation as well as to the size of the domain, this as a function of lead time and regime dependence.

• hourly time-lagged ensemble with initial conditions centred around the high-resolution deterministic to mimic the current operational ensemble (MOGREPS-UK), 
• downscaler from a global ensemble on the same domain as the hourly-cycling,
• downscaler from a global ensemble on an increased size domain. 

In all the different designs the ensemble consists of 18 members and runs four times a day. However, in the downscaler all the members are generated from a 6-hourly cycle with initial perturbations coming from the same parent ensemble forecast range, whereas in the hourly-cycling three new members are generated every hour, with five additional sets of initial conditions perturbations and high-resolution deterministic analyses.  

The aim of this study is then to assess the sensitivity of the probabilistic skill of heavy precipitation forecasts to these three different designs, both through a series of case studies and aggregate statistics over a nine months period (November 2022-August 2023), using ad-hoc verification measures, focussing on the spatial spread-skill relationship. By looking at different cases we will also assess whether there is any link between the probabilistic skill and the synoptic weather patterns, in order to make more informed decisions about which ensemble is the most skilful.