NOAA Great Lakes Environmental Research Laboratory

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

Tag Archives: lake effect snow


by Leave a comment

Lake effect snow: What, why and how?

Winter is nearly here — and those who live and work in the Great Lakes region are already wondering what the winter of 2021 has in store. Early indications suggest a La Niña winter pattern, which shifts the odds towards cooler, wetter weather with more ice cover. 

More snow and ice can mean more fun, and can be great for winter sports like ice fishing, snowmobiling and skiing. Unfortunately, it can also mean severe weather events involving ice and snow. In the Great Lakes region, snow comes via the usual low pressure systems, but we also can get lake effect snow. 

Average location of the jet stream and typical temperature and precipitation impacts during La Niña winter over North America. Map by Fiona Martin for NOAA Climate.gov.

What is lake effect snow?

In the Great Lakes region, hazardous winter weather often happens when cold air descends from the Arctic region. Lake effect snow is different from a low pressure snow storm in that it is a much more localized and sometimes very rapid and intense snow event. As a cold, dry air mass moves over the unfrozen and relatively warm waters of the Great Lakes, warmth and moisture from the lakes are transferred into the atmosphere. This moisture then gets dumped downwind as snow.

Lake Effect Snow Can Be Dangerous

Lake effect snow storms can be very dangerous. For example, 13 people were killed by a storm that took place November 17-19, 2014 in Buffalo, New York. During the storm, more than five  feet of snow fell over areas just east of Buffalo, with mere inches falling just a few miles away to the north. Not only were lives lost, but the storm disrupted travel and transportation, downed trees and damaged roofs, and caused widespread power outages. Improving  lake effect snow forecasts is critical because of the many ways lake effect snow conditions affect commerce, recreation, and community safety.

Lake Effect Snow animation: Mid-December 2016 The lake effect snow EVENT resulted in extremely heavy snow across Michigan, Ohio, upstate New York as well as the province of Ontario east of Lake Superior and Huron.

Why is lake effect snow so hard to forecast?

There are a number of factors that make lake effect snow forecasting difficult. The widths of lake-effect snowfall bands are usually less than 3 miles — a very small width that makes them difficult to pinpoint in models. The types of field measurements scientists need to make forecasts better are also hard to come by, especially in the winter!  We would like to take frequent lake temperature and lake ice measurements but that is currently not possible to do during the winter, as conditions are too rough and dangerous for research vessels and buoys. Satellite measurements can also be hard to come by. The Great Lakes region is notoriously cloudy in the winter –  it’s not uncommon to go for over a week without usable imagery. 

MODIS satellite image of a lake effect snow event in the Great Lakes, caused by extensive evaporation as cold air moves over the relatively warm lakes. November 20, 2014. Credit: NOAA Great Lakes CoastWatch.

GLERL and CIGLR work to improve lake effect snow forecasting

Currently, NOAA Great Lakes operational models provide guidance for lake effect snow forecasts and scientists at NOAA GLERL and CIGLR are conducting studies to improve them. 

They use data from lake effect snow events in the past and compare how a new model performs relative to an existing model.  One way to improve forecast model predictions is through a model coupling approach, or linking two models so that they can communicate with each other. When they are linked, the models can share their outputs with each other and produce a better prediction in the end. 

Our lake effect snow research continues

Our lake effect modeling research is ongoing, and GLERL, CIGLR, NWS Detroit, the NOAA Global Systems Laboratory continue to address the complex challenges and and our studies build upon each other to improve modeling of lake-effect snow events. A new focus will be on running the models on a smaller grid scale and continuing to work to improve temperature estimates as both are key to forecasting accuracy.

A recent study, published by CIGLR and GLERL and other research partners, “Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach,” is the latest in a recent series of studies* (see list below) that help to make lake effect snow forecasts better. This study takes a closer look at how rapid changes in Great Lakes temperatures and ice impact regional atmospheric conditions and lake-effect snow. Rapidly changing Great Lake surface conditions during lake effect snow events are not accounted for in existing operational weather forecast models. The scientists identified a new practical approach for how models communicate that does a better job of capturing rapidly cooling lake temperatures and ice formation. This research can result in improved forecasts of weather and lake conditions. The models connect and work together effectively and yet add very little computational cost. The advantage to this approach in an operational setting is that computational resources can be distributed across multiple systems.

Study model run: This panel of images shows model runs that looks at data from a lake effect snow event from January 2018 with and without the new type of model coupling. The image on the far right labeled Dynamic – Control Jan 06 shows the differences in air temperature (red = warmer, blue = colder) and wind (black arrows) when the models are coupled. The areas in color show how the new model coupling changed the model output considerably and improved the forecast.

Related news articles and blog posts:

From the CIGLR Winter 2020 eNewsletter – Improving Lake Effect Snow Forecasts

NOAA Research News, April 2019 NOAA research yields better lake-effect snow forecasts

NOAA GLERL Blog, 2018 – Improving lake effect snow forecasts by making models talk to each other

Related research papers: 

Fujisaki-Manome et al. (2020) Improvements to lake-effect snow forecasts using a one-way air-lake model coupling approach. 

Anderson et al. (2019) Ice Forecasting in the Next-Generation Great LakesOperational Forecast System (GLOFS) 

Fujisaki-Manome et al. (2017) Turbulent Heat Fluxes during an Extreme Lake-Effect Snow Event

Xue et al. (2016) Improving the Simulation of Large Lakes in Regional Climate Modeling: Two-Way Lake–Atmosphere Coupling with a 3D Hydrodynamic Model of the Great Lakes

map of great lakes showing colors of model output


by 2 Comments

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.