NOAA Great Lakes Environmental Research Laboratory

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

Author Archives: Nicole Rice

photo of building in water with skyline of city in backgroun


by Leave a comment

NOAA and partners team up to prevent future Great Lakes drinking water crisis

A new video SMART BUOYS: Preventing a Great Lakes Drinking Water Crisis released by Ocean Conservancy describes how NOAA forecast models provide advance warnings to Lake Erie drinking water plant managers to avoid shutdowns due to poor water quality.

An inter-agency team of public and private sector partners, working with the Cleveland Water Department, are addressing drinking water safety for oxygen depleted waters (hypoxia). By leveraging NOAA’s operational National Weather Service and National Ocean Service forecast models and remote sensing for the Great Lakes, NOAA’s latest experimental forecast models developed by its Great Lakes Environmental Research Laboratory can predict when water affected by harmful algal blooms and hypoxia may be in the vicinity of drinking water intake pipes. Advance notice of these conditions allows water managers to change their treatment strategies to ensure the health and safety of drinking water.  

“Hypoxia occurs when a lot of organic material accumulates at the bottom of the lake and decomposes. As it decomposes, it sucks oxygen from the water, can discolor the water and allow for metals to concentrate,” explains Devin Gill, stakeholder engagement specialist for NOAA’s Cooperative Institute for Great Lakes Research, hosted at the University of Michigan.

Low dissolved oxygen on its own is not a problem for water treatment. However, low oxygen is often associated with a high level of manganese and iron in the bottom water that then leads to drinking water color, taste, and odor problems. In addition, the same processes that consume oxygen also lower pH and, if not corrected, could cause corrosion in the distribution system, potentially elevating lead and copper in treated water.

“Periodically, this water with depleted oxygen gets pushed up against the shoreline and the drinking water intakes pipes,” said Craig Stow, senior research scientist for NOAA’s Great Lakes Environmental Research Laboratory. “We have buoys stationed at various places and those guide our models to let us know when conditions are right for upwellings that would move this hypoxic water into the vicinity of the drinking water intakes.”

NOAA provides advanced warning of these events so that drinking water plant managers can effectively change their treatment strategies to address the water quality, which is a huge benefit in the water treatment industry.

For more information on NOAA GLERL’s harmful algal blooms and hypoxia research, visit www.glerl.noaa.gov/res/HABs_and_Hypoxia.

A buoy near the Cleveland Water intake—approximately 3.5 miles off the Cleveland shoreline—gives researchers at NOAA GLERL the ability to incorporate water temperature, pH, turbidity, dissolved oxygen and other parameters into their Experimental Hypoxia Forecast model. This allows water treatment managers enough time to anticipate and respond to changes in lake water quality before it is drawn into their treatment plants. Photo Credit: Ed Verhamme, Limnotech
CIGLR Mechanical Technician, Russ Miller, and Research Associate, Dack Stuart retrieve a sediment core from Lake Erie, near the Cleveland Water Intake Crib. Photo Credit: CIGLR
CIGLR Mechanical Technician, Russ Miller, prepares to deploy temperature and dissolved oxygen sensors in Lake Erie, near the Cleveland Water Intake Crib. Photo Credit: CIGLR
Retrospective animation of the Experimental Lake Erie Hypoxia Forecasts from the 2018 season, showing the predicted change in bottom temperature and dissolved oxygen.
Cleveland skyline and Cleveland Water Intake Crib. Daily, about 165 million gallons of water leaves Lake Erie through the crib to the pump station, where the water will begin treatment before it flows to other water facilities. Photo Credit: Ed Verhamme, Limnotech.
CIGLR Associate Director & Research Scientist, Tom Johengen, works hard to understand more about Lake Erie hypoxia by measuring sediment oxygen demand. Photo Credit: Michele Wensman, CIGLR.
The story of hypoxia


by Leave a comment

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.

habtracker2018

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.

43447135081_b893240224_o.jpg

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.


by 1 Comment

Photo story: Using an AUV to track algae in Lake Erie

In late July and early September, during the peak of the 2018 harmful algal bloom in the Western Basin of Lake Erie, NOAA GLERL, NOAA National Centers for Coastal Ocean Science (NCCOS), NOAA Atlantic Oceanographic and Meteorological Laboratory (AOML) and CIGLR researchers teamed up with a group of scientists and engineers from the Monterey Bay Research Institute (MBARI). Their mission: to test how well a third-generation environmental sample processor (3GESP), mounted inside a long-range autonomous underwater vehicle (LRAUV), can track and analyze toxic algae in the Western Basin of Lake Erie. You can read more about the purpose of this project in this great news story by MBARI’s Kim Fulton-Bennett.

Below is a photo story showing all (well, much) of the hard work that went into this test deployment.

First, the new gear had to be shipped from California to the GLERL laboratory in Ann Arbor, Michigan.

On the truck and heading to Michigan!
Ready to go.
It’s load out day!

 

ESP3-b (1)

Upon arrival, Jim Birch, Director of the MBARI SURF (Sensors Underwater Research of the Future) Center, & Bill Ussler, MBARI biogeochemist, got straight to work in GLERL’s Marine Instrumentation Lab.

Image from iOS (6)

The inside of the 3G ESP has a lot of moving parts. Since this is the first time the team is testing it in freshwater, before it can go out, everything needs to be fine-tuned to work in a variety of conditions in Lake Erie (more on that later.)

So. Many. Moving. Parts.

 

Running a test sample.
Cartridges that hold the water sample for both real-time processing, as well as an archive sample for further analysis back in the lab.
The propeller end of the long-range AUV in which the 3GESP will be housed.
More testing and tweaking.
The “guts” of the 3GESP prior to inserting the cartridges.

Image from iOS (7)

Once everything is in working order, the 3GESP gets inserted into an LRAUV or long-range autonomous underwater vehicle (the torpedo-looking thing). This gives the 3GESP the ability to move around in the water all by itself once researchers have set parameters for it. The team has named this particular vehicle, Makai, which is Hawaiian for “toward or by the sea.” Seems appropriate! That’s Brian Kieft, MBARI software engineer, on the right. He plays a crucial role in making sure Makai does her job.

IMG-1263

All hands on deck for a few more tweaks.

testing_b

Once everything is installed tightly, helium is added into the canister to check for leaks. CIGLR engineer, Russ Miller, is working with Jim to fill it up.

Now, the team is ready to head out to Lake Erie. Here’s where things start to get exciting!

 

A kayak? What do we need that for? You’ll see.
Loading the Makai into the van.
Heading to Toledo Beach Marina in La Salle, Michigan .

20180822_134836.jpg

Before the team sets Makai free to track the algal bloom in the Western Basin of Lake Erie, they must first check her ballast and trim. This is especially important for such a shallow lake (relative to where the team has been testing this technology in the deep canyons of of Monterey Bay off the coast of California.)

20180822_133254

Brian has to do all of the hard work.

Image from iOS (8)

Because, science.

Image from iOS (11)

Time to load Makai onto the NOAA vessel, which is stationed in La Salle, Michigan. Captain Kent Baker, a contractor with NOAA, is in the background operating the crane. Kent takes NOAA and CIGLR researchers and technicians out to bi-weekly sampling stations, helps deploy buoys and other instrumentation, and is at the ready for pretty much anything that needs to happen in Lake Erie.

onboat

Once she’s all settled onto the boat, the team takes Makai to the first deployment location.

Screen Shot 2018-08-23 at 12.55.24 PM

The inaugural deployment was set to match up with the bi-weekly sampling stations.

inwater

Look closely and you’ll see Makai off on her way!

Makai and the team spent nearly two weeks tracking, sampling, adjusting, and learning about using this technology to track algal toxins in Lake Erie.

Image from iOS (1) copy

The team used the images from GLERL’s Experimental Lake Erie Harmful Algal Bloom (HAB) Tracker to determine where to send Makai.

Bloom Edge

Then, they would determine how many samples to take, and program her to go to specific waypoints.

Remember when we said this Lake Erie mission will be different than the ones the team has performed in Monterey Bay? Well, here’s one example of how.

20180826_132023

After a few hours of no communication, and a little hunting, this is how the team found Makai. Two problems here: One, with the propellor up and the nose down, Makai cannot transmit data, including her location, as the transmitter only works above water. And, two, well . . .

OLYMPUS DIGITAL CAMERA

The reason she was nose down in the first place is because Lake Erie is pretty shallow, and she’d taken on quite a bit of mud.

20180830_172121

Once she was all cleaned up, the team set Makai out again to complete the rest of her mission.

Once the deployment was over, the research didn’t stop there.

archive_full-liquid

Archive samples were taken so that folks back in the lab could further analyze them.

bps2

Here’s GLERL’s Observing Systems and Advanced Technology (OSAT) branch chief, Steve Ruberg (left), along with Paul Den Uyl, a researcher with CIGLR, helping Bill extract the sample filters from the cartridges.

