Numerical weather prediction models still struggle to accurately forecast extreme precipitation events, both due to their relative rarity and the length-scales of the processes involved, which fall below the grid scale and so must be parameterised. However, extreme precipitation events are by no means decoupled from the large-scale flow: the prevailing winds and wave-structures ‘set the scene’ for amplified or suppressed risk of an extreme, by controlling moisture availability and vertical stability. This is especially true in the mid-latitudes where frontal rainfall is dominant.
The idea that explicitly resolved large-scale circulation patterns, may serve as potentially skilful precursors to extreme precipitation has been explored in a number of studies, investigating precursor patterns to flooding in particular geographical areas, in both process-oriented and regime-based approaches. However, such insights are not yet easily extensible to other regions, or available to the operational meteorologist.
We will report on the development of a new flexible tool to identify the precursors of extreme events, assess their usefulness as predictors, and produce statistical forecasts of increased event probability. This will allow the leveraging of physical insight to add value to traditional NWP. We will use results found for UK and Northern Italy extreme precipitation as motivational examples, exploring time-scales ranging from the medium range out to the sub-seasonal. Development of this tool is driven by applications to operational meteorology, but we also see considerable opportunities for application in an academic setting, by streamlining studies of large-scale physical processes and enabling the use of precursor chains in model validation and bias analysis.
How to cite: Dorrington, J., Grams, C., Grazzini, F., Ferranti, L., Magnusson, L., and Vitart, F.: A tool to identify large-scale dynamical precursors to European extreme precipitation, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-220, https://doi.org/10.5194/ems2022-220, 2022.