NOAA Great Lakes Environmental Research Laboratory

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

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Andrea VanderWoude blends science and art to study the Great Lakes from the sky

A woman sits in a small airplane with headphones and a mic on, looking out the window at a bay on Lake Michigan Below.

Andrea VanderWoude on a flight over Grand Traverse Bay.

Andrea VanderWoude is a remote sensing specialist — that means she’s looking at things from far away. Whether she’s studying harmful algal blooms or rip currents, her job is to pull information out of pictures taken from airplanes or satellites. What makes her extra good at it? She’s got an artistic streak! Read on to learn more. 

How would you describe your job?

As a remote sensor, I use satellites and airborne cameras to monitor the Great Lakes – specifically harmful algal blooms, rip currents and submerged aquatic vegetation. I am an oceanographer working on the Great Lakes and most people wonder how that is possible. The lakes are so large they behave similarly to the ocean. I coordinate flights out of the Ann Arbor, Michigan airport with a contracted pilot that we work with and we put a small hyperspectral camera in the back of the airplane to take photos of the lakes.

Hyperspectral means that there are many discrete [color] bands or channels that are used (these colors are more detailed than the human eye can see). These channels can be used to map harmful algal blooms, which absorb, scatter and reflect light in a specific way. The hyperspectral camera is also able to fly underneath the clouds where passive sensors on satellites are unable to see. My day is spent programming, writing algorithms to process the images and looking at beautiful imagery. It is a wonderful blend of science and art!

What is the most interesting thing you’ve accomplished in your job?

Every year we fly over the Sleeping Bear Dunes National Lakeshore to monitor submerged aquatic vegetation and specifically for cladophora. As a northern Michigander growing up in that area, it is always amazing to see that area from the sky and to dream about hiking the Manitou Islands again. I also enjoy contributing to aiding the mapping of submerged aquatic vegetation in an area that is personally important to me.

What do you feel is the most significant challenge in your field today?

The most significant challenge I think is keeping up with the changing technology at the speed it is developing at this time. We are working on getting our new hyperspectral camera on an unmanned aerial system (UAS) for rapid response and I am really interested in using UAS’s for frequent monitoring of rip current troughs in the Great Lakes.

Where do you find inspiration? Where do your ideas come from in your research or other endeavors in your job?

I found my inspiration from growing up on the lakes and my parents always made a point of being on the water during all times of the year, either on Lake Michigan or Lake Superior. I have always felt connected to the water and jump in the lake during every month of the year, as a surfer on the Great Lakes. My ideas come from the public and what public needs could be supported. While living on the west side of Michigan, I have really seen the effect of rip currents and was recently stuck in one myself. It was a scary event and even furthered my desire to help warning and detection of rip currents.

How would you advise young women interested in science as a career path, or someone interested in your particular field?

I would advise women to get outside. When asked this question, people frequently turn towards an answer that involves STEM involvement but for me, and I think this also rings true for my Michigan Tech cohorts from undergrad, it was getting outside and learning about the natural world that sparked my interest in science. I was allowed to watch a limited amount of television as a kid and my mom would send me outside to play in the woods. I would spend my time creating forts around trees in the woods or we would go to the lake to swim for hours. This love of the outdoors continued through my undergraduate and graduate degrees with a curiosity to learn how the earth was formed, different rock types or how ocean dynamics and biology could be measured from space.

What do you like to do when you AREN’T sciencing?

I love to bake, learn about different plants, go rock hunting, trail running, rustic camping, stand up paddle boarding and I am newly returning to surfing but on the Great Lakes. I also spend an enormous amount of time with my boys on the beach, searching for cool rocks or treasures on the beach.

What do you wish people knew about scientists or research?

Many scientists also have an artistic outlet as well as their science life. It creates a life-balance. I personally find balance spending my free-time creating art from found objects on the beach, drawing, painting and baking unique pastries. Constantly a life in motion, as a pendulum between science and art.

Dr. Andrea VanderWoude is a contractor and remote sensing specialist with Cherokee Nation Businesses. She is currently working with researchers from NOAA GLERL and the Cooperative Institute for Great Lakes Research.


