forecasting | NOAA Great Lakes Environmental Research Laboratory

December 14, 2022
by Gabrielle Farina Leave a comment

Decades in the making, NOAA’s newest Lake Superior and Lake Ontario forecast systems become fully operational

Did you know that NOAA operates a forecasting system that predicts water conditions on the Great Lakes? Whether you’re wondering about a lake’s temperature, currents, or water level changes, NOAA’s got you covered! This fall, NOAA implemented newly updated versions of the Lake Superior and Lake Ontario portions of this system, and added ice forecasts to all five lakes.

*Lake Michigan waves at the St. Joseph North Pier Lighthouses following superstorm Sandy. October 29, 2012. Credit: S. Lashley, NOAA NWS.*

GLOFS forecasts Great Lakes conditions

The publicly available Great Lakes Operational Forecast System (GLOFS) is a NOAA automated, model-based prediction system aimed at providing improved predictions of these conditions in the five Great Lakes (Erie, Michigan, Superior, Huron and Ontario) for the commercial, recreation, and emergency response communities. GLOFS models use current lake conditions and predicted weather patterns to forecast the lake conditions for up to five days (120 hours) in the future. GLOFS predictions enable users to increase the margin of safety and maximize the efficiency of commerce throughout the Great Lakes.

NOAA’s National Weather Service (NWS) and National Ocean Service (NOS) work together to run GLOFS operationally on NOAA’s High Performance Computing System. By running on NOAA’s High Performance Computing System, GLOFS has direct access to National Weather Service operational meteorological products that are required for reliable and timely operations.

A major update for Lakes Ontario and Superior

A key goal of NOAA’s Research branch is to continually make forecasts better, and GLERL scientists play a major role in improving the models that constitute GLOFS. Like the rest of GLOFS, the Lake Ontario and Lake Superior portions – Lake Ontario Operational Forecast System (LOOFS) and Lake Superior Operational Forecast System (LSOFS) – were originally based on the Princeton Ocean Model. As of October 2022, they’ve now been upgraded with higher-resolution versions that are based on a newer computer model.

*MODIS satellite images of Lakes Superior (left) and Ontario (right) in March 2021.*

The new LOOFS and LSOFS use the Finite Volume Community Ocean Model (FVCOM), coupled with an unstructured grid version of the Los Alamos Sea Ice model (CICE). The new model provides users with higher resolution of nowcast (near-present conditions) and forecast guidance of water levels, currents, water temperature, ice concentration, ice thickness and ice velocity out to 120 hours in the future, and it updates four times per day. By invoking advanced model schemes and algorithms, LOOFS and LSOFS are expected to generate a more accurate model output than their former versions.

Before they were ready to become operational, the new versions of LOOFS and LSOFS were run experimentally at GLERL for several years, where they underwent extensive testing and evaluation. GLERL played a key role in developing these models and ran them as part of the Great Lakes Coastal Forecasting System (GLCFS) – an experimental version of GLOFS that GLERL uses to prepare new models to become operational.

With this transition, the GLOFS models for all five Great Lakes have now been upgraded to FVCOM versions, as the Lake Erie model was upgraded in 2016, and the Lake Michigan-Huron model was upgraded in 2019. A new FVCOM-based model for the Huron-Erie Corridor, which includes Lake St. Clair and both the St. Clair and Detroit Rivers, is scheduled to be added to GLOFS in 2023. Read more about the LOOFS and LSOFS transition here.

*These output maps from the current 3rd generation GLOFS show Lake Superior wind speed*
*and direction (top) and Lake Ontario water temperatures (bottom).*

GLERL has been improving GLOFS for over 30 years

GLOFS is based on the Great Lakes Forecasting System, originally developed by The Ohio State University (OSU) and GLERL in the late 1980s and 1990s under the direction of Dr. Keith Bedford (OSU) and Dr. David Schwab (NOAA GLERL). The original forecasting systems utilized the Princeton Ocean Model (POM) and used a set of uniformly structured bathymetric grids. The first routine nowcast, using a low-resolution grid for Lake Erie, began at OSU in 1992.

Starting in 2002, GLERL’s semi-operational GLCFS was expanded to five lakes using medium-resolution grids (5 – 10 km) and 48-hr forecasts were added. This version was successfully transferred from research to operations at NOAA NOS in 2010. The transition to operations at NOAA NOS was a joint effort between NOAA GLERL, NOS Center for Operational Oceanographic Products and Services (CO-OPS) and NOS Office of Coast Survey (OSC) Coast Survey Development Laboratory (CSDL), private industry, and academia (OSU).

