Ensemble based first guess support towards a risk-based severe weather warning service

被引:46
|
作者
Neal, Robert A. [1 ]
Boyle, Patricia [1 ]
Grahame, Nicholas [1 ]
Mylne, Kenneth [1 ]
Sharpe, Michael [1 ]
机构
[1] Met Off, Exeter EX1 3PB, Devon, England
关键词
probabilistic forecast verification; severe weather impact; forecast application; PREDICTION; FORECAST;
D O I
10.1002/met.1377
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
This paper describes an ensemble-based first guess support tool for severe weather, which has evolved over time to support changing requirements from the UK National Severe Weather Warning Service (NSWWS). This warning tool post-processes data from the regional component of the Met Office Global and Regional Ensemble Prediction System (MOGREPS), and is known as MOGREPS-W ('W' standing for 'warnings'). The original system produced area-based probabilistic first guess warnings for severe and extreme weather, providing forecasters with an objective basis for assessing risk and making probability statements. The NSWWS underwent significant changes in spring 2011, removing area boundaries for warnings and focusing more on a risk-based approach. Warnings now include details of both likelihood and impact, whereby the higher the likelihood and impact, the greater the risk of disruption. This paper describes these changes to the NSWWS along with the corresponding changes to MOGREPS-W, using case studies from both the original and new systems. Calibration of the original MOGREPS-W system improves forecast accuracy of severe wind gust and rainfall warnings by reducing under-forecasting. In addition, verification of forecasts from different groups of areas of different sizes shows that larger areas have better forecast accuracy than smaller areas.
引用
收藏
页码:563 / 577
页数:15
相关论文
共 50 条
  • [1] Risk-Based Decision Support in Service Value Networks
    Michalk, Wibke
    Blau, Benjamin
    Stosser, Jochen
    Weinhardt, Christof
    [J]. 43RD HAWAII INTERNATIONAL CONFERENCE ON SYSTEMS SCIENCES VOLS 1-5 (HICSS 2010), 2010, : 1350 - 1358
  • [2] Decision making with risk-based weather warnings
    Mu, Di
    Kaplan, Todd R.
    Dankers, Rutger
    [J]. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2018, 30 : 59 - 73
  • [3] Designing an innovative warning system to support risk-based meat inspection in poultry slaughterhouses
    Allain, Virginie
    Salines, Morgane
    Le Bouquin, Sophie
    Magras, Catherine
    [J]. FOOD CONTROL, 2018, 89 : 177 - 186
  • [4] Risk-based emergency decision support
    Körte, J
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2003, 82 (03) : 235 - 246
  • [5] EXPERIMENTS ON IMPACT-BASED FORECASTING AND RISK-BASED WARNING OF TYPHOON IN CHINA
    Wei, Li
    Li, Jiaying
    Yang, Xuan
    [J]. TROPICAL CYCLONE RESEARCH AND REVIEW, 2018, 7 (01) : 31 - 36
  • [6] Risk-based Service Selection in Federated Clouds
    Ahmed, Usama
    Petri, Ioan
    Rana, Omer
    Raza, Imran
    Hussain, Syed Asad
    [J]. 2018 IEEE/ACM INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING COMPANION (UCC COMPANION), 2018, : 77 - 82
  • [7] Towards a risk-based typology for transnational education
    Healey, Nigel Martin
    [J]. HIGHER EDUCATION, 2015, 69 (01) : 1 - 18
  • [8] Studies Support Risk-Based Mammography Screening
    Peres, Judy
    [J]. JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2012, 104 (19) : 1428 - 1430
  • [9] Risk-based warning system design methodology for multimode processes
    Wang, Hangzhou
    Khan, Faisal
    Ahmed, Salim
    Imtiaz, Syed
    [J]. IFAC PAPERSONLINE, 2015, 48 (08): : 663 - 668
  • [10] A risk-based approach to design warning system for processing facilities
    Chang, Yanjun
    Khan, Faisal
    Ahmed, Salim
    [J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2011, 89 (05) : 310 - 316