Casting a high tech sampling net to learn more about the Great Lakes ecosystem


Researchers at GLERL are using a new tool, a MOCNESS, to study the Great Lakes.

In the Great Lakes, communities of plants and animals vary depending on where and when you look. They are dispersed up and down and all around in the water, making it tricky to collect them for research studies. To answer questions about these organisms and how they interact in the Great Lakes ecosystem, scientists from NOAA’s Great Lakes Environmental Research Laboratory (GLERL) and CIGLR (Cooperative Institute for Great Lakes Research) are using a new high tech sampling tool called a MOCNESS (Multiple Opening and Closing Net and Environmental Sensing System).

GLERL’s MOCNESS is the first of its kind to be used in a freshwater system. Scientists are hopeful that this technology will lead to new discoveries about the Great Lake ecosystem, such as where plankton (microscopic aquatic plants and animals) live and what causes their distributions to change over space and time. The MOCNESS will also help scientists learn more about predator-prey interactions that involve zooplankton (microscopic aquatic animals), phytoplankton (microscopic aquatic plants), and larval and juvenile fishes.


A closer look the MOCNESS (Multiple Opening and Closing Net and Environmental Sensing System)

Keeping track of changes in plant and animal communities in the Great Lakes over time is important, especially with changes in climate, the onslaught of invasive species, and land use practices causing increased nutrient runoff into the lakes.

The MOCNESS is a big improvement over the traditional single mesh sized sample collection nets. The sampling system provided by this new tool has a series of nets of different mesh sizes to collect different sized organisms (see a few examples in the gallery below). The operator can remotely open and close these nets, much like an accordion. At the heart of the system is a set of sensors that measure depth, temperature, oxygen, light levels, and the green pigment found in algae, Chlorophyll-a. Because this data can be viewed in real time on the vessel, the operator can better determine what is going on below the water surface and choose where and when to sample different sized organisms.

Here are some of the key questions that the scientists hope to answer using this advanced technology:

  • How do plankton and larval fish respond to environmental gradients (water temperature, dissolved oxygen, UV radiation) over the course of the day, season, and across years?
  • What are the major causes for changing distributions of the animals across space and over time (long-term, seasonal, 24-hour cycle)?
  • How do these changes in affect reproduction, survival, and growth of individuals and their communities?

The MOCNESS has been tested in the waters of lakes Michigan and Huron for the past three years. The team, led by Dr. Ed Rutherford, is supporting GLERL’s long term study of the Great Lakes food webs and fisheries. “The MOCNESS will enhance the ability of our scientists to more effectively observe the dynamics of Great Lakes ecosystem over space and time—a critical research investment that will pay off for years to come,” says Rutherford.

This year, the team is actively processing samples that were collected in the spring and will continue to collect more samples through the fall. The MOCNESS will support ongoing ecological research on the Great Lakes and the results will be shared with others around the region who are working to make decisions about how to manage Great Lakes fisheries and other water resources.

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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

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

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.


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New algorithm to map Great Lakes ice cover

Leshkvich sampling ice

GLERL researcher, George Leshkevich, drilling through the ice in Green Bay, Lake Michigan.

NOAA’s Great Lakes Environmental Research Laboratory (GLERL) is on the cutting edge of using satellite remote sensing to monitor different types of ice as well as the ice cover extent. To make this possible, an algorithm—a mathematical calculation developed at GLERL to retrieve major Great Lakes ice types from satellite synthetic aperture radar (SAR) data—has been transferred to NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) for evaluation for operational implementation.

Once operational, the algorithm for Great Lakes ice cover mapping holds multiple applications that will advance marine resource management, lake fisheries and ecosystem studies, Great Lakes climatology, and ice cover information distribution (winter navigation).  Anticipated users of the ice mapping results include the U.S. Coast Guard (USCG), U.S. National Ice Center (NIC), and the National Weather Service (NWS).