Deunyl

The filters are being collected for analysis of DNA. The DNA will be extracted from each filter and analyzed. We’re looking at absolute quantity of known microcystin producing toxin genes in samples collected, information on bacterial community composition, and information on eukaryotic organism community composition. The samples will also analyzed through shotgun sequencing. This is where all of the genes in the sample are turned into human readable information and can be combined to make what can be thought of as an organism’s genetic instruction guide (what genes it has). This information will be very helpful in better understanding what causes the algae to be toxic (not all algae is toxic).

 


by Leave a comment

Great Lakes in winter: Water levels and ice cover

The Great Lakes, along with their connecting waterways and watersheds, make up the largest lake system on the planet—more than 20% of the world’s surface freshwater! Water levels on the lakes change in response to a number of factors, and these changes can happen quickly. Changing water levels can have both positive and negative impacts on shipping, fisheries, tourism, and coastal infrastructure like roads, piers, and wetlands.

Currently, water levels on all of the Great Lakes are above their monthly averages, and have been developing since the spring of 2013, when a record-setting two-year rise in water levels began on the upper Great Lakes. Extreme conditions in spring of 2017 produced flooding and widespread damage at the downstream end of the basin—Lake Ontario and the St. Lawrence River. In case you missed it, check out our infographic on this flooding event.

So, what’s happening now that it’s winter?

As we entered the late fall-early winter of 2017-2018, a warm weather pattern had forecasters looking toward a fairly warm winter. However, in late December, the conditions changed and a much colder than normal weather pattern took many folks living in the Great Lakes by surprise. Much like how water levels can change quickly in the Great Lakes, so can ice cover. Due to frigid air temperatures, between December 20 and January 7, total ice cover on the lakes jumped 26.3%. Lake Erie alone jumped up to nearly 90%!

 

 

After January 7th, ice coverage dropped a bit as the air temperatures warmed, then rose again as temperatures went back down, showing again how vulnerable the lakes are to even the slightest changes. Compare where we are now to where we were 2 years ago at this time, and you’ll easily see how variable seasonal ice cover can be in the Great Lakes.

Image depicting Great Lakes total ice cover on on January 15, 2018, compared to 2017 and 2016.

What’s the outlook for ice and water levels?

Below, you’ll find what GLERL researchers expect to see for ice cover this winter, as well as the U.S. Army Corps’ water levels forecast into Spring 2018. Be sure to read further to find out more about the science that goes into these predictions!

—GLERL’s 2018 Seasonal Ice Cover Projection for the Great Lakes—

On 1/3/2018, NOAA’s Great Lakes Environmental Research Laboratory updated the maximum 2018 Great Lakes basinwide ice cover projection to 60%. The long-term average is 55%. The updated forecast reflects changes in teleconnection patterns (large air masses that determine our regional weather) since early December 2017—movement from a strong to a weak La Nina, a negative to a positive Pacific Decadal Oscillation, and a positive to a negative North Atlantic Oscillation. These patterns combine to create colder than average conditions for the Great Lakes.

—Water Levels forecast into spring 2018—

According to the most recent weekly water level update from the U.S. Army Corps, water levels for all of the Great Lakes continue to be above monthly average levels and above last year’s levels at this time. All of the lakes have declined in the last month.  Note that ice developing in the channels and on the lake surface can cause large changes in daily levels during the winter, especially for Lake St. Clair. Over the next month, Lake Superior and Lake Michigan-Huron are expected to continue their seasonal decline. Lake St.Clair, Lake Erie, Lake Ontario are expected to begin their seasonal rise.

 

More information on water levels and ice cover forecasting

How are water levels predicted in the Great Lakes?

Forecasts of Great Lakes monthly-average water levels are based on computer models, including some from NOAA GLERL, along with more than 150 years of data from past weather and water level conditions. The official 6-month forecast is produced each month through a binational partnership between the U.S. Army Corps of Engineers and Environment and Climate Change Canada.

At GLERL, research on water levels in the Great Lakes analyzes all of the components of the Great Lakes water budget. The information we gather is used to improve forecast models. The infographic below goes into more detail about the Great Lakes water budget.

Image depicting the makeup of water budgets in the Great Lakes

How does winter ice cover affect water levels?

As mentioned in the recently released Quarterly Climate Impacts and Outlook for the Great Lakes, water levels in the Great Lakes tend to decline in late fall and early winter, mainly due to reduced runoff and streamflow combined with higher over-lake evaporation caused by the temperature difference between air and water. Factors such as surface water temperatures, long stretches of cold or warm air temperatures, and winds all impact the amount of lake ice cover as well as extreme winter events, such as lake-effect snow—which we’ve already seen plenty of this winter—and vice versa. All of these factors influence winter water levels in the Great Lakes. The timing and magnitude of snow melt and spring runoff will be major players in the spring rise.

Looking for more info?

You can find more about GLERL’s water levels research, on this downloadable .pdf of the GLERL fact sheet on Great Lakes Water Levels.

View current, historical, and projected water levels on the Great Lakes Water Levels Dashboard at https://www.glerl.noaa.gov/data/dashboard/portal.html.

For more on GLERL’s research on ice in the Great Lakes, check out the Great Lakes Ice fact sheet, or check out our website at https://www.glerl.noaa.gov/data/ice/.

Want to see a really cool graphic showing the extent of the maximum ice cover on the Great Lakes for each year since 1973? You’ll find that here.

 


by Leave a comment

“Just Because the Blooms in Lake Erie Slow Down, Doesn’t Mean We Do”

NOAA GLERL harmful algal blooms research program featured on Detroit Public Television

As part of a series on The Blue Economy of the Great Lakes, NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is featured in a short video, produced by Detroit Public Television (DPTV) and published on the DPTV website. The video, which features GLERL and its partners from the Cooperative Institute for Great Lakes Research (CIGLR, known formerly as CILER), describes the advanced technology GLERL uses to monitor, track, predict, and understand harmful algal blooms (HABs) in the Great Lakes. More specifically, the video focuses on efforts in Lake Erie, where over 400,000 people were affected by a 3-day shutdown of the Toledo drinking water treatment facility in 2014. Since then, GLERL and CIGLR have enhanced their HABs research program—much of which is made possible by funding from the Great Lakes Restoration Initiative, or GLRI—to include cutting-edge technologies such as the hyperspectral sensors and an Environmental Sample Processor (ESP), as well as experimental forecasting tools like the Lake Erie HAB Tracker.

In addition to online coverage, the video will be broadcast via DPTV at a future time, yet to be determined.

View the video above, or visit http://bit.ly/2pK2g0J.


by Leave a comment

NOAA research to be highlighted at bi-national Conference on Great Lakes Research

Researchers from NOAA GLERL and the Cooperative Institute for Great Lakes Research (CIGLR), know formerly as CILER, along with other collaborators, will showcase their work on observations, advanced technologies, harmful algal blooms, modeling, forecasting, and more at the 60th Conference on Great Lakes Research on May 15-19th in Detroit, Michigan.

This year’s conference, held by the International Association for Great Lakes Research (IAGLR), is titled “From Cities to Farms: Shaping Great Lakes Ecosystems” and will feature over 660 presentations, plus a poster social with over 160 posters. GLERL and CIGLR authors will showcase their research in more than 50 presentations. In addition, GLERL scientists George Leshkevich, Ashley Baldridge Elgin; Henry Vanderploeg, Philip Chu, Eric Anderson, Brent Lofgren, Jia Wang, Steve Ruberg and Andrew Gronewold; CIGLR’s Dima Beletsky and Tom Johengen; and Great Lakes Sea Grant’s Rochelle Sturtevant will co-chair sessions throughout the week. These sessions include:

Here’s a schedule of where you’ll find our scientists and their research throughout the week (presentation titles linked to online abstract and poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA and CIGLR exhibitor tables for more information on our research programs, collaborations, or to grab a copy of one of our recent publications.

For more information you can find .pdf of the full abstract book on the IAGLR conference website.

