Ensemble based first guess support towards a risk-based severe weather warning service
Ensemble based first guess support towards a risk-based severe weather warning service
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DOI：
10.1002/met.1377
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发表时间：
2014-07-01
影响因子：
2.7
通讯作者：
Sharpe, Michael
中科院分区：
文献类型：
--
作者：
Neal, Robert A.;Boyle, Patricia;Sharpe, Michael
本文介绍了一种基于集合的第一猜测支持工具，恶劣天气，它随着时间的推移，以支持不断变化的要求，从英国国家恶劣天气警报服务（NSWWS）。该预警工具对来自英国气象局全球和区域性风暴预测系统（MOGREPS）的区域部分的数据进行后处理，并被称为MOGREPS-W（“W”代表“警告”）。最初的系统为恶劣和极端天气提供基于区域的概率初步猜测警报，为预报员评估风险和作出概率陈述提供客观依据。NSWWS在2011年春季经历了重大变化，取消了警告的区域边界，更加注重基于风险的方法。现在，风险评估包括可能性和影响的细节，可能性和影响越大，中断的风险就越大。本文介绍了这些变化的NSWWS沿着相应的变化MOGREPS-W，使用的案例研究，从原来的和新的系统。对原有MOGREPS-W系统进行校准，减少预报不足的情况，提高了严重阵风和降雨警报的预报准确性。此外，对不同面积的不同区域的预测进行验证表明，较大的区域比较小的区域具有更好的预测准确性。
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.