For satellite retrieval of key parameters (translation of satellite imagery into information on ice types and extent), it is necessary to develop algorithms specific to the Great Lakes owing to several factors:

  • Ocean algorithms often do not work well in time or space on the Great Lakes
  • Ocean algorithms often are not tuned to the parameters needed by Great Lakes stakeholders (e.g. ice types)
  • Vast difference exists in resolution and spatial coverage needs
  • Physical properties of freshwater differ from those of saltwater

The relatively high spatial and temporal resolution (level of detail) of SAR measurements, with its all-weather, day/night sensing capabilities, make it well-suited to map and monitor Great Lakes ice cover for operational activities. Using GLERL and Jet Propulsion Lab’s (JPL) measured library of calibrated polarimetric C-band SAR ice backscatter signatures, an algorithm was developed to classify and map major Great Lakes ice types using satellite C-band SAR data (see graphic below, Methodology for Great Lakes Ice Classification prototype).

ICECON (ice condition index) for the Great Lakes—a risk assessment tool recently developed for the Coast Guard—incorporates several physical factors including temperature, wind speed and direction, currents, ice type, ice thickness, and snow to determine 6 categories of ice severity for icebreaking operations and ship transit.  To support the ICECON ice severity index, the SAR ice type classification algorithm was modified to output ice types or groups of ice types, such as brash ice and pancake ice to adhere to and visualize the U.S. Coast Guards 6 ICECON categories. Ranges of ice thickness were assigned to each ice type category based on published freshwater ice nomenclature and extensive field data collection. GLERL plans to perform a demonstration/evaluation of the ICECON tool for the Coast Guard this winter.

Mapping and monitoring Great Lakes ice cover advances NOAA’s goals for a Weather-Ready Nation and Resilient Coastal Communities and Economies, and Safe Navigation. Results from this project, conducted in collaboration with Son V. Nghiem (NASA/Jet Propulsion Laboratory), will be made available to the user community via the NOAA Great Lakes CoastWatch website (




Measuring different ice types on Green Bay used to validate the ICECON (ice type classification) Scale in a RADARSAT-2 synthetic aperture radar (SAR) scene taken on February 26, 2017.


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NOAA GLERL collaborating with partners to monitor the Lake Huron ecosystem

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The NOAA Great Lakes Environmental Research Laboratory (GLERL) is participating in an international, multi-agency effort to study invasive species, water quality, fisheries, and climate change in Lake Huron this field season—pursuing key knowledge gaps in the ecosystem. The Coordinated Science and Monitoring Initiative (CSMI) coordinates across U.S. and Canadian agencies to conduct intensive sampling in one Great Lake per year, on a five-year cycle. The Great Lakes Restoration Initiative, which is administered by the U.S Environmental Protection Agency (EPA), is funding this research.

“While GLERL has had a long-term research program focused on Lake Michigan, we are using this initiative to advance long-term research on Lake Huron,” said GLERL Director Deborah Lee. “Invasive species, warming temperatures, and changes in nutrient loading are putting as much stress on Lake Huron as on Lake Michigan. We want to better understand the Lake Huron ecosystem and develop modeling tools to predict how the lake is changing.”

Henry Vanderploeg, Ph.D., chief of GLERL’s Ecosystem Dynamics research branch and lead researcher for GLERL’s efforts in the pelagic (open water) portion of the initiative comments, “GLERL plays a critical role in the CSMI, addressing key science questions. GLERL’s high frequency temporal and spatial sampling will help determine nutrient and energy flows from tributaries, nearshore to offshore. This type of data is critical to effectively manage Lake Huron for water quality and fish production.” Frequent spatial surveys are key to understanding food web connections throughout the seasons.

Researchers from GLERL  will expand upon their recent work in Lake Michigan (CSMI 2015) and past work in Huron (2012) to determine fine-scale food-web structure and function from phytoplankton to fishes along a nutrient-rich transect (from inner Saginaw Bay out to the 65-m deep Bay City Basin) and along a nutrient-poor transect (from inner Thunder Bay out to the Thunder Bay basin) during May, July, and September. GLERL will collect additional samples of fish larvae and zooplankton along both transects in June to help estimate larvae growth, diet, density, and mortality and to identify fish recruitment bottlenecks.

