%0 Journal Article %1 thornton2019skilful %A Thornton, Hazel Elizabeth %A Scaife, Adam %A Hoskins, Brian J %A Brayshaw, David J %A Smith, Doug %A Dunstone, Nick %A Stringer, Nicky %A Bett, Philip E %D 2019 %I IOP Publishing %J Environmental Research Letters %K MySeclifirmWTwork energy gas myown nao seasonal skill weathertypes %P 024009 %R 10.1088/1748-9326/aaf338 %T Skilful seasonal prediction of winter gas demand %V 14 %X In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events.

@article{thornton2019skilful, abstract = {In Britain, residential properties are predominantly heated using gas central heating systems. Ensuring a reliable supply of gas is therefore vital in protecting vulnerable sections of society from the adverse effects of cold weather. Ahead of the winter, the grid operator makes a prediction of gas demand to better anticipate possible conditions. Seasonal weather forecasts are not currently used to inform this demand prediction. Here we assess whether seasonal weather forecasts can skilfully predict the weather-driven component of both winter mean gas demand and the number of extreme gas demand days over the winter period. We find that both the mean and the number of extreme days are predicted with some skill from early November using seasonal forecasts of the large-scale atmospheric circulation (r > 0.5). Although temperature is most strongly correlated with gas demand, the more skilful prediction of the atmospheric circulation means it is a better predictor of demand. If seasonal weather forecasts are incorporated into pre-winter gas demand planning, they could help improve the security of gas supplies and reduce the impacts associated with extreme demand events.
}, added-at = {2018-12-03T12:40:01.000+0100}, author = {Thornton, Hazel Elizabeth and Scaife, Adam and Hoskins, Brian J and Brayshaw, David J and Smith, Doug and Dunstone, Nick and Stringer, Nicky and Bett, Philip E}, biburl = {https://www.bibsonomy.org/bibtex/2aa79ec66220c218bc33fdf9fb7e7652e/pbett}, doi = {10.1088/1748-9326/aaf338}, interhash = {fb04ee094ee6de87858b7236d0a9e0b8}, intrahash = {aa79ec66220c218bc33fdf9fb7e7652e}, journal = {Environmental Research Letters}, keywords = {MySeclifirmWTwork energy gas myown nao seasonal skill weathertypes}, month = feb, pages = 024009, publisher = {IOP Publishing}, timestamp = {2019-08-14T12:55:54.000+0200}, title = {Skilful seasonal prediction of winter gas demand}, volume = 14, year = 2019 }