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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Scientists Work Around the Clock During Seasonal Lake Michigan Cruise

Last month, scientists from GLERL, the Cooperative Institute for Limnology and Ecosystems Research (CILER), and other university partners took the research vessel Laurentian for a multi-day cruise on Lake Michigan as part of seasonal sampling to assess the spatial organization of the lower food web—spatial organization simply means the vertical and horizontal location where organisms hang out at different times of day, and the lower food web refers to small organisms at the bottom of the food chain.

The research goes on around the clock. Scientists work in shifts, taking turns sleeping and sampling. The Laurentian spends a full 24 hours at each monitoring station, sampling vertical slices of the water column. Sampling at these same stations has been going on since 2010, providing a long-term dataset that is essential for studying the impact of things like climate change and the establishment of invasive species.

Sampling focuses on planktonic (floating) organisms such as bacteria, phytoplankton (tiny plants), zooplankton (tiny animals), and larval fishes which feed on zooplankton. Many of the zooplankton migrate down into deep, dark, cold layers of the water column during the day to escape predators such as fish and other zooplankton. They return unseen to warm surface waters at night to feed on abundant phytoplankton. Knowing where everything is and who eats whom is important for understanding the system.

Our researchers use different sampling tools to study life at different scales. For example, our MOCNESS (Multiple Opening Closing Net Environmental Sampling System) is pretty good at catching larger organisms like larval fish, Mysis (opossum shrimp), and the like. The MOCNESS has a strobe flash system that stuns the organisms, making it easier to bring them into its multiple nets.

The PSS (Plankton Survey System) is a submersible V-Fin (vehicle for instrumentation) that is dragged behind the boat and measures zooplankton, chlorophyll (a measure of phytoplankton), dissolved oxygen, temperature, and light levels. Measurements are made at a very high spatial resolution from the top to the bottom of the water. At the same time fishery acoustics show where the fish are. Together, these two techniques allow us to see where much of the food web is located.

Water samples are taken at various depths and analyzed right on the boat. This is a good way to study microbes such as bacteria and very small phytoplankton. The lower food web has been pretty heavily altered by the grazing of quagga and zebra mussels. Specifically, the microbial food web (consisting of microbes such as bacteria and very small phytoplankton) makes up a larger component of the food web than before mussel invasion, and scientists are working to find out exactly how this has happened.

Check out the photos below for a glimpse of life in the field!

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Central Michigan University students Anthony and Allie are all smiles as they prepare to head out!

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Getting the MOCNESS ready.

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Chief scientist Hank Vanderploeg looks at some data.

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Filtering a water sample—filtering out the big stuff makes it easier to see microbes.

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Paul prepares the fluoroprobe.

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Taking a water sample in the presence of a beautiful sunset!