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This paper describes a new seamless blended multi‐model ensemble configuration of an existing probabilistic medium‐ to extended‐range weather pattern forecasting tool (called Decider) run operationally at the Met Office. In its initial configuration, the tool calculated and presented probabilistic weather pattern forecast information for five individual ensemble forecasting systems, which varied in terms of their number of ensemble members, horizontal resolution, update frequencies and forecast lead time. This resulted in multiple forecasts for the same validity time which varied in terms of forecast skill depending on the lead time in question. This presented challenges for end‐users (e.g., operational meteorologists) in terms of knowing which forecast output is best to use and at which lead time, as well as knowing what to do in situations where forecasts varied between ensembles. To get around these challenges, a new seamless blended multi‐model ensemble configuration has been implemented operationally, comprising of output from five separate ensembles, and provides a single best forecast from day one out to day 45. Objective verification for a set of eight weather pattern groups covering forecasts initialized over a 6‐year period (2017–2022) shows that the seamless blended multi‐model ensemble forecasts are at least as good as, if not better than the best performing individual model. [ABSTRACT FROM AUTHOR]

Reserve power systems are widely used to provide power to critical infrastructure systems in the event of power outages. The reserve power system may be subject to regulation, typically focussing on a strict operational time commitment, but the energy involved in supplying reserve power may be highly variable. For example, if heating or cooling is involved, energy consumption may be strongly influenced by prevailing weather conditions and seasonality. Replacing legacy assets (often diesel generators) with modern technologies could offer potential benefits and services back to the wider electricity system when not in use, therefore supporting a transition to low‐carbon energy networks. Drawing on the Great Britain telecommunications systems as an example, this paper demonstrates that meteorological reanalyses can be used to evaluate capacity requirements to maintain the regulated target of 5‐days operational reserve. Across three case‐study regions with diverse weather sensitivities, infrastructure with cooling‐driven electricity demand is shown to increase energy consumption during summer, thus determining the overall capacity of the reserve required and the availability of 'surplus' capacity. Lower risk tolerance is shown to lead to a substantial cost increase in terms of capacity required but also enhanced opportunities for surplus capacity. The use of meteorological forecast information is shown to facilitate increased surplus capacity. Availability of surplus capacity is compared to a measure of supply–stress (demand‐net‐wind) on the wider energy network. For infrastructure with cooling‐driven demand (typical of most UK telecommunication assets), it is shown that surplus availability peaks during periods of supply–stress, offering the greatest potential benefit to the national electricity grid. [ABSTRACT FROM AUTHOR]

Extreme weather events can cause significant damage to power distribution network infrastructure, often resulting in power outages. Distribution Network Operators (DNOs) are faced with the challenging task of responding to these outages in real time while maintaining a resilient grid. Our paper presents an innovative approach to alert operators about the potential risk associated with upcoming extreme weather through a normalized fragility curve. The uniqueness of the curve is the ability to capture regional differences across a DNO's territory while presenting operators with a means of setting unified risk thresholds. This can support a proactive response and allow the staging of necessary resources to minimize the threat posed by such events. Our approach captures the changes in failure probability associated with differing wind regimes and demonstrates the benefit of sub‐regional meteorological information. The proposed approach is demonstrated for wind events using 20 years of historical fault records from a DNO in the United Kingdom (UK). While its efficacy is demonstrated for windstorms in the UK, the approach could be applied globally to develop normalized fragility curves for other types of seasonal extreme weather events such as snowstorms, hurricanes, or linked hazards such as wildfires. The approach can also facilitate an understanding of how infrastructure may operate under future climate conditions, supporting proactive adaptation. [ABSTRACT FROM AUTHOR]

Global warming is projected to have major implications on global health. It is however not yet clear how this will translate to impacts on the healthcare system. By linking changes in temperature with changes in required bed days at a hospital level, through the use of a simple bed model, we quantify the projected impacts UK hospitals will need to adapt to. We show that there is already a local peak of bed days required in the main summer months due to hot temperatures. The results further show that there will be a significant increase during the main summer in both the mean and maximum number of beds needed, but a non‐significant decrease during the peak winter months. These changes lead to a more constant need of care of the year and shift the seasonal cycle of lowest hospital needs. [ABSTRACT FROM AUTHOR]

A new approach to improve the ocean surface boundary condition used in regional numerical weather prediction is proposed. Typically, regional atmosphere forecast systems assume a fixed sea surface temperature during a simulation. The study assesses the use of ocean temperature from an operational regional ocean model as an evolving lower boundary in a kilometrescale regional atmosphere configuration centred on the UK. Simulations of a winter and two five day duration summer case studies associated with anomalously warm temperatures are considered. The largest impact is found in summer, when a growing cold bias in mean temperature over land compared with observations is apparent when using a fixed global-scale analysis lower boundary condition. The mean error is improved by 0.1 K when using a fixed temperature boundary condition from a kilometre-scale regional ocean model initial condition. When using hourly surface temperature data from the same regional ocean model, the error is improved by 0.5 K for this case. Prediction of daytime maximum air temperature is also improved during the summer heat wave cases. A winter case study shows marginal improvement over the ocean and negligible changes over land. These results are confirmed in longer duration experiments using an hourly cycling regional forecast system with data assimilation for summer and winter periods. A systematic and statistically significant improvement of near-surface temperature verification relative to the observations over land is demonstrated for both summer and winter using the new approach. This study supports future operational implementation of a time-varying lower boundary for regional numerical weather prediction. [ABSTRACT FROM AUTHOR]