GLERL and CIGLR posters and presentations during IAGLR 2017

PRESENTATIONS

Tuesday May 16, 2017

8:00A HALL, D.K., NGHIEM, S.V., GUNN, G.E., LESHKEVICH, G., HELFRICH, S.R., CRAWFORD, C.J., KEY, J.R., CZAJKOWSKI, K.P., RIGOR, I.G. and KIM, E.J. GLAWEX’17 – Snow and Ice Field and Aircraft Experiment in Michigan and the Great Lakes
8:40A NGHIEM, S.V., LESHKEVICH, G. and JACKSON, C. Great Lakes Satellite SAR Ice Type Classification and Its Relation to ICECON
9:00A SAYERS, M.J., RUBERG, S.A., LESHKEVICH, G., STUART, D.G., SHUCHMAN, R.A. and ADEN, S.T. Spatial and Temporal Patterns of Inherent Optical Properties in Western Lake Erie for 2015 and 2016
9:40A BOSSE, K.R., SHUCHMAN, R.A., SAYERS, M.J., SCHWAB, D.J. and LESHKEVICH, G. Developing A Daily Composite Product for Water Quality Parameters in the Great Lakes
2:40P LESHKEVICH, G. and LIU, S. Great Lakes CoastWatch – New Data Sets and New Data Servers
9:00A GILL, D.G., JOSHI, S.J., and ROWE, M. Understanding the Potential Utility of the HAB Tracker Forecast Model for Western Lake Erie Anglers
10:00A STOW, C.A., ROWE, M.D., RUBERG, S.A., JOHENGEN, T.H., ZHANG, H., BELETSKY, D., JOSHI, S.J., COLLINGSWORTH, P., MASON, D.M., KRAUS, R.T. and ANDERSON, E.J. Lake Erie Hypoxia Forecasting for Public Water Systems Decision Support
4:20P GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F., RUTHERFORD, E.S., WELLS, D.J., NASH, R.D.M. and GEFFEN, A.J. Potential effects of UV radiation on vertical distribution of zooplankton in Southeast Lake Michigan
4:40P CARRICK, H., RUDSTAM, L.G., WARNER, D. and VANDERPLOEG, H.A. Plankton dynamics in Lake Michigan along a near to offshore gradient in Lake Michigan
4:40P QIAN, S.S. and STOW, C.A. A Risk Forecasting Model of Cyanobacterial Toxin for Western Lake Erie

Wednesday, May 17, 2017

10:00A MARINO, J.A., PEACOR, S.D., BUNNELL, D.B., VANDERPLOEG, H.A., POTHOVEN, S.A., ELGIN, A.K., and IONIDES, E.L. Fitting models to field time series data to quantify Bythotrephes effects in Lake Michigan
10:20A ELGIN, A.K., BURLAKOVA, L.E., KARATAYEV, A.Y., MEHLER, K. and NALEPA, T.F. Quagga Mussel Body Condition and Size Distribution Inform Recent Lake Michigan Population Trends
11:20A VANDERPLOEG, H.A., SARNELLE, O., DENEF, V.J., CARRICK, H., ELGIN, A.K., ROWE, M.D., RUTHERFORD, E.S. and POTHOVEN, S.A. Food-web impacts of Dreissena are Context-dependent: Mapping out a New Research Agenda
2:00P STURTEVANT, R.A., BERGERON, D. and BUNTING-HOWARTH, K.E. Stakeholder Engagement in a Wicked World:Crude Oil Transport in the Great Lakes Region
8:20A ROWE, M.D., ANDERSON, E.J., RUBERG, S.A., MOEGLING, S., VERHAMME, E.M., BELETSKY, D., ZHANG, H., JOHENGEN, T.H. and STOW, C.A. Modeling Dissolved Oxygen Dynamics Near Drinking Water Intakes in the Central Basin of Lake Erie
8:40A HAWLEY, N., BELETSKY, D., WANG, J. and CHU, P. Time series measurements of ice thickness in Lake Erie, 2010-2011
10:00A ANDERSON, E.J., LANG, G.A., CHU, P., FUJISAKI-MANOME, A. and WANG, J. Development of the Next-Generation Lake Michigan-Huron Operational Forecast System (LMHOFS)
11:00A HOFFMAN, D.K., MCCARTHY, M.J., DAVIS, T.W., GOSSIAUX, D.C., BURTNER, A.M., JOHENGEN, T.H., PALLADINO, D.A., GARDNER, W.S., MYERS, J.A. and NEWELL, S.E. Water Column Ammonium Dynamics Affecting Harmful Cyanobacterial Blooms in Lake Erie
2:00P ZHANG, H., ROWE, M.D., JOHENGEN, T.H., ANDERSON, E.J. and RUBERG, S.A. Modeling succession of algal functional groups associated with Lake Erie harmful alga blooms
3:40P LIU, Q., ANDERSON, E.J. and BIDDANDA, B.A. A Physical-Biogeochemical Simulation of Muskegon Lake
3:40P LOFGREN, B.M. and XIAO, C. Influence of Greenhouse Gas Concentrations on Lake Phenology and Temperature Profiles
4:00P HU, H., WANG, J., LIU, H. and GOES, J. Simulation of Phytoplankton Distribution and Variation in the Bering-Chukchi Sea using a 3D Physica
4:20P RUTHERFORD, E.S., GEFFEN, A.J., NASH, R.D.M., WELLS, D.J., GLYSHAW, P., VANDERPLOEG, H.A., CAVALETTO, J.F. and MASON, D.M. Have Invasive Species Caused Changes in Larval Fish Density and Distribution in SE Lake Michigan?
4:40P FUJISAKI-MANOME, A., WANG, J. and ANDERSON, E.J. Modeled ice thickness in Lake Erie with different parameterizations of the ice strength
5:00P KESSLER, J.A., WANG, J., MANOME, A.F. and CHU, P. Modeling the Great Lakes with FVCOM+UGCICE

Thursday, May 18, 2017

8:00A WANG, J., KESSLER, J.A., HU, H., FUJISAKI-MANOME, A., CLITES, A., LOFGREN, B.M. and CHU, P. Seasonal forecast of Great Lakes ice cover using multi-variable regression and FVCOM+ice models
8:20A XUE, P., CHU, P., YE, X. and LANG, G.A. Improve Lake Erie Thermal Structure Predictions using Data Assimilative Hydrodynamic Model
8:40A CHU, P., ANDERSON, E.J., LOFGREN, B.M., GRONEWOLD, A.D., WANG, J., STOW, C.A., LANG, G.A., HUNTER, T. and CLITES, A. Towards an Integrated Environmental Modeling System for the Great Lakes
9:00A YE, X., XUE, P., PAL, J.S., LENTERS, J.D. and CHU, P. Coupling a Regional Climate Model with a 3-D Hydrodynamic Model over the Great Lakes
10:00A LUCIER, H.M., HAWLEY, N. and CHU, P. Developing a long-term database of water temperature measurements in the Great Lakes
2:00P PEI, L., HUNTER, T., BOLINGER, R. and GRONEWOLD, A.D. Applying Climate Change Projections in Great Lakes Regional Water Management Decisions
2:40P DAVIS, T.W., ROWE, M.D., ANDERSON, E.J., VANDERWOUDE, A., JOHENGEN, T.H., RUBERG, S.A., STUMPF, R.P. and DOUCETTE, G. Combining advanced technologies to develop an early warning system for HABs in western Lake Erie
3:00P MUZZI, R.W., RUBERG, S.A., BEADLE, K.S., CONSTANT, S.A., DAVIS, T.W., JOHENGEN, T.H., LUCIER, H.M. and VERHAMME, E.M. Observations of the distribution of phytoplankton during cyanobacteria blooms using an AVP
3:00P STURTEVANT, R.A., MARTINEZ, F., RUTHERFORD, E.S., ELGIN, A.K., SMITH, J.P. and ALSIP, P. Update on the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS)
3:40P VANDER WOUDE, A.J., MILLER, R.J., JOHENGEN, T.H. and RUBERG, S.A. Variability in Lake Erie by Integrating Hyperspectral Imagery, AUV’s and a Shipboard Underway System
4:00P STUART, D.G., BURTNER, A.M., JOHENGEN, T.H., MILLER, R.J., PALLADINO, D.A. and RUBERG, S.A. Trends In Nitrate, Phosphate And Bloom Indicators During The 2016 Western Lake Erie Field Season
4:20P RUBERG, S.A., CONSTANT, S.A., MUZZI, R.W., MILLER, R.J. and SMITH, J.P. Utilization of PostgreSQL Database for Real-Time Western Lake Erie Data Storage and Dissemination
4:40P JOHENGEN, T.H., PAIGE, K., RUBERG, S.A., TWISS, M.R. and PEARSON, R. State of the Science for Great Lakes Observations: Conclusions from the 2016 CILER Symposium
4:40P MASON, L., SAMPSON, K., DUGGER, A., GOCHIS, D., RISENG, C.M. and GRONEWOLD, A.D. Development of a new geospatial hydrofabric to support advanced hydrological modeling
5:00P LEE, D.H. The application of hydroclimate science to Lake Ontario-St. Lawrence River System regulation

Friday, May 19, 2017

8:00A FRY, L.M., GRONEWOLD, A.D., BOLINGER, R. and MUELLER, R. Assessment of Probabilistic 5-year Forecasts of Great Lakes Levels and Outflows for Hydropower
10:00A GRONEWOLD, A.D. and SMITH, J.P. Great Lakes water budget modelling and uncertainty estimation under a Bayesian MCMC framework
10:40A QUINN, F.H., CLITES, A. and GRONEWOLD, A.D. Reconciling Discontinuity of Temporal Flow Measurements for the Detroit River
11:20A LABUHN, K.A., CALAPPI, T.J., GRONEWOLD, A.D., ANDERSON, E.J. and KOWALSKI, P.J. Optimizing Water Levels in the Grass Island Pool for Hydropower Production on the Niagara River

POSTERS 

Wednesday, May 17, 2017

Using the Fluoromarker Calcein to Assess Growth Rates of Quagga Mussels in situ.

