Linking weather patterns to regional extreme precipitation for highlighting potential flood events in medium‐ to long‐range forecasts

Abstract

Medium‐ to long‐range precipitation forecasts are a crucial component in mitigating the impacts of fluvial flood events. Although precipitation is difficult to predict at these lead times, the forecast skill of atmospheric circulation tends to be greater. The study explores using weather patterns (WPs) as a preliminary step in producing forecasts of upper‐tail precipitation threshold exceedance probabilities for the UK. The WPs are predefined, discrete states representing daily mean sea‐level pressure (MSLP) over a European–North Atlantic domain. The WPs most likely to be associated with flooding are highlighted by calculating upper‐tail exceedance probabilities derived from the conditional distributions of regional precipitation given each WP. WPs associated with higher probabilities of extreme precipitation are shown to have occurred during two well‐known flood events: the 2014 Somerset Levels floods in southwest England; and Storm Desmond over the northern UK in December 2015. To illustrate the potential of this WP‐based prediction framework, a forecast guidance tool called Fluvial Decider is introduced. It is intended for use by hydro‐meteorologists in the England and Wales Flood Forecasting Centre (FFC). Forecasts of the MSLP from ensemble prediction systems (EPSs) are assigned to the closest‐matching WP, providing daily probabilistic forecasts of WPs out to the chosen lead time. Combining these probabilities with observed precipitation threshold exceedance probabilities provides a parsimonious tool for highlighting potential periods with increased risk of flooding. Model forecasts using the European Centre for Medium‐range Weather Forecasts (ECMWF) EPS highlighted both flood events as being at a higher than average risk of heavy extreme precipitation at lead times of over five days.

What do large‐scale patterns teach us about extreme precipitation over the Mediterranean at medium‐ and extended‐range forecasts?

“…Büeler et al (2021), for example, analysed the skill of the ECMWF forecasts in predicting seven weather regimes over Europe and the horizon of skilful predictions was about 14 days. Richardson et al (2020b) showed that the ECMWF 51-member model has a predictability limit of about 15 days for the eight UK-defined regimes, when analysing the performance based on BS and considering a 1-day flexibility window in the occurrence of the patterns. Increased model skill is also observed in this study when considering a flexibility window in the occurrence of the patterns (not shown).…”

“…These patterns can be used as a prognostic tool for extremes and provide skilful predictions of surface weather variability at longer lead times. In a recent study, it was shown that UK‐specific large‐scale patterns can support the predictability of extreme rainfall at medium‐range forecasts (Richardson et al ., 2020b). The same patterns have also been used to infer information about the likelihood of coastal flooding (Neal et al ., 2018) and of droughts (Richardson et al ., 2020a) at medium‐ and extended‐range lead times.…”

Synoptic circulation changes over Central Europe from 1900 to 2100: Reanalyses and Coupled Model Intercomparison Project phase 6

“…CCs can analyse the impact of both natural climate variability and external anthropogenic forcing at the regional scale. Beyond mirroring changes in mean climate, CCs also represent a powerful tool to investigate extreme events, which are often associated with specific atmospheric circulation patterns (Richardson et al ., 2020; Mastrantonas et al ., 2021).…”

The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather

“…For example, the climatology centred on 1 June would include all days between 12 April and 21 July (±50 days) from all years in the 39‐year period. A 101‐day period is roughly equivalent to a 3‐month climatology, which is often used as a definition for a seasonal climatology and has been used in other related studies (Richardson, Neal, et al, 2020). Step 2 —Calculate weather pattern empirical probabilities of exceeding the 95th percentile …”