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 forMedium-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. [ABSTRACT FROM AUTHOR]

Despite increasing use of impact‐based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This presents a challenge for validation of warnings and weather impact models. This study considers the application of social sensing, the systematic analysis of unsolicited social media data to observe real‐world events, to determine the impacts of named storms in the United Kingdom and Ireland during the winter storm season 2017–2018. User posts on Twitter are analysed to show that social sensing can robustly detect and locate storm events. Comprehensive filtering of tweets containing weather keywords reveals that ~3% of tweets are relevant to severe weather events and, for those, locations could be derived for about 75%. Impacts of storms on Twitter users are explored using the text content of storm‐related tweets to assess changes in sentiment and topics of discussion over the period before, during and after each storm event. Sentiment shows a consistent response to storms, with an increase in expressed negative emotion. Topics of discussion move from warnings as the storm approaches, to local observations and reportage during the storm, to accounts of damage/disruption and sharing of news reports following the event. There is a high level of humour expressed throughout. This study demonstrates a novel methodology for identifying tweets which can be used to assess the impacts of storms and other extreme weather events. Further development could lead to improved understanding of social impacts of storms and impact model validation. [ABSTRACT FROM AUTHOR]

Forecasts of high‐impact weather systems require sufficiently high resolution of state‐of‐the‐art numerical models in order to resolve the small‐scale features. At the National Centre for Medium Range Weather Forecasting (NCMRWF) in India, a global ensemble prediction system (EPS—called the NEPS) has been implemented operationally at 12 km horizontal grid size. The NEPS configuration is based on the UK Met Office Global and Regional Ensemble Prediction System (MOGREPS). Initial condition perturbations are generated by the ensemble transform Kalman filter (ETKF) method. Model uncertainties are taken care of by stochastic kinetic energy backscatter (SKEB) and random parameters (RP) schemes. Forecast perturbations obtained from 6 hr short forecasts of 22 ensemble members are updated by the ETKF four times a day (0000, 0600, 1200 and 1800 UTC). Perturbations of surface parameters such as sea‐surface temperature, soil moisture content and soil temperature are also included in the new NEPS. The NEPS aims to provide 10 day probabilistic long forecasts using 23 ensemble members (22 perturbed plus one control). The long forecast provided at 0000 UTC is the combination of 11 members from the 0000 UTC cycle and lagged 11 members from the 1200 UTC cycle. The new NEPS shows improvements in terms of forecast agreement among the members in comparison with the previously operational NEPS that was running at 33 km horizontal grid size with 44 perturbed members. The ratio between the root mean square error of the ensemble mean and ensemble spread as a function of lead time has improved in both the Northern and Southern Hemispheres in the new NEPS. [ABSTRACT FROM AUTHOR]

Reliable and affordable telecommunications are an integral part of service‐based economies, but the nature of the associated physical infrastructure leads to considerable exposure to weather. With unique access to observational records of the UK fixed‐line telecommunications infrastructure, an end‐to‐end demonstration of how extended range forecasts can be used to improve the management of weather risk is presented, assessing forecast value on both short‐term "operational" (weeks) and long‐term "planning" timeframes (months/years). A robust long‐term weather‐related fault‐rate climatology is first constructed at weekly resolution, based on the ERA‐Interim reanalysis. A clear dependence of winter fault rates on large‐scale atmospheric circulation indices is demonstrated. The European Centre for Medium‐Range Weather Forecasts (ECMWF) sub‐seasonal forecast system is subsequently shown to produce skilful forecast of winter weekly fault rates at lead times of three to four weeks ahead (i.e. days 14–20 and 21–28). Forecast skill at a given lead time is, however, a necessary rather than a sufficient condition for improved risk management. It is shown that practical decision‐making leads to dependencies across multiple forecasts times that cannot be modelled using traditional "cost‐loss matrix" methods as errors in previous forecasts influence the value of subsequent forecasts. A parsimonious model representing operational decision‐making for fault repair scheduling is therefore constructed to show that fault‐rate forecast skill does improve both short‐ and long‐term management outcomes (in this case meeting performance targets more often in the short term, or reducing the resources required to achieve these targets in the long term). Consequently, it is argued that new methods are needed for forecast skill assessment in complex decision environments. [ABSTRACT FROM AUTHOR]