MABREY, K. (1), GLYSHAW, P. (1) and ELGIN, A.K. (2) – (1) CILER University of Michigan, G110 Dana 440 Church St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 1431 Beach St., Muskegon, MI, 49441, USA.

The quagga mussel (Dreissena r. bugensis) exerts a profound effect on the Lake Michigan food web. Understanding quagga mussel growth habits in situ will help us to better predict population growth and anticipate ecosystem effects. One technique used to mark mollusk species for growth experiments is exposure to the fluoromarker calcein. Studies on calcein are more common in marine conditions; less has been reported for freshwater species, let alone in natural environments. We conducted a field experiment at 45m in Lake Michigan to assess if calcein is an effective and noninvasive marking technique for quagga mussels. Mussels from two size groups were assigned to calcein or no calcein treatments, measured, then placed in replicate mesh cages attached to a tripod mooring platform 0.4m above the lakebed. We removed cages to remeasure mussels after 5 and 12 months. We captured fluorescent images using a dark box with a blue filter on the light source and a yellow filter on the camera. New shell growth beyond the calcein mark was visually delineated and measured using ImagePro Premier(v9.1) software. Preliminary results indicate that small and large quagga mussels respond differently to exposure to calcein. Studies using calcein will need to take these artifacts into account when measuring dreissenid mussel growth rates

Shock and Awe! Estimating Mysis Density and Catch Avoidance using the MOCNESS in SE Lake Michigan

WELLS, D.J.(1), PSAROUTHAKIS, Z.(1), RUTHERFORD, E.S.(2), CHIN, T.(1), VANDERPLOEG, H.A.(2), CAVALETTO, J.F.(2) and GLYSHAW, P.1(1) – (1) Cooperative Institute for Limnology and Ecosystems Research, 440 Church Street, Ann Arbor, MI, 48109, USA; (2) NOAA GLERL, 4840 S. State Rd., Ann Arbor, MI, 48108, USA

Mysis diluviana is a key member of Great Lakes aquatic food webs and is important prey for pelagic planktivores. Mysis are visual predators of zooplankton, and migrate diurnally from the lake bottom into the water column. Mysis biomass estimates are highly variable but critical for food web models that inform salmonid stocking decisions. In 2016, we evaluated catch efficiency of Mysis in a Multiple Opening/Closing Net and Environmental Sensing System (MOCNESS) with LED strobe lighting that is used to sample plankton and fish larvae in marine waters. We sampled Mysis density at a mid-depth (45 m) station in June and July, and at an offshore (110m) station in September off Muskegon, MI. We made replicate tows in thermally stratified depth layers during day and night, and compared Mysis densities sampled with the LED strobe on vs strobe off. There were no significant differences in Mysis density among depths in samples with strobe on or off in any month. Mysis were concentrated in dense layers in the metalimnion at night, and were highest in the hypolimnion during day. We conclude that the strobe flash light had no effect on catch avoidance by Mysis.

Forecasting Lake Levels Under Climate Change: Implications of Bias Correction.

CHANNELL, K.E.(1), GRONEWOLD, A.D.(2), XIAO, C.(3), ROOD, R.B.(1), LOFGREN, B.M.(2) and HUNTER, T.(2) – (1) University of Michigan Climate and Space Sciences and Engineering, Ann Arbor, MI, USA; (2) NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3) Cooperative Institute for Limnology and Ecosystems Research, Ann Arbor, MI, USA.

Numerical models can provide a basis for projecting future water levels for the Great Lakes under climate change. Hydrological components critical to generating water supplies were extracted from the WRF/GFDL-CM3 downscaled climate model, and were then used to drive a routing model to produce water levels for the 21st century. A new method of bias correction was used to provide a more consistent representation of seasonality, trends, and variability, when compared to more conventional methods. Here we demonstrate the relative differences in hydrology projections from our method and previously used methods. Our results indicate that the bias correction method used is an important source of variability in water level projections. This is a source of variability that is perhaps just as important as choice of models, emission scenarios, etc., but is commonly overlooked.

Simulating and forecasting seasonal ice cover

JI, X.(1), ROOD, R.B.(1), DAHER, H.(2), GRONEWOLD, A.D.(2) and BOLINGER, R.(2)
(1) Climate and Space Science and Engineering, University of Michigan, 2455 Hayward St, Ann Arbor, MI, 48109, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S State Rd, Ann Arbor, MI, 48108, USA.

Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential water bodies. Successfully projecting future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Great Lakes. Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

Implementation of the WRF-Hydro Model in the Great Lakes Region

XIAO, C.(1), LOFGREN, B.M.(2), GRONEWOLD, A.D.(2), GOCHIS, D.(3), MASON, L.(1) and PEI, L.(3) – (1) Cooperative Institute for Limnology and Ecosystems Research (CILER), University of Michigan, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (2) NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3) National Center for Atmospheric Research (NCAR), P.O. Box 3000, Boulder, CO, 80307, USA.

As a physics-based, spatially-distributed hydrologic modeling system, the community Weather Research and Forecasting model (WRF) hydrological extension package (WRF-Hydro) has been used in several streamflow prediction applications in the U.S. and around the world, including the National Water Model (NWM) at the newly established NOAA National Water Center. However, because of lack of consistency of the geofabric data along the U.S. and Canada borders, the Great Lakes basin is not entirely included in NWM, leaving a substantial gap for applying the national model to the water-dominated region. Thus, a specific effort has been devoted to implementing the WRF-Hydro modeling system in the Great Lakes basin, including preparing high-resolution terrain datasets, parameterizing lakes and reservoirs, and calibrating the model. Two experiments have been carried out to support implementation of the NWM in the Great Lakes basin: an offline WRF-Hydro simulation forced by NLDAS2 and a coupled WRF/WRF-Hydro simulation. The model results are validated against observations in terms of precipitation, runoff, soil moisture, channel flow, and land surface heat fluxes. Our preliminary study presented here shows that the WRF-Hydro model is capable of reproducing the land-hydro-air feedbacks in the Great Lakes region.

Investigation into Recent Meteotsunami Events in the Great Lakes.

ANDERSON, E.J.(1), BECHLE, A.J.(2), WU, C.H.(2), CHU, P.(1), MANN, G.E.(3), SCHWAB, D.J.(4) and LOMBARDY, K.(5) –  (1) NOAA GLERL, 4840 S. State Rd, Ann Arbor, MI, 48104, USA; (2)University of Wisconsin-Madison, Madison, WI, USA; (3) NWS WFO-Detroit, Detroit, MI, USA; (4) University of Michigan, Water Center, Ann Arbor, MI, USA; (5) NWS WFO-Cleveland, Cleveland, OH, USA.

Meteotsunami events have been documented in several countries around the world in the coastal ocean, semi-enclosed basins, and in the Great Lakes. In particular, investigations in the Great Lakes have raised the issue of dangers posed by enclosed basins due to the reflection and interaction of meteotsunami waves, in which the destructive waves can arrive several hours after the atmospheric disturbance has passed. This disassociation in time and space between the atmospheric disturbance and resultant meteotsunami wave can pose a significant threat to the public. In recent events in the Great Lakes, atmospheric conditions have induced meteotsunami waves in Lake Erie and Lake Superior. The resulting waves impacted swimmers, inundated a marina, flooded coastal communities. In this work, we attempt to explain the processes that led to these conditions through a combination of atmospheric and hydrodynamic modeling and an analysis of the observed meteorology. Results from a high-resolution atmospheric model and hydrodynamic model reveal the formation of destructive waves resulted from a combination of wave reflection, focusing, and edge waves, though important differences have been found between recent events in the Great Lakes.

Reconstructing evaporation over Lake Erie during the historic November 2014 lake effect snow event

FITZPATRICK, L.E.(1), FUJISAKI-MANOME, A.(1), GRONEWOLD, A.D.(2), ANDERSON, E.J.(2), SPENCE, C.(5), CHEN, J.(4), SHAO, C.(4), POSSELT, D.(3), WRIGHT, D.(3), LOFGREN, B.M.(2) and SCHWAB, D.J.(3),- (1)Cooperative Institute for Limnology and Environmental Research, Ann Arbor, MI, USA; (2)Great Lakes Environmental Research Laboratory, Ann Arbor, MI, USA; (3)University of Michigan, Ann Arbor, MI, USA; (4)Michigan State University, East Lansing, MI, USA; (5)Environment Climate Change Canada, Gatineau, QC, CANADA.

The extreme North American winter storm of November 2014 triggered a record lake effect snowfall (LES) event in southwest New York. This study examined the evaporation from Lake Erie during the record LES event between November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms and three different meteorological forcings. A few non-FVCOM model outputs were also included for further evaporation analysis. Model-simulated water temperature and meteorological forcing data were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from NOAA’s NOHRSC showed a spike in water content on the November 20th. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake, however, the overall analysis showed significant evaporation from Lake Erie appeared to be the main contribution to the LES event.

