Speaking: Edmund Chang, Stony Brook University, Wanqiu Wang, NOAA/CPC, Di Chen, Stony Brook University, Yutong Pan, NOAA/CPC
Part of the NOAA in Alaska and the Arctic seminar series hosted by NOAA NCEI Regional Climate Services Director, Alaska Region and the NOS Science Seminar Series.
Remote Access
https://attendee.gotowebinar.com/register/4926113776693821452this link opens in a new window
Webinar ID: 902-953-659
Abstract
Extratropical cyclones give rise to most of the high impact weather near Alaska, including heavy precipitation and strong winds. Thus it is important for many stakeholders to be warned of approaching periods of increased or decreased potential of storm activities. 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 are expected to be more predictable and have been shown to be highly correlated with cyclone related weather. The outlook uses 6-hrly sub-seasonal forecasts from GEFSv12 and CFSv2. Hindcasts and operational forecasts from 1999-2016 have been used to assess the prediction skill. Our results show that the combined ensemble has higher skill than either individual ensemble. The combined ensemble shows good skill in predicting cyclone amplitude and frequency for week 2, and some skill in predicting these metrics for weeks 3-4. Models also show some skill in predicting the statistics of deep cyclones for week 2. For both week 2 and weeks 3-4, the prediction skills for an Eulerian sea level pressure variance storminess metric is significantly higher than those for Lagrangian track statistics. We expect that the skills for real time forecasts should be higher than those in the hindcasts since the operational ensembles are much larger than the hindcast ensembles. A publicly accessible web page will be developed to display the subseasonal predictions in real time. The web page will also contain information on climatology and forecast verification to enable users to make more informed use of the outlook.