NOAA Great Lakes Environmental Research Laboratory

The latest news and information about NOAA research in and around the Great Lakes

map of great lakes showing colors of model output


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Improving lake effect snow forecasts by making models talk to each other

If you live in the Great Lakes basin and have been on or even near a road recently, you might be feeling unreasonably ragey at the mere mention of lake effect snow. We get it. But bear with us, because we’re doing some cool science we’d like to tell you about. It may even make your commute easier someday, or at least more predictable.

GLERL scientists are working with researchers at the University of Michigan’s Cooperative Institute for Great Lakes Research (CIGLR), the National Weather Service, and NOAA’s Earth Systems Research Laboratory (ESRL) to make lake effect snow forecasts in the Great Lakes better.

NOAA’s high resolution rapid refresh (HRRR) model is the most commonly used weather model for predicting lake effect snow. An experimental version runs on a beastly high-performance computer at ESRL in Colorado, and predicts a whole list of atmospheric variables (including snowfall) every 15 minutes. The model relies on water surface temperature data, collected via satellite, to make its predictions. It’s important to give the model accurate water surface temperatures to estimate evaporation across the Great Lakes, since it is the main driver of lake effect snow.

Unfortunately, satellite temperature data has limitations. If clouds keep satellites from measuring the temperature at a specific location, the weather model will just use the most recent measurement it has. Since it’s especially cloudy in the Great Lakes during the lake effect snow season (late fall and early winter), that data could be days old. Because lake temperatures are changing quite rapidly this time of year, days-old data just doesn’t cut it.

As it turns out, GLERL already has a model that predicts Great Lakes surface temperature pretty well. The Great Lakes Operational Forecast System (GLOFS) spits out lake surface temperatures every hour. If we tell the weather model to use GLOFS output instead of satellite data, it has the potential to do a far better job of forecasting lake effect snow.

Linking two models like this is called “coupling”. GLOFS actually already uses input from HRRR—wind, air temperature, pressure, clouds and humidity data all inform GLOFS’ predictions. We’re just coupling the models in both directions. HRRR will send its output to GLOFS, GLOFS will “talk back” with its own predictions of water surface temperature (and ice cover), and HRRR will produce a (hopefully) more informed prediction of lake effect snow.

Initial results are promising. We used the coupled models to do a ‘hindcast’ (a forecast for the past) to predict lake effect snow for a major event over Lake Erie in November of 2014. They did a significantly better job than without coupling. The figure below shows the difference.

The coupled models improved cumulative snow water equivalent forecasts. Red shows where the model increased snowfall.

You’ll notice a band of blue on the southeastern edge of Lake Erie, indicating that the coupled models predicted less lake effect snow in that area. There’s a band of orange directly to the north of it, where the coupled models predicted more lake effect snow. What you’re seeing is the coupled model predicting the same band of snow, but further north, closer to where it actually fell.

That storm slammed the city of Buffalo, New York, killing 13 people. Better lake effect snow predictions have the potential to save lives.

GLERL and partners will be doing further testing this winter, and if it works out, the model coupling will be carried over in research-to-operations transitions.


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Working to improve Great Lakes modeling

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The new two-way coupled model is driven by heat budget estimates (how much energy enters the system); that affects the water budget and how much energy is exchanged between a lake and the atmosphere along with large lake processes that are dynamic and seasonally variable.

The Great Lakes are more like inland seas. From the cold depths of Lake Superior fisheries to the shallow algae blooms of Lake Erie, the bodies of water differ greatly from one another. Yet they are all part of one climate system.

Up until now, atmospheric models and hydrodynamic models have remained separate to a large extent in the region, with only a few attempts to loosely couple them. In a new study, published this week in the Journal of Climate, an integrated model brings together climate and water models.

The collaborative work brought together researchers from Michigan Technological University, Loyola Marymount University, LimnoTech as well as GLERL scientist, Philip Chu. Pengfei Xue, an assistant professor of civil and environmental engineering at Michigan Tech, led the study through his work at the Great Lakes Research Center on campus.

“One of the important concepts in climate change, in addition to knowing the warming trend, is understanding that extreme events become more severe,” Xue says. “That is both a challenge and an important focus in regional climate modeling.”

To help understand climate change and other environmental issues, Xue and his team connected the dots between the air and water of the Great Lakes. The new model will be useful for climate predictions, habitat modeling for invasive species, oil spill mitigation and other environmental research.

To read more about this research, please visit a full version of this Michigan Tech news article, posted by Allison Mills at: http://www.mtu.edu/news/stories/2016/november/weather-storm-improving-great-lakes-modeling.html