Open AccessEnsemble based first guess support towards a risk‐based severe weather warning service
Author(s) -
Neal Robert A.,
Boyle Patricia,
Grahame Nicholas,
Mylne Kenneth,
Sharpe Michael
Publication year - 2014
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1377
Subject(s) - warning system , probabilistic logic , computer science , national weather service , service (business) , meteorology , early warning system , ensemble forecasting , environmental science , machine learning , business , geography , artificial intelligence , telecommunications , marketing
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. © 2013 British Crown copyright, the Met Office. Published by John Wiley & Sons Ltd.