Investigating synoptic- and meso-scale severe weather phenomena using PMAP at sub-kilometer resolution

Understanding and forecasting severe weather events requires atmospheric models that not only reliably predict the circulation on the planetary and synoptic scales but also adequately represent meso- and micro-scale processes. Within extratropical cyclones, for instance, severe surface winds can occur along the bent-back extension of the warm front. In many cases, these are accompanied by a short-lived meso-scale vigorous jet, the so-called sting jet, whose underlying processes are an active subject of research. Much of the cyclone-induced heavy precipitation, in turn, is attributable to the frontal zones outside of the cyclone center. While a grid spacing of a few kilometers is typically sufficient to capture the occurrence of such events, models at higher resolution are needed to pinpoint the most disruptive wind gusts and precipitation peaks.

The Portable Model for multi-scale Atmospheric Prediction (PMAP) is a flexible physics-based framework that supports horizontal grid spacings from a few kilometers down to tens of meters. This Python-based model is currently under active development at ECMWF, ETH Zürich, and CSCS. PMAP solves the non-hydrostatic and fully compressible equations using a bespoke finite-volume, 3D semi-implicit dynamical core coupled to state-of-the-art physical parametrizations. The key strengths of the model are its portability across diverse hardware architectures and its high computational performance, both enabled by the programming implementation with the GridTools for Python (GT4Py) domain-specific library.

To evaluate the model representation of a real weather event, we simulate the passage of cyclone Goretti along the English Channel on 8 January 2026. This storm serves as a prime case study since Goretti induced strong wind gusts over the UK and France associated with a sting jet, as well as disruptive snow accumulation in Northern Germany. Forced by kilometer-scale DestinE forecasts at the lateral boundaries, PMAP is run over a regional domain at hectometer scale including nested large-eddy simulations. We benchmark PMAP using near-surface wind observations in the vicinity of the sting jet at the southern coast of the UK, and radar data covering Northern Germany. Beyond model evaluation, we leverage these simulations to investigate sting jet formation mechanisms and the structure of frontal precipitation at high spatial and temporal resolution.