MCMC modelling with JAGS and applications in the Great Lakes

SMITH, J.P.(1), MASON, L.(2), QIAN, S.S.(3) and GRONEWOLD, A.D.(4) – (1)CILER, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48109-1041, USA; (2)University of Michigan, School of Natural Resources, 440 Church St., Ann Arbor, MI, 48109-1041, USA; (3)The University of Toledo, Department of Environmental Sciences, 2801 West Bancroft St., Toledo, OH, 43606-3390, USA; 4NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108-9719, USA.

Markov Chain Monte Carlo (MCMC) methods have made realistic modeling possible and are widely used in areas such as genetics, ecology, biostatistics, economics. Software packages, such as Just Another Gibbs Sampler (JAGS), have made MCMC accessible to many scientists, engineers, and other professionals looking to utilize the method. This high-level talk discusses the theory, the JAGS package, and a few applications including analyzing the effects of climate change on Great Lakes ice cover and water budget modelling.

Effect of light exposure and nutrients on buoyancy of Microcystis colonies.

MING, T.(1), VANDERPLOEG, H.A.(2), ROWE, M.D.(3), FANSLOW, D.L.(2), STRICKLER, J.R.(4), MILLER, R.J.(3), JOHENGEN, T.H.(3), DAVIS, T.W.(2) and GOSSIAUX, D.C.(2), – (1)School of Natural Resources & Environment, University of Michigan, 440 Church St., Ann Arbor, MI, 48109, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 S. State Rd., Ann Arbor, MI, 48108, USA; (3)Cooperative Institute for Limnology and Ecosystems Research, 440 Church St., Ann Arbor, MI, 48109, USA; (4)Great Lakes WATER Institute, University of Wisconsin – Milwaukee, 600 E. Greenfield Ave., Milwaukee, WI, 53204, USA.

Understanding the vertical distribution of Microcystis spp. is important for improving satellite-derived estimates of bloom biomass and for predicting the transport of blooms. For example, the Lake Erie HAB Tracker forecast model is initialized from satellite imagery, then predicts the transport and vertical distribution of harmful algal blooms (HABs) in Lake Erie over a five-day period. To improve vertical distribution predictions, we used novel videographic methods to determine effects of light intensity, colony size, as well as dissolved and particulate nutrient concentrations on the buoyant velocities of Microcystis colonies collected from western Lake Erie. We incubated whole water samples in two 2L borosilicate bottles in an outdoor incubator maintained at ambient lake temperatures. Light levels were varied to represent day and night conditions for a surface scum or turbulent mixed layer distributions. After an overnight dark adaption, subsamples from each bottle were collected in the morning and evening, then buoyant velocities were measured. In general, colonies were positively buoyant with rates increasing with colony size. However, varying nutrient and light conditions differentially impacted buoyancy rates of Microcystis colonies.

Skill Assessment of the Lake Erie HAB Tracker Forecast Model using Variable Spatial Neighborhoods.

OUYANG, W.(1), ROWE, M.D.(2) and ZHANG, H.(2), – (1)School of Natural Resources and Environment, University of Michigan, Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA; (2) Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, G110 Dana Building 440 Church Street, Ann Arbor, MI, 48108, USA.

Forecasts of harmful algal bloom (HAB) spatial distribution are useful to public water systems, anglers, and recreational boaters. The Lake Erie HAB Tracker model is initialized from satellite-derived HAB spatial distribution, then uses a hydrodynamic forecast to predict the transport and vertical distribution of HABs in Lake Erie over a 5-day period. The model was assessed previously using pixel-by-pixel skill statistics; however, such statistics produce large penalties for small spatial mismatch between simulated and observed fields. Here, we used an alternative approach, fractions skill score (FSS), which has been used in precipitation forecast skill assessment. FSS assesses model skills over a series of increasing spatial neighborhood sizes. Model skill may improve with increasing neighborhood size if spatial mismatch between simulated and observed fields is a problem. We calculated FSS for a series of 26 hindcast simulations from 2011. We compared model skill to a benchmark persistence forecast, which assumed no change from the initial satellite image. Model skill exceeded that of the persistence forecast initially, but the advantage decreased at day 7. Model skill was greatest at 1 km neighborhood size for days 1-2, but improved at neighborhood size of 3-5 km for days 3-6, a period when the forecast is more challenging.

Hydrodynamics of Western Lake Erie.

BELETSKY, D.(1), ANDERSON, E.J.(2) and BELETSKY, R.(1) – (1)University of Michigan, Ann Arbor, MI, USA; (2) NOAA GLERL, Ann Arbor, USA

Prediction of harmful algal blooms in Lake Erie depends on the accuracy of hydrodynamic models that provide information on lake circulation and temperature. Until recently, long-term observations of circulation in the western basin were practically non-existent, but in summer 2015 GLERL deployed 4 ADCPs that revealed highly variable circulation patterns. To evaluate model skill and better understand the dynamics of Lake Erie’s western basin, we compare FVCOM model results with current and temperature observations conducted in Lake Erie.

The Great Lakes Aquatic Nonindigenous Species Information System Watchlist.

ALSIP, P.(1), RICE, N.M.(2), IOTT, S.(1), STURTEVANT, R.A.(3), MARTINEZ, F.(4) and RUTHERFORD, E.S.(2), – (1)Cooperative Institute for Limnology and Ecosystems Research, 4840 South State Road, Ann Arbor, MI, 48108, USA; (2)NOAA Great Lakes Environmental Research Laboratory, 4840 South State Road, Ann Arbor, MI, 48108, USA; (3)Great Lakes Sea Grant Network, 4840 South State Road, Ann Arbor, MI, 48108, USA; (4)NOAA NCCOS, 4840 South State Road, Ann Arbor, MI, 48108, USA.

The Laurentian Great Lakes is one of the most heavily invaded aquatic systems in the world with over 180 documented aquatic nonindigenous species, the peak invasion rate was estimated to be 2.8 species introduced per year (1990-1995). While invasion rates have slowed in recent years, prevention remains the best defense. The Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) currently serves information for 67 species which have been identified through the peer-reviewed scientific literature as having some likelihood of invading the Great Lakes. This poster was developed primarily as an outreach tool to help scientists and citizens know what to look for in monitoring for the presence of these species. Monitoring is essential both to determine the success of prevention programs and to support early detection – early enough to make true rapid response and eradication feasible. The poster links back to the GLANSIS database, which includes further information about the potential for introduction, establishment, and impact of these species as well as more detailed information on how to identify the species and information on management options in the event they are detected.

NOAA booth at annual American Meteorological Society meeting.


by Leave a comment

GLERL researchers heading to AMS 2017

The American Meteorological Society’s Annual Meeting (AMS 2017) is upon us and researchers from GLERL and CILER (the Cooperative Institute for Limnology and Ecosystems Research), along with other partners, are hitting the grounds running on Monday with posters and presentations on climate, ice, HABs, modeling, forecasting, transitioning research to ops, and more!

Here’s a schedule of where you’ll find us throughout the week. (GLERL and CILER researchers highlighted in italics. Poster titles linked to .pdf of poster, if available.) And, don’t forget to swing by the NOAA booth (#405) to check out all of the fantastic work that NOAA scientists are doing around the world!

GLERL and CILER posters and presentations during AMS 2017

Monday, 23 January 2017

The Great Lakes Adaptation Data Suite: Providing a Coherent Collection of Climate Data for the Great Lakes Region
Type: Poster
Location: 4E (Washington State Convention Center), Poster #1
Authors: Omar C. Gates, University of Michigan, Ann Arbor, MI; and K. Channell, D. Brown, W. Baule, D. J. Schwab, C. Riseng, and A. Gronewold

Abstract: Climate change impacts are a growing concern for researchers and adaptation professionals throughout society. These individuals look to different data sources in order to contemplate the challenges that are present from climate impacts. The use of observational data helps to understand which climatic factors exploit vulnerabilities and to develop solutions to make systems more resilient. However, non-uniform data collection and processing often hinders the progress towards such a goal because many publicly-accessible data sets are not readily usable to address the concern of climate impacts on societies. In the Great Lakes region, there is the added challenge of data quality and coverage issues for over-lake versus over-land observations. The creation of the Great Lakes Adaptation Data Suite (GLADS) aims to resolve these dilemmas by providing processed over-land and over-lake observations within one suite for the Great Lakes region of North America, and this data suite is provided to individuals with a vested interest in decision-making for climate resilience. This intent serves as a way for the GLADS to engage with individuals, from various backgrounds, that are interested in incorporating climate information into their work. Feedback from this audience will be analyzed to further improve the GLADS for use in decision-making. Further analysis will look at the connections among potential users and how they perceive the GLADS as being a useful tool for their research. By gaining perspective into the individuals’ expectations of the tool and their understanding of climate information, the GLADS will be able to accommodate the necessary steps for integrating climate information into decision-making processes to mitigate climate impacts.

