AUTHOR=Chang Edmund K. M. , Pan Yutong , Wang Wanqiu , Zheng Cheng TITLE=Developing and evaluating week 2 and weeks 3-4 outlook tools for extratropical storminess JOURNAL=Frontiers in Earth Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.963779 DOI=10.3389/feart.2022.963779 ISSN=2296-6463 ABSTRACT=

Extratropical cyclones give rise to most of the high impact weather in the mid-to high-latitudes during the cool seasons, including heavy precipitation and strong winds. Thus it is important for stakeholders to be informed of approaching periods of increased or decreased cyclone activity. While individual cyclone tracks can be predicted out to about a week or so, from week 2 on, statistics summarizing cyclone activity, or storminess, are more useful. Storminess can be defined based on Lagrangian cyclone tracking or by Eulerian variance statistics. The outlook includes a combination of both methods. Lagrangian cyclone tracks provide information about where cyclones pass through and are more intuitive to users, while Eulerian variance statistics have been shown to be highly correlated with cyclone-related weather and are expected to be more predictable given that they are not as noisy. In this paper, we evaluate a storminess outlook tool developed based on dynamical model forecasts in the week-2 and weeks 3-4 time ranges. The outlook uses two 6-hourly subseasonal ensemble forecasts–the Global Ensemble Forecast System version 12 (GEFSv12), and the coupled Climate Forecast System version 2 (CFSv2). Hindcasts and operational forecasts from 1999–2016 are used to assess the prediction skill. Our results show that the GEFSv12 and CFSv2 combined ensemble has higher skill than either individual ensemble. The combined ensemble shows some skill in predicting cyclone amplitude and frequency out to weeks 3-4, with highest skill in winter, and lowest skill in summer. Models also show some skill in predicting the statistics of deep cyclones for week 2. The prediction skills for an Eulerian sea level pressure variance storminess metric is significantly higher than those for Lagrangian track statistics. Our results also show that GEFSv12 performs better than its predecessor GEFSv11. Correlations between the storminess indices and surface weather, including precipitation and high winds, are examined. A publicly accessible web page has been developed to display the subseasonal predictions in real time. The web page also contains information on climatology and forecast verification to enable users to make more informed use of the outlook.