Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

Abstract

A number of studies have investigated the large‐scale drivers and upstream‐precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote in both time and space from the impacted region. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building story‐lines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather, and providing early warning of their possible development. However, operational applications of this developing knowledge‐base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion, and for reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how for more continental‐scale applications the regionally‐specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales and regions.This article is protected by copyright. All rights reserved.

Precursors and pathways: Dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood

“…The coherent Lagrangian pathways we have just identified are connected to robust dynamical flow precursors prior to north Italian extreme rainfall on large scales. Documented extensively in recent years (Dorrington et al, 2023;Grazzini et al, 2021) these precursors isolate configurations of the large-scale flow which favour the occurrence of a localised extreme in the following days. The details of the precursor circulations undergo seasonal fluctuations as the large-scale background state evolves, and while they are more clearly visible at long lead times during SON and DJF, they are also relevant in MAM and still robust at time lags of 0-2 days (Dorrington et al, 2023).…”

“…Documented extensively in recent years (Dorrington et al, 2023;Grazzini et al, 2021) these precursors isolate configurations of the large-scale flow which favour the occurrence of a localised extreme in the following days. The details of the precursor circulations undergo seasonal fluctuations as the large-scale background state evolves, and while they are more clearly visible at long lead times during SON and DJF, they are also relevant in MAM and still robust at time lags of 0-2 days (Dorrington et al, 2023). a strong positive Mediterranean moisture transport anomaly with suppressed transport over the UK, respectively.…”

“…Interestingly, this was not the case for the earlier rainfall on the 10th: while IVTmag precursors are slightly positive, the Z500 precursor is 2 standard deviations below normal, which corresponds to the West European ridge and tilted Eastern Mediterranean low visible for the 10th in Figure 8. Such small-scale cut-off lows are not well accounted for in the large-scale precursor framework, and this is partly responsible for the overall reduced coherence of flow precursors in MAM and JJA noted in Dorrington et al (2023).…”

“…We have found that the May 2023 event is linked to a chance co-occurrence of high rainfall from multiple airmass pathways, and that each pathway can be linked to a distinct large-scale flow. In Dorrington et al (2023) it was proposed that flow precursors could be used to improve the predictability of extreme events and understanding of forecast evolution, but with the assumption that a single set of precursors could be used to understand the event. The three-pathway narrative we have described above provides a challenge for such an approach, especially as the operational application of specialised Lagrangian tools for identifying such pathways is impractical, due to the substantial computational resources and extensive time required for calculating trajectories.…”

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

“…The choice of predictors is based largely on previous work by the authors (Grazzini et al, 2020a(Grazzini et al, , 2021. In addition, we include non-local predictors: spatial composites of Euro-Atlantic anomaly patterns in the days preceding Italian EPEs, as described in a recent companion paper (Dorrington et al, 2023). MaLCoX -composed of two modules that detect and classify precipitation extremes respectively -has been implemented semi-operationally at ARPAE-SIMC using as predictors the available fields the institute is receiving in real time from the European Centre for Medium-Range Weather Forecasts (ECMWF) dissemination stream.…”