Tuesday, 24 January 2017

Coupling Effects Between Unstructured WAVEWATCH III and FVCOM in Shallow Water Regions of the Great Lakes
Type: Presentation
Time: 9:15 AM
Locations: Conference Center: Chelan 4 (Washington State Convention Center )
Authors: Jian Kuang, IMSG@NOAA/NWS/NCEP, College Park, MD; and A. J. Van der Westhuysen, E. J. Anderson, G. Mann, A. Fujisaki, and J. G. W. Kelley

Abstract: The modeling of waves in shallow environments is challenging because of irregular coastlines and bathymetry, as well as complicated meteorological forcing. In this paper, we aim to provide insight into the physics of storm surge-wave interaction within shallow water regions of the Great Lakes under strong wind events. Extensive hindcast analysis using the 3D-circulation model FVCOM v3.2.2 and the third generation spectral wave model WAVEWATCH III v4.18 was conducted on unstructured meshes for each of the Great Lakes. The circulation and wave models are coupled through a file-transfer method and tested with various coupling intervals. We conducted tests for five short-term (storm length) test cases and three long-term (seasonal) test cases. Time series, spatial plots and statistics are provided. Data exchange of radiation stress, water elevation and ocean currents were tested in both two-way and one-way coupling regimes in order to assess the influence of each variable. Three types of wave current parametrizations will be discussed (surface layer, depth-averaged, and hybrid). The meteorological input forcing fields are 1km/4km/12km WRF model results with time interval of 1h for 4km/12km resolution and 10min for 1km resolution. Statistical analysis was performed in order to evaluate the model sensitivity on the unstructured domain in terms of wind, physics packages and surge-wave coupling effects. These efforts are towards an assessment of the model configuration with a view toward future operational implementation.

Linking Hydrologic and Coastal Hydrodynamic Models in the Great Lakes
Type: Presentation
Time: 2:00pm
Location: Conference Center – Chelan 4 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and A. Gronewold, L. Pei, C. Xiao, L. E. Fitzpatrick, B. M. Lofgren, P. Y. Chu, T. Hunter, D. J. Gochis, K. Sampson, and A. Dugger

Abstract: As the next-generation hydrologic and hydrodynamic forecast models are developed, a strong emphasis is placed on model coupling and the expansion to ecological forecasting in coastal regions. The next-generation NOAA Great Lakes Operational Forecast System (GLOFS) is being developed using the Finite Volume Community Ocean Model (FVCOM) to provide forecast guidance for traditional requirements such as navigation, search and rescue, and spill response, as well as to provide a physical backbone for ecological forecasts of harmful algal blooms, hypoxia, and pathogens. However, to date operational coastal hydrodynamic models have minimal or no linkage to hydrologic inflows and forecast information. As the new National Water Model (NWM) is developed using the Weather Research and Forecasting Hydrologic model (WRF-Hydro) to produce forecast stream flows at nearly 2.7 million locations, important questions arise about model coupling between the NWM and coastal models (e.g. FVCOM), how this linkage will impact forecast guidance in systems such as GLOFS, and how WRF-Hydro stream flows compare to existing products. In this study, we investigate hindcasted WRF-Hydro stream flows for the Great Lakes as compared to existing legacy research models. These hydrological stream flows are then linked with the next-generation FVCOM models, where the impacts to hydrodynamic forecast guidance can be evaluated. This study is a first step in coupling the next-generation NWM with NOAA’s operational coastal hydrodynamic models.

Regional Hydrological Response from Statistically Downscaled Future Climate Projections in the 21st Century
Type: Poster
Location: 4E (Washington State Convention Center), Poster #462
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, T. Hunter, and R. Bolinger

Abstract: Understanding how future climate change signals propagate into hydrological response is critical for water supply forecasting and water resources management. To demonstrate how this understanding can be improved at regional scales, we studied the hydrological response of the Laurentian Great Lakes under future climate change scenarios in the 21st century using a conventional regional hydrological modeling system (the Great Lakes Advanced Hydrologic Prediction System, or GL-AHPS) forced by statistically downscaled CMIP5 (Coupled Model Intercomparison Project Phase 5) future projections. The Great Lakes serve as a unique case study because they constitute the largest bodies of fresh surface water on Earth, and because their basin is bisected by the international border between the United States and Canada, a feature that complicates water level and runoff modeling and forecasting. The GL-AHPS framework is specifically designed to address these unique challenges. Existing model validation results indicate that the GL-AHPS model framework provides reasonable simulation of historical seasonal water supplies, but has significant deficiencies on longer time scales. A major component of this study, therefore, includes reformulating key algorithms within the GL-AHPS system (including those governing evapotranspiration), and assessing the benefits of those improvements.

Reconstructing Evaporation over Lake Erie during the Historic November 2014 Lake Effect Snow Event
Type: Poster
Location: 4E (Washington State Convention Center), Poster #898
Authors: Lindsay E. Fitzpatrick, CILER, Ann Arbor, MI; and A. Manome, A. Gronewold, E. J. Anderson, C. Spence, J. Chen, C. Shao, D. M. Wright, B. M. Lofgren, C. Xiao, D. J. Posselt, and D. J. Schwab

Abstract: The extreme North American winter storm of November 2014 triggered a record lake effect snowfall event in southwest New York, which resulted in 14 fatalities, stranded motorists, and caused power outages. While the large-scale atmospheric conditions of the descending polar vortex are believed to be responsible for the significant lake effect snowfall over the region, to-date there has not yet been an assessment of how state-of-the-art numerical models performed in simulating evaporation from Lake Erie, which is tied to the accuracy in forecasting lake effect snow.

This study examined the evaporation from Lake Erie during the record lake effect snowfall event, November 17th-20th, 2014, by reconstructing heat fluxes and evaporation rates over Lake Erie using the unstructured grid, Finite-Volume Community Ocean Model (FVCOM). Nine different model runs were conducted using combinations of three different flux algorithms: the Met Flux Algorithm (COARE), a method routinely used at NOAA’s Great Lakes Environmental Research Laboratory (SOLAR), and the Los Alamos Sea Ice Model (CICE); and three different meteorological forcings: the Climate Forecast System version 2 Operational Analysis (CFSv2), Interpolated observations (Interp), and the High Resolution Rapid Refresh (HRRR). A few non-FVCOM model outputs were also included in the evaporation analysis from an atmospheric reanalysis (CFSv2) and the large lake thermodynamic model (LLTM). Model-simulated water temperature and meteorological forcing data (wind direction and air temperature) were validated with buoy data at three locations in Lake Erie. The simulated sensible and latent heat fluxes were validated with the eddy covariance measurements at two offshore sites; Long Point Lighthouse in north central Lake Erie and Toledo water crib intake in western Lake Erie. The evaluation showed a significant increase in heat fluxes over three days, with the peak on the 18th of November. Snow water equivalent data from the National Snow Analyses at the National Operational Hydrologic Remote Sensing Center showed a spike in water content on the 20th of November, two days after the peak heat fluxes. The ensemble runs presented a variation in spatial pattern of evaporation, lake-wide average evaporation, and resulting cooling of the lake. Overall, the evaporation tended to be larger in deep water than shallow water near the shore. The lake-wide average evaporations from CFSv2 and LLTM are significantly smaller than those from FVCOM. The variation among the nine FVCOM runs resulted in the 3D mean water temperature cooling in a range from 3 degrees C to 5 degrees C (6-10 EJ loss in heat content), implication for impacts on preconditioning for the upcoming ice season.

Projecting Water Levels of the Laurentian Great Lakes in the 21st Century from a Dynamical Downscaling Perspective
Type: Presentation
Time: 11:15 AM
Locations: 602 (Washington State Convention Center)
Authors: Chuliang Xiao, University of Michigan, CILER, Ann Arbor, MI; and B. M. Lofgren, J. Wang, P. Y. Chu, and A. Gronewold

Abstract: As the largest group of fresh surface water bodies on earth, the Laurentian Great Lakes have a significant influence on regional climate. Due to the limited spatial resolution of general circulation models (GCMs), the Great Lakes are generally ignored in GCMs. Thus, the technique of dynamical downscaling serves as a practical and important, but challenging solution to the problem of understanding climate impacts and hydrological response in this unique region. Here, we employed the Weather Research and Forecasting model (WRF) with an updated lake scheme to downscale from a GCM with two future greenhouse gas concentration scenarios in the 21st century. Historical validation shows that the WRF-Lake model, with a fine horizontal resolution and a 1-dimensional lake representation, improves the hydroclimatology simulation in terms of seasonal cycles of lake surface temperature, precipitation, and ice coverage. Based on the downscaling results, a hydrologic routing model is performed to project the Great Lakes’ water level changes in 21st century using net basin supply (NBS, calculated as the sum of over-lake precipitation, basin-wide runoff, and lake evaporation) as an input. As the lakes warm and lake ice diminishes, water levels are projected to have persistent and enhanced interannual variations in the presumed climate change. These changes have a range of potential socioeconomic impacts in the Great Lakes region, including changes in hydropower capacity, the length of the commercial shipping season, and the design life of coastal residences and infrastructure.