“GLERL was instrumental in establishing the long-term monitoring efforts that provide the foundation for current CSMI food-web studies,” said Ashley Elgin, Ph.D., research ecologist in the Ecosystem Dynamics research branch. Elgin serves as the NOAA representative on the CSMI Task Team, part of the Great Lakes Water Quality Act Annex 10, alongside partners from the U.S. Geological Survey (USGS), EPA, the U.S. Fish & Wildlife Service, Environment and Climate Change Canada, and the Ontario Ministries of Natural Resources and the Environment and Climate Change. This year, Elgin is conducting critical mussel growth field experiments in Lake Huron, expanding upon work she developed in Lake Michigan.  She will be addressing the following questions: (1) How does quagga mussel growth differ between regions with different nutrient inputs?; and (2) How do growth rates compare between Lakes Michigan and Huron? Elgin will also coordinate a whole-lake benthic survey, which will update the status of dreissenid mussels and other benthic-dwelling organisms in Lake Huron.  

GLERL’s key research partner, the Cooperative Institute for Great Lakes Research (CIGLR), will deploy a Slocum glider for a total of sixteen weeks to collect autonomous measurements of temperature, chlorophyll, colored dissolved organic matter (CDOM), and photosynthetically active radiation (PAR) between outer Saginaw Bay and open waters of the main basin.  Deployment times and coverage will be coordinated with other glider deployments by the EPA Office of Research and Development (ORD) and/or USGS Great Lakes Science Center, spatial research cruises, and periods of expected higher nutrient loads (i.e., following runoff events).  

CSMI research cruises began in late April and will continue through September. Researchers are using an impressive fleet of research vessels, including EPA’s 180-foot R/V Lake Guardian, GLERL’s 80-foot R/V Laurentian and 50-foot R/V Storm, and two large USGS research vessels, the R/V Articus and R/V Sterling. Sampling missions will also be conducted aboard Environment Canada’s Limnos across Lake Huron. The Laurentian is fitted out with a variety of advanced sensors and sampling gear, making it especially suitable for examining fine-scale spatial structure.

Scientists from the USGS Great Lakes Science Center, the Michigan Department of Natural Resources, and the University of Michigan are also participating in the Lake Huron CSMI.

Aerial photo survey improves NOAA GLERL’s Lake Erie ice model

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Understanding the duration, extent, and movement of Great Lakes ice is important for the Great Lakes maritime industry, public safety, and the recreational economy. Lake Erie is ice-prone, with maximum cover surpassing 80% many winters.

Multiple times a day throughout winter, GLERL’s 3D ice model predicts ice thickness and concentration on the surface of Lake Erie. The output is available to the public, but the model is under development, meaning that modelers still have research to do to get it to better reflect reality.

As our scientists make adjustments to the model, they need to compare its output with actual conditions so they know that it’s getting more accurate. So, on January 13th of this year, they sent a plane with a photographer to fly the edge of the lake and take photos of the ice.

The map below shows the ice model output for that day, along with the plane’s flight path and the location of the 172 aerial photos that were captured.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations -- January 13, 2017. Credit NOAA GLERL/Kaye LaFond.

NOAA GLERL Lake Erie ice model output with all aerial photo survey locations — January 13, 2017. Map Credit NOAA GLERL/Kaye LaFond.

These photos provide a detailed look at the sometimes complex ice formations on the lake, and let our scientists know if there are places where the model is falling short.

Often, the model output can also be compared to images and surface temperature measurements taken from satellites. That information goes into the GLSEA product on our website (this is separate from the ice model). GLSEA is useful to check the ice model with. However, it’s important to get this extra information.

“These photographs not only enable us to visualize the ice field when satellite data is not available, but also allow us to recognize the spatial scale or limit below which the model has difficulty in simulating the ice structures.” says Eric Anderson, an oceanographer at GLERL and one of the modelers.

 “This is particularly evident near the Canadian coastline just east of the Detroit River mouth, where shoreline ice and detached ice floes just beyond the shoreline are not captured by the model. These floes are not only often at a smaller spatial scale than the model grid, but also the fine scale mechanical processes that affect ice concentration and thickness in this region are not accurately represented by the model physics.”

Click through the images below to see how select photos compared to the model output. To see all 172 photos, check out our album on Flickr. The photos were taken by Zachary Haslick of Aerial Associates.


This gallery contains 10 photos

NOAA booth at annual American Meteorological Society meeting.

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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.