The Met Office winter testbed 2020/2021: Experimenting with an on‐demand 300‐m ensemble in a real‐time environment

Abstract

The Met Office held a testbed over winter 2020/2021 where a new numerical weather prediction (NWP) sub‐km ensemble was set up on‐demand in response to interesting weather phenomena in the United Kingdom. The domain for the model was chosen in real time by a community of Met Office Research Scientists and Operational Meteorologists and over a 4‐month period the ensemble was triggered for nine events. The purpose of the testbed was to investigate whether a real‐time weather regime‐based enhancement in NWP capability was feasible, to understand what benefits a testbed environment might give, and to explore the practicalities of running such an event. Case studies from the testbed demonstrated that forecast ensembles at 2.2 km and 300 m grid spacing were able to capture observed winter weather, with greater spatial detail apparent, especially over complex orography, in the 300‐m model. Ensemble spread appeared less influenced by resolution, potentially due to the size of the domains tested or the weather regimes of the case studies. The testbed also showcased underutilized observations and additional radiosonde ascents were conducted. All the testbed meetings were conducted virtually due to COVID‐19 restrictions, and decisions were made about when to trigger the event using an online message board. The winter 2020/2021 testbed provides ideas for how on‐demand weather‐dependent testbeds might be conducted in the future. However, several recommendations are made that would enhance testbed benefits further, including more dedicated resource, stronger technology and data visualization and greater participation from both academia and weather information users.

Stratification of the vertical spread‐skill relation by radiosonde drift in a convective‐scale ensemble

“…The underspread nature of the ensemble at the tropopause is consistent with both Hanley and Lean (2021) and Bain et al (2022). This is likely related to the model's tendency to under‐represent inversions.…”

“…The first aspect of the spread‐skill relation is often defined through the root mean square difference and error, respectively (e.g., Hopson, 2014). However, as in Bain et al (2022), here the skill is defined as the “ensemble members – observations”, and the spread is defined as the differences between ensemble members for each unique pairing. When differences between the spread and skill are considered the mean absolute difference and mean absolute error are considered.…”

“…Examination of the vertical aspects are particularly useful as the vertical state of the atmosphere is critical for forecasting certain types of weather phenomenon (e.g., convection and precipitation type; Bourgouin, 2000). The vertical state of the atmosphere is regularly considered through observed profiles (e.g., radiosonde ascents) which can be compared against model profiles to examine spread‐skill relations, understand physical processes, and ascertain skill (e.g., Bain et al, 2022; Hanley & Lean, 2021; Schwartz et al, 2014; Woodhams et al, 2022).…”

“…The work presented here is a development of analysis presented in Schwartz et al (2014) and Bain et al (2022). Schwartz et al (2014) considered the spread‐skill relation using radiosonde ascents as the basis for assessing their 15 km ensemble.…”

“…However, the dewpoint values were underspread throughout the troposphere, and wind and temperature were overspread in the mid-upper troposphere. On, the other hand, Bain et al (2022) considered a small selection of cases from the UK Testbed Winter 2020. The differences in the spread-skill relationship between convective and hectometric-scale models was examined.…”