Wednesday, 25 January 2017

Simulating and Forecasting Seasonal Ice Cover
Type: Poster, #1147
Authors: Xiaolong Ji, University of Michigan, Ann Arbor, MI; and H. Daher, R. Bolinger, A. Gronewold, and R. B. Rood

Abstract: Over the past several decades, dramatic changes in the spatial extent of seasonal and long-term ice cover have been documented for both marine and continential (inland) water bodies. Successfully projecting (and planning for) future changes in global ice cover requires an understanding of the drivers behing these historical changes. Here, we explore relationships between continental climate patterns and regional ice cover across the vast surface waters of the Laurentian Great Lakes. The Great Lakes constitute the largest collective surface of freshwater on Earth, and seasonal variability in ice cover is closely linked with lake heat content, energy fluxes, and water levels (all of which have strong linkages with ecological and socioeconomic stability in the region). Our findings indicate that abrupt historical changes in Great Lakes seasonal ice cover are coincident with historical changes in teleconnections, including both the El Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). We find, in particular, that these teleconnections explain much of the ice cover decline in the late 1990s (coincident with the strong 1997-1998 winter El Nino) and the following persistent period of below-average period of ice that followed. We encode these relationships in a probabilistic model that provides seasonal projections of ice cover areal extent across the Great Lakes, as well as specific spatiotemporal patterns in ice cover at resolutions that align with critical regional human health and safety-related management decisions.

What Does It Take to Transition Six Forecasting Systems into Operations in Ten Years? — Lessons Learned, Myths and Facts
Type: Presentation
Time: 11:15 AM
Location: 608 (Washington State Convention Center)
Authors: Philip Y. Chu, GLERL, Ann Arbor, MI; and E. J. Anderson, G. Lang, J. G. W. Kelley, E. Myers, A. Zhang, J. Xu, and Y. Chen

Abstract: NOAA Great Lakes Operational Forecasting System (GLOFS), developed by the Great Lakes Environmental Research Laboratory and National Ocean Service, has been operational since 2005. A project to upgrade GLOFS, using FVCOM as the core 3-D oceanographic forecast model, has been conducted during the past 3 years involving GLERL, NOS/CSDL and CO-OPS and NCEP Central Operations. The 1st phase of this project has been completed with the operational implementation of a new GLOFS version for Lake Erie on NOAA’s Weather and Climate Operational Supercomputer System in May 2016.

Many lessons were learned from transitioning six forecasting systems to operations in 10 years. On the technical aspects which include hardware, software, systems — we found that keys to successful transition are on 1) methods to harden the software infrastructure to make a robust, automated system; 2) backup and alternative procedures for handling missing or corrupted input data; 3) standardized validation and skill assessment metrics; 4) preparation of complete documentation including validation test reports, standard operating procedures (SOP), and software user guides; 5) adequate near-real-time observations of discharge, and water levels to provide LBCs for the system and 6) field projects in the Great Lakes (i.e. IFYGL) to provide surface and subsurface data for the evaluation of the forecast models during development and testing. In particular, program source codes need to be frozen during the testing, validation and the transition period with proper version control.

In addition to the technical aspects, a successful system transition from the research/development stage into operations also involves non-technical aspects, such as commitment from senior leadership, frequent communications among all involved parties on progress and milestones, training sessions for the system operators and user engagement workshops for the end users.

Applying WRF-Hydro in the Great Lakes Basin: Offline Simulations in the Seasonal Hydrological Responses
Type: Presentation
Time: 4:45 PM
Location: Conference Center – Chelan 2 (Washington State Convention Center )
Authors: Lisi Pei, NOAA, Ann Arbor, MI; and A. Gronewold, D. J. Gochis, K. Sampson, A. Dugger, C. Xiao, L. Mason, B. M. Lofgren, and P. Y. Chu

Abstract: As a unified atmosphere-land hydrological modeling system, the WRF-Hydro (Weather Research and Forecasting model Hydrological modeling extension package) framework is being employed by the NOAA-National Water Center (NWC, Tuscaloosa, AL) to provide streamflow forecasting over the entire CONUS in 250 m resolution from hourly to monthly scale. Currently, efforts are focused on tests and an operational forecast launch on August 16th, 2016. But due to inconsistencies in the land surface hydrographic datasets between U.S. and Canada over the Great Lakes Basin, many of the tributaries feeding the Great Lakes and the major channels connecting the Great Lakes (including the Niagara, St. Clair, and Detroit Rivers) are missing or poorly represented in the current NWC streamflow forecasting domain. Improvements in the model’s current representation of lake physics and stream routing are also critical for WRF-Hydro to adequately simulate the Great Lakes water budget and Great Lakes coastal water levels. To customize WRF-Hydro to the Laurentian Great Lakes Basin using protocols consistent with those used for the current CONUS operational domain, the NOAA-Great Lakes Environmental Research Laboratory has partnered with the National Center for Atmospheric Research (NCAR) and other agencies to develop land surface hydrographic datasets and compatible stream routing grids that connect to the current CONUS operational domain. This research group is also conducting 1-km resolution offline tests with WRF-Hydro based on current best available bi-national land surface geographic datasets to examine the model’s ability to simulate seasonal hydrological response over the Great Lakes (runoff and land-atmosphere fluxes) with its coupled overland flow terrain-routing module, subsurface lateral flow module and channel flow (runoff) module.

Thursday, 26 January 2017

Using the Next-Generation Great Lakes Operational Forecasting System (GLOFS) to Predict Harmful Algal Bloom (HAB) Transport with the HAB Tracker
Type: Presentation
Time: 3:30 PM
Location: 611 (Washington State Convention Center)
Authors: Eric J. Anderson, NOAA/ERL/GLERL, Ann Arbor, MI; and M. Rowe, J. Xu, A. Zhang, G. Lang, J. G. W. Kelley, and R. Stumpf

Abstract: Harmful algal blooms (HAB) plague coastal environments around the world, and particularly in the United States in areas such as the Great Lakes, Florida, Washington, and Maine. In the Great Lakes, shallow embayments such as the western basin of Lake Erie have experienced a period of increasing HAB intensity in recent years, including an event in 2014 where high toxicity levels resulted in a drinking water restriction to nearly 400,000 residents. In order to help decision makers and the public respond to these events, an experimental model has been developed short-term forecasts of HAB concentration and transport. The HAB Tracker uses the next-generation NOAA Lake Erie Operational Forecast System (LEOFS), which is based on the Finite Volume Community Ocean Model (FVCOM). The new FVCOM-based LEOFS model produces hydrodynamic forecast guidance out to 5 days using meteorology from the 3-km HRRR and 2.5 km NDFD. An experimental version of this model also extends the forecast horizon out to 10 days using forecasted meteorology from the GFS. Hourly hydrodynamic conditions (currents, diffusivity, water temperature) are supplied to a three-dimensional Lagrangian particle trajectory model that has been developed to predict HAB transport and vertical migration through the water column. Initial conditions are provided by satellite remote sensing of surface chlorophyll concentration, when available, in which previous nowcasts are used to fill gaps in satellite-derived HAB extent and extend surface concentrations into the water column to produce a three-dimensional field of HAB concentration. In-situ observations of microcystis concentration provide a calibration of particle buoyancy (i.e. colony migration) and a basis for model validation. Results show the three-dimensional HAB Tracker has improved forecast skill out to 10 days over two-dimensional surface concentration forecast products and is better than a persistence forecast out to 5 days.


by 1 Comment

Ice cover on the Great Lakes

The USCGC Mackinaw arrives in Duluth via Lake Superior. March 24, 2014

U.S. Coast Guard Cutter Mackinaw is an icebreaking vessel on the Great Lakes that assists in keeping channels and harbors open to navigation. Here, the USCGC Mackinaw arrives in Duluth via Lake Superior on March 24, 2014. Credit: NOAA
Ice formation on the Great Lakes is a clear sign of winter!

Looking back in time, the lakes were formed over several thousands of years as mile-thick layers of glacial ice advanced and retreated, scouring and sculpting the basin. The shape and drainage patterns of the basin were constantly changing from the ebb and flow of glacial meltwater and the rebound of the underlying land as the massive ice sheets retreated.

The amount and duration of ice cover varies widely from year to year. As part of our research, GLERL scientists are observing longterm changes in ice cover as a result of global warming. Studying, monitoring, and predicting ice coverage on the Great Lakes plays an important role in determining climate patterns, lake water levels, water movement patterns, water temperature, and spring algal blooms.

