Julia F. Lockwood, N. Stringer, Katie R. Hodge, P. Bett, J. Knight, Doug Smith, Adam A. Scaife, Matthew Patterson, N. Dunstone, H. Thornton
{"title":"英国平均风和极端风的季节性预测","authors":"Julia F. Lockwood, N. Stringer, Katie R. Hodge, P. Bett, J. Knight, Doug Smith, Adam A. Scaife, Matthew Patterson, N. Dunstone, H. Thornton","doi":"10.1002/qj.4568","DOIUrl":null,"url":null,"abstract":"Recent studies have shown that seasonal forecasting systems have significant skill in predicting Northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the UK, we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.This article is protected by copyright. All rights reserved.","PeriodicalId":49646,"journal":{"name":"Quarterly Journal of the Royal Meteorological Society","volume":" ","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Seasonal prediction of UK mean and extreme winds\",\"authors\":\"Julia F. Lockwood, N. Stringer, Katie R. Hodge, P. Bett, J. Knight, Doug Smith, Adam A. Scaife, Matthew Patterson, N. Dunstone, H. Thornton\",\"doi\":\"10.1002/qj.4568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent studies have shown that seasonal forecasting systems have significant skill in predicting Northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the UK, we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.This article is protected by copyright. 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Recent studies have shown that seasonal forecasting systems have significant skill in predicting Northern European winds and storms in winter, but other seasons have not been so extensively analysed. Given this fact, and coupled with requests from users of the Met Office 3‐month outlook for the UK, we have investigated the skill in predicting seasonal (3‐month) mean UK wind speed and storms (extreme winds) with a one‐month lead‐time, throughout the year, using a large ensemble from the Met Office's seasonal prediction system, GloSea. We find that seasonal UK storms and mean wind speeds are well correlated, and therefore a single prediction of UK mean wind speed will give an indication of predicted storm counts. Skill for these predictions is highest in winter (December–February), related to predictability of the North Atlantic oscillation. In contrast, summer (June–August) UK wind skill is not significant and furthermore appears to be negative. We find evidence, in both observations and model members, for a Rossby wave from the tropics influencing UK summer winds and forming a significant predictable component in the model ensemble mean. However, the model predictable signal appears to be out of phase with that observed leading to the negative correlation. Further investigation into summer Rossby wave generation and propagation is necessary to understand whether summer predictions could be improved.This article is protected by copyright. All rights reserved.
期刊介绍:
The Quarterly Journal of the Royal Meteorological Society is a journal published by the Royal Meteorological Society. It aims to communicate and document new research in the atmospheric sciences and related fields. The journal is considered one of the leading publications in meteorology worldwide. It accepts articles, comprehensive review articles, and comments on published papers. It is published eight times a year, with additional special issues.
The Quarterly Journal has a wide readership of scientists in the atmospheric and related fields. It is indexed and abstracted in various databases, including Advanced Polymers Abstracts, Agricultural Engineering Abstracts, CAB Abstracts, CABDirect, COMPENDEX, CSA Civil Engineering Abstracts, Earthquake Engineering Abstracts, Engineered Materials Abstracts, Science Citation Index, SCOPUS, Web of Science, and more.