Second storm to hit UK with more wind and rain as thousands remain without power

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1. 1

   Second storm to hit UK with more wind and rain as thousands remain without power

   BBC NewsPublished Oct 24, 2026Checked Oct 24, 2026

   “A second storm in as many days is set to batter parts of the UK, as thousands of homes remain without power following powerful winds and heavy rain on Friday. Storm Boelo is forecast to hit on Saturday, with an amber weather warning issued from 21:00 BST across much of Scotland as well as parts of Northern Ireland and northern England where gusts of up to 80mph (128km/h) could lash exposed areas. Travel chaos, power cuts and localised flooding are all possible as the storm sweeps in on Saturday. The warning comes just one day after Storm Austen unleashed wind gusts in excess of 75mph, resulting in toppled trees, transport disruption and thousands of power outages. Trains cancelled and roads warnings as Scotland prepares for Storm Boelo Storm Austen brings power cuts and travel disruption as more wind and rain forecast Strong winds and heavy rain will persist over the weekend as Storm Boelo moves across parts of the country. An amber weather warning for wind has been issued for across much of Scotland, excluding the Northern Isles, as well as the far north of Northern Ireland and England from 21:00 BST on Saturday to 21:00 BST on Sunday. Wind gusts of 60-70mph are forecast, with exposed coastal areas potentially seeing speeds of up to 80mph. The Central Lowlands could also experience periods of disruption as strong winds funnel through the region.”

2. 2

   Sociodemographic vulnerability to cold and stormy weather in relation to health and life.

   Weather and climate extremes (Lo et al.)Published Dec 1, 2025Checked Oct 24, 2026

   “However, their effects on the UK public's daily life and health remain underexplored. Leveraging resources from the Avon Longitudinal Study of Parents and Children, we surveyed 2 736 individuals about their experiences and behaviour during a cold spell and Storms Henk, Isha, and Jocelyn in January 2024. Over half of respondents reported reduced physical activity during cold (51 %) and stormy weather (53 %), and worsened mood during the storms (57 %). Certain sociodemographic groups had more than twice the odds of reporting negative impacts compared to other groups during both cold and storms: females, individuals aged ≤34, and those with a disability reported worse appetite; unemployed and employed individuals (compared to retired respondents) reported lower productivity; and those with a long-term health condition or disability reported worse physical health and access to healthcare. While over 90 % of respondents were aware of severe weather warnings and more than 80 % took adaptive actions, enhanced public messaging on staying active indoors and seasonal vaccinations, and financial support for home heating are crucial to mitigate the adverse effects of cold and stormy weather.”

3. 3

   Socioeconomic vulnerability and differential impact of severe weather-induced power outages

   PNAS Nexus (Ganz et al.)Published Sep 29, 2023Checked Oct 24, 2026

   “Our paper evaluates the hypothesis that more economically and socially vulnerable communities experience longer-duration power outages following hurricanes than less vulnerable communities do, conditional on the severity of the impact of the storm itself. Using data from eight major Atlantic hurricanes that made landfall between January 2017 and October 2020 and induced power outages for over 15 million customers in 588 counties in the Southeast, we demonstrate a significant relationship between socioeconomic vulnerability and the duration of time that elapses before power is restored for 95% of customers in a county. Specifically, a one-decile change in the socioeconomic status theme in the Social Vulnerability Index, a measure of vulnerability produced by the Centers for Disease Control and Prevention and the Agency for Toxic Substances and Disease Registry, produces a 6.1% change in expected outage duration in a focal county. This is equivalent to a 170-min average change in the period of time prior to power restoration.”

4. 4

   Public Perceptions and Behaviour in Response to Weather Warnings: A UK Perspective

   Research paper (Jenkins et al.)Published Jul 5, 2024Checked Oct 24, 2026

   “When amber or red warnings are issued, post event surveys are regularly used to capture public awareness, understanding, perceptions and behavioural responses to the warning. This talk presents the results of analysis of data collected from 32 surveys between 2014 and 2022 (n= 16,276). Here we examine whether perceptions and behavioural responses significantly differ according to the timing of the event, type of event the warning was issued for (e.g., snow, wind, heat etc), warning colour and whether storm naming was used. We also consider whether socio-demographic characteristics such as age, gender and location (urban/rural) predict perceptions and responses. Our results have implications for the communication of impact based weather warnings, and provide insights into how these can be best utilised to promote protective action and preparedness.”

5. 5

   Predicting power outages caused by extratropical storms

   Natural hazards and earth system sciences (Tervo et al.)Published Feb 11, 2021Checked Oct 24, 2026

   “Abstract. Strong winds induced by extratropical storms cause a large number of power outages especially in highly forested countries such as Finland. Thus, predicting the impact of the storms is one of the key challenges for power grid operators. This article introduces a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with forest inventory. We start by identifying storm objects from wind gust and pressure fields by using contour lines of 15 m s−1 and 1000 hPa respectively. The storm objects are then tracked and characterized with features derived from surface weather parameters and forest vegetation information. Finally, objects are classified with a supervised machine learning method based on how much damage to the power grid they are expected to cause. Random Forest Classifier, Support Vector Classifier, Naive Bayes, Gaussian Processes, and Multilayer Perceptron were evaluated for the classification task, Support Vector Classifier providing the best results.”