Doing research to improve forecasts is important for a variety of reasons.

Ice provides us a connection to the past and also serves as a measure of the harshness of current day winter weather. Understanding the major effect of ice on the Great Lakes is very important because ice cover impacts a range of benefits provided by the lakes—from hydropower generation to commercial shipping to the fishing industry. The ability to forecast and predict ice cover is also really important for recreational safety and rescue efforts, as well as for navigation, weather forecasting, adapting to lake level changes, and all sorts of ecosystem research. One great example of the importance of forecasting is illustrated by an incident that occurred in Lake Erie on a warm sunny day in February 2009 when a large ice floe broke away from the shoreline. The floating ice block stranded 134 anglers about 1,000 yards offshore and also resulted in the death of one man who fell into the water. While the ice on the western sections of the lake was nearly 2 feet thick, rising temperatures caused the ice to break up, and southerly wind gusts of 35 mph pushed the ice off shore. Having the ability to forecast how much ice cover there will be, where it may move, and what other factors like temperature, waves, or wind might play a role in what the ice is going to do, is incredibly important to a lot of users.

— GLERL’s 2017 (Dec. 2016 – March 2017) Seasonal Ice Cover Projection for the Great Lakes —

GLERL’s ice climatologist, Jia Wang, along with partners from the Cooperative Institute for Limnology and Ecosystems Research, use two different methods to predict seasonal ice cover for the Great Lakes. One, a statistical regression model, uses mathematical relationships developed from historical observations to predict seasonal ice cover maximum based on the status of several global air masses that influence basin weather. This method forecasts that the maximum ice cover extent over the entire Great Lakes basin, will be 64%. The other forecast method, a 3-dimensional mechanistic model, is based on the laws of physics that govern atmospheric and hydrodynamic (how water moves) processes to predict ice growth in response to forecast weather conditions. This method predicts a maximum ice cover of 44% for the basin this year.

As you can see, the two methods have produced different answers. However, if you look at the last chart here, you’ll see that three of the lakes show good agreement between these two model types–Lakes Michigan, Erie, and Ontario. Continued research, along with the historical data we’ve been monitoring and documenting for over 40 years, will help GLERL scientists improve ice forecasts and, ultimately, improve our ability to adapt and remain resilient through change.

More information!

Below, is the most recent Great Lakes Surface Environmental Analysis (GLSEA) analysis of the Great Lakes Total Ice Cover. GLSEA is a digital map of the Great Lakes surface water temperature (see color bar on left) and ice cover (see grayscale bar on right), which is produced daily at GLERL by Great Lakes CoastWatch. It combines lake surface temperatures that are developed from satellite images and ice cover information provided by the National Ice Center (NIC). This image is the analysis of January 10, 2017 (13%). For the most current analysis, visit https://coastwatch.glerl.noaa.gov/glsea/cur/glsea_cur.png.

GLSEA total ice cover analysis for January 10, 2017

For technical information on GLERL’s ice forecasting program, check out our website here. 

You can also find much of the information in this post, and more, on this downloadable .pdf of the GLERL fact sheet on Great Lakes ice cover.

Want to see a really cool graphic showing the extent of the maximum ice cover on the Great Lakes for each year since 1973? You’ll find that here.

Great Lakes ice cover facts since 1973

94.7% ice coverage in 1979 is the maximum on record.

9.5% ice coverage in 2002 is the lowest on record.

11.5% ice coverage in 1998, a strong El Niño year.

The extreme ice cover in 2014 (92.5%) and 2015 (88.8%) were the first consecutive high ice cover years since the late 1970’s.

14066190766_c1623828fb_o
On March 6, 2014, Great Lakes ice cover was 92.5%, putting winter 2014 into 2nd place in the record books for maximum ice cover. Satellite photo credit: NOAA Great Lakes CoastWatch and NASA.
Hydrilla verticillata. Common Name: Hydrilla.


by 1 Comment

Collaborative team identifies 16 high-risk Great Lakes invaders

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) recently published a very detailed NOAA Technical Memorandum (GLERL-169), which identifies the potential for introduction (getting in), establishment (living and reproducing), and impact (changing the ecosystem in one way or another) of 67 species that were previously identified through peer-reviewed research as being highly likely to invade the Great Lakes basin. The study also identifies a subset of 16 species (5 plants, 6 fishes, 4 invertebrates), which should be considered the highest overall risk to the Great Lakes region (see photo gallery below).

This slideshow requires JavaScript.

The tech memo—titled “A Risk Assessment of Potential Great Lakes Aquatic Invaders“— is the result of a large collaborative effort between partners all throughout the Great Lakes region. The paper was authored by Abigail Fusaro (Wayne State University), Rochelle Sturtevant (NOAA’s Great Lakes Sea Grant Network liaison with GLERL), Ed Rutherford (NOAA GLERL) and others—including 5 student co-authors and more than 30 students who contributed to the literature review, assessment of individual species, and editing of the final report.

A little history on this project

NOAA GLERL—in cooperation with United States Geological Survey (USGS)—has been tracking nonindigenous aquatic species (species that enter a body of water that is outside of the historical range, in other words, they’ve never lived there before) in the Great Lakes system and serving that information through the GLANSIS database  since 2003. Information in the GLANSIS database includes an overview of the species life history, ecology, and invasion history as well as maps of current distribution, comprehensive impact assessments and overviews of management options—all very useful and important information for tracking invaders. An enhancement to the database in 2011* gave researchers the ability to add information on species that pose a risk of invasion, but are not yet established in the Great Lakes. The addition of these assessments, which were previously published in peer-reviewed scientific literature, helps to identify the species that pose the highest overall risk (introduction + establishment + impact). This information is key in that it allows scientists and environmental managers to better monitor for invasions and make decisions about management options in a rapid response situation.

How this is unique

The risk assessment tools developed for GLANSIS apply a consistent approach across all taxonomic groups and vectors, and allow researchers to compare the potential impact of high-risk species with the realized impact of nonindigenous species that are already established.  The tech memo serves as documentation of these tools and approaches as well as examines cross-taxa patterns in risk.  An analysis of the risk assessment method itself and its results will appear in an upcoming issue of Management of Biological Invasions.

For more information on GLANSIS, please contact Rochelle Sturtevant, rochelle.sturtevant@noaa.gov, 734-741-2287.

*This was made possible with funding from the Great Lakes Restoration Initiative.


by 1 Comment

Retrieval of new data from instruments in Manistique River will inform research and decision making

During recent fieldwork, Dr. Philip Chu, scientist at NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and Professor Chin Wu, from the University of Wisconsin Madison, retrieved six water level sensors and one Acoustic Doppler Current Profiler (ADCP) from the Manistique River—a 71.2 mile long river in the Upper Peninsula of Michigan that drains into Lake Michigan.

An ADCP measures water currents with sound by using the Doppler effect— sound wave has a higher frequency, or pitch, when it moves toward you than it does when it moves away. Think of the Doppler effect in action the next time you hear a speeding train pass you by. As the train moves toward you, the pitch of its whistle will be higher. As it moves away, it will be lower. The same effect happens as sound moves through water. The ADCP emits pulses of sounds that bounce off of particles moving through the water. Particles that are moving toward the sensor will produce a higher frequency than those moving away from the sensor. This effect allows the profiler to record data about sediment transport in the river.

After quality control and assurance procedures back in the lab, currents and water level data collected during this deployment, scientists will use the information to research the impacts of meteotsunamis, seiches, and flooding events on sediment transport through the river. The outcomes of this research will then will be used by organizations, such as the U.S. Army Corps of Engineers, for dredging operations on the river with the ultimate goal of improving water quality. (See the Great Lakes Water Quality Agreement for more on why the Manistique River is considered an “Area of Concern.”)

In addition, researchers will use this valuable field data while validating the NOAA next generation Lake Michigan-Huron Operational Forecasting System, one of the forecast systems within the Great Lakes Operational Forecasting System, or GLOFS. GLOFS is a prediction system that provides timely information to lake carriers, mariners, port and beach managers, emergency response teams, and recreational boaters, surfers, and anglers through both nowcast and forecast guidance.

Nowcast vs. Forecast: What’s the difference?

A nowcast is a description of the present lake conditions based on model simulations using observed meteorology. Nowcasts are generated every 6 hours and you can step backward in hourly increments to view conditions over the previous 48 hours, or view animations over this time period.

A forecast is a prediction of what will happen in the future. Our models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to 5 days in the future. These forecasts are run twice daily, and you can step through these predictions in hourly increments, or view animations over this time period.

Professor Wu, along with Dr. Eric Anderson from GLERL, deployed these sensors earlier this summer. As with the majority of GLERL’s projects, this is a collaborative effort. Through the Cooperative Institute for Limnology and Ecosystems Research (CILER), this work is supported by NOAA National Marine and Fishery Service and funded by EPA Great Lakes Restoration Initiative. The University of Wisconsin is one of ten CILER Consortium partners.