Increasing lead time for early warnings through dynamically informed forecasting of extreme events and monitoring predictability barriers

The ever-increasing complexity and data volumes of numerical weather prediction demands innovations in the analysis and synthesis of operational forecast data, in particular in the context of warnings of extreme events.

Here we discuss how dynamical thinking can offer directly applicable forecast information for early warnings of extreme events. We present the semi-automated framework “DOMINO” which allows identifying globally, in any variable of the ECMWF ERA5 reanalysis, the robust, and statistically significant dynamical precursor patterns to any type of meteorological event on the meso-α to synoptic scale O(200-2000 km). We call these patterns “event-prone regime” (EPR) and develop a scalar index which allows a very easy monitoring of “EPR activity”. Computing this index for all members of ECMWF’s medium- and extended-range ensemble forecasting system provides a massive simplification of the forecast information. This enables not only early warnings for the potential of an extreme events, but also allows us to derive the physical forecasting storyline for the unfolding of a given potential extreme. This storyline can help identify predictability barriers well before an extreme and assess when the ensemble spread reduces and thus when the forecast scenario becomes more reliable.

We will demonstrate this workflow in case studies in particular for the extreme north Italian flooding of May 2023. We show in ECMWF medium-range forecasts that an EPR perspective was able to identify the growing possibility of the Emilia-Romagna extreme event eight days beforehand – four days earlier than the direct precipitation forecast. Furthermore, we demonstrate that a cyclogenesis near New Foundland posed a predictability barrier. Only once the details about this cyclogenesis verified did all ensemble members converge towards the extreme event.  We conclude that dynamical precursors identified through the respective EPR prove well-suited for monitoring the potential unfolding of an extreme event in the medium-range as well as in the extended-range. In addition, through identifying and interpreting predictability barriers, the EPRs approach provides important additional guidance to forecasters in the early warning process. 

 

Dorrington, J., Wenta, M., Grazzini, F., Magnusson, L., Vitart, F., and Grams, C.: Precursors and pathways: Dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-415, 2024.

Dorrington, J., Grams, C., Grazzini, F., Magnusson, L. & Vitart, F. (2024) Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall. Quarterly Journal of the Royal Meteorological Society, 150(759), 776–795. Available from: https://doi.org/10.1002/qj.4622

Federico Grazzini, Joshua Dorrington, Christian M. Grams, George C. Craig, Linus Magnusson, Frederic Vitart  Grazzini, 2024: Improving forecasts of precipitation extremes over Northern and Central Italy using machine learning. In review for Quarterly Journal of the Royal Meteorological Society  Available from:  https://arxiv.org/html/2402.06542v1