Meteotsunami Forecasting and Warning System for the Laurentian Great Lakes:

Dates: June 19-21, 2017
Leads: Chin Wu, University of Wisconsin-Madison; Dave Kristovich, University of Illinois at Urbana Champaign, Philip Chu, NOAA GLERL; Eric Anderson, NOAA GLERL; Steven Ruberg, NOAA GLERL; Greg Mann, NOAA NWS; Yu-Hen Hu, University of Wisconsin-Madison; Victoria Campbell-Arvai, University of Michigan
GLERL Research Program: Integrated Physical and Ecological Modeling and Forecasting

Lake Michigan waves at South Haven lighthouse. Credit: Tom Gill

Description: Meteotsunamis are similar to earthquake-generated tsunamis, but are caused by meteorological events such as rapid changes in barometric pressure often associated with fast moving weather systems. Although many meteotsunamis are too small to notice, large meteotsunamis can have devastating coastal impacts (damaging waves, flooding, strong currents) that cause significant damage, injury and death. Meteotsunamis are frequently observed in the Great Lakes, averaging 106 events per year (Bechle et. al., 2016). Examples of destructive Great Lakes meteotsunamis include:

• In 1929, a retreating 6 meter wave pulled ten people to their deaths at in Lake Michigan at Grand Haven, MI [Grand Haven Daily Tribune 1929].
• In 1954, a 3 meter wave hit Chicago and swept many fishermen off a pier, killing seven.
• In 1998, a strong meteotsunami in Lake Michigan capsized a tug boat at the White Lake, MI harbor [NOAA 1998].
• On July 4, 2003 at Sawyer, MI, seven people drowned in an incident initially attributed to rip currents, though the water level records indicated a moderate meteotsunami occurred around the time of the drownings.
• Anderson et al. (2015) documented recent meteotsunamis in Lake Erie swept 3 swimmers one kilometer offshore.
• A so-called tidal wave (meteotsunami) in Lake Superior (2014) caused flooding, interrupting shipping operations and prompting residents to be evacuated.

Furthermore, sudden and unexpected water level drawdown due to meteotsunamis could cause dry intakes, leading to insufficient water supply and endangering the safety of nuclear power plants in the Great Lakes. While the hazards that meteotsunamis pose the Great Lakes and U.S. oceanic coastlines has been recognized, a reliable warning system for forecasting meteotsunamis has yet to be developed.

Meteotsunami forecasting has been hindered by challenges related to big data features associated with rapidly varied atmospheric and hydrodynamic conditions. The four V’s, defined by the NSF/JST Big Data in Disasters Workshop [Pu and Kitsuregawa 2013], are (i) velocity – the speed at which data is generated and processed to meet demands; (ii) veracity – the quality of the data in terms of accuracy, uncertainty, and reliability; (iii) volume – the scale of data and the implications of scale on how it can be processed; and (iv) variety – the heterogeneity of data from various sources with diverse formats, resolutions, and semantics.

Goals: The overall goal of the summit is to construct a framework for developing a real-time meteotsunami warning system in the Laurentian Great Lakes that can meet weather forecast requirements, thus reducing risks due to coastal flooding, preventing swimmer drowning, and increasing the safety of nuclear power plant operations.

2018 Ocean Science Meeting:

2017 Meteotsunami Summit: