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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The HAB season is over, but the work goes on

It’s nearly winter here in the Great Lakes—our buoys are in the warehouse, our boats are making their way onto dry land, and folks in the lab are working hard to assess observed data, experiments, and other results from this field season.

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This is a retrospective animation showing the predicted surface chlorophyll concentrations estimated by the Experimental Lake Erie HAB Tracker model during the 2018 season. Surface chlorophyll concentrations are an indicator of the likely presence of HABs. For more information about how the HAB Tracker forecast model is produced and can be interpreted, visit our About the HAB Tracker webpage.

The harmful algal bloom (HAB) season is also long over in the region. The final Lake Erie HAB Bulletin was sent out on Oct. 11, as the Microcystis had declined in satellite imagery and toxins decreased to low detection limits in samples. In the seasonal assessment, sent out by NOAA’s Centers for Coastal Ocean Science on Oct. 26, it was determined that the season saw a relatively mild bloom—despite its early arrival in the lake—and the bloom’s severity was significantly less than that which was predicted earlier in the season. These bulletins and outlooks are compiled using several models. Over the winter, the teams working on the models take what they learn from the previous season, and update their models for future use.

Back in the lab, the HABs team—researchers from both GLERL and the Cooperative Institute for Great Lakes Research (CIGLR)—will spend the winter analyzing data they collected through a variety of observing systems. This summer was packed with the use of new observing technologies, like hyperspectral cameras and the Environmental Sample Processor (in case you missed it, check out this fun photo story of the experimental deployment of a 3rd generation ESP). In addition, GLERL and CIGLR staff maintained a weekly sampling program program, from which scientists are analyzing and archiving samples and conducting experiments.

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Aerial photograph of the harmful algal bloom in Western Basin of Lake Erie on July 2, 2018, (Photo Credit: Aerial Associates Photography, Inc. by Zachary Haslick). Pilots from Aerodata have been flying over Lake Erie this summer to map out the general scope of the algal blooms. In addition to these amazing photos, during the flyovers, additional images are taken by a hyperspectral imager (mounted on the back of the aircraft) to improve our understanding of how to map and detect HABs. The lead researcher for this project is Dr. Andrea VanderWoude, a NOAA contractor and remote sensing specialist with Cherokee Nation Businesses. For more images, check out our album on Flickr.

This lab work is super important for understanding the drivers of toxic algae in the Great Lakes. For instance, in a new study released this month, researchers looking at samples from previous years found that “ . . . the initial buildup of blooms can happen at a much higher rate and over a larger spatial extent than would otherwise be possible, due to the broad presence of viable cells in sediments throughout the lake,” according to the lead author Christine Kitchens, a research technician at CIGLR, who works here in the GLERL lab. This type of new information can be incorporated into the models used to make the annual bloom forecasts.

As you can see, our work doesn’t end when the field season is over.  In spring 2019, when the boats and buoys are back in the water and samples are being drawn from the lakes, researchers will already have a jump on their work, having spent the winter months analyzing previous years, preparing, and applying what they’ve learned to the latest version of the Experimental HAB Tracker, advanced observing technologies, and cutting-edge research on harmful algal blooms in the Great Lakes.