NOAA GLERL has continued to make improvements to the experimental GLCFS; these include increasing the grid resolution (2 – 10 km), adding ice dampening and an ice model, and extending the forecasts to 120 hours during the period of 2006-2014 (generation 2). The current 3rd generation of the GLOFS is what you see run by NOS today, with a resolution of 200m to 2.5km and producing 120-hour forecasts.

The development and implementation of LSOFS and LOOFS is a joint project across several NOAA offices and external partners.

NOAA National Ocean Service Center for Operational Oceanographic Products and Services
NOAA NOS Office of Coast Survey
NOAA Office of Oceanic and Atmospheric Research Great Lakes Environmental Research Laboratory
Finite Volume Community Ocean Model development group at the University of Massachusetts Dartmouth
NOAA National Weather Service National Centers for Environmental Prediction Central Operations

November 20, 2020
by Margaret Lansing Leave a comment

Lake effect snow: What, why and how?

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

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

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

What is lake effect snow?

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

Lake Effect Snow Can Be Dangerous

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

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

Why is lake effect snow so hard to forecast?

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

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

GLERL and CIGLR work to improve lake effect snow forecasting

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

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

Our lake effect snow research continues

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

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

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

GLERL Ocean(lake)ographer Eric Anderson on watching the Straits of Mackinac

Eric Anderson, GLERL oceanographer, used to study the movement of fluid inside bone tissue — now he studies the movement of water in the Great Lakes.

Eric Anderson is NOAA GLERL’s resident oceanographer (but his Twitter handle is @lakeographer—you should trademark that one, Eric). At its core, his research centers around the movement of water. You might have seen our animations of currents in the Straits of Mackinac, or of meteotsunamis coming across Lake Michigan — he’s the guy behind those computer models.

Some cool things about Eric are that he plays the banjo, that he used to study the movement of fluid inside bone tissue, and that he’s quick to remind us people were watching the Straits of Mackinac millennia before his computer models existed. Read on to learn more cool things!

How would you describe your job?

My research is on hydrodynamics, which is a fancy way of saying the moving physical aspects of the water in the Great Lakes—things like currents, temperatures, ice, and waves. Most of my day is built around looking at measurements of the water and air and then developing computer models that simulate how the lakes respond to different weather conditions. This field of science is particularly helpful in safe navigation of the lakes, responding to contaminant spills, search and rescue operations, and understanding how the ecosystem responds to different lake conditions.

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

Maybe the most rewarding has been working on the Straits of Mackinac. It’s one of the most beautiful spots in the Great Lakes, but also one of the most dynamic, with high-speed currents changing every few days, if not hours. A groundswell of attention to the Straits in the last several years has pushed the public to get more engaged and learn about the conditions in the Straits, and I’ve been glad to help where I can.

As part of this work, we’ve found some 1600’s-era [settler] written accounts of the currents in the Straits. We also know that [Indigenous] people have been watching the Straits for thousands of years, and it’s rewarding to continue this thread of knowledge.

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

It seems like the hardest thing is to communicate the science. People are starved for information, and there’s a real love out there for learning about the Great Lakes. All we can do is to try and keep the flow of information getting out to the folks who care, and just as important, to those who don’t think they care. When you see environmental science covered in the news, it’s usually reporting on something negative or even catastrophic, which is certainly important, but there are pretty cool discoveries being made routinely, big and small, and those don’t often seem to make it to the headlines. We have to keep working hard to make sure these stories make it out, and at the same time keep our ears open to the concerns that people have for the lakes.

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

Inspiration is everywhere. Try to hike up to a good vantage point overlooking the lake, like the dunes or a bluff, and not feel inspired. More often, though, inspiration comes from talking with other people, whether scientists, students, or interested members of the public. I can’t think of a time where I’ve given a public seminar and not walked away with a new question or idea to investigate. People’s enthusiasm and bond with the Great Lakes is infectious, and so I try to tap into that as often as I can.

Two meteotsunamis, large waves caused by storm systems, came across Lake Michigan on April 13, 2018. Eric Anderson models meteotsunamis in his role as oceanographer at NOAA GLERL.

How would you advise high school students interested in science as a career path, or someone interested in your particular field?

I took somewhat of a winding career path to get where I’m at with GLERL, working in car assembly plants and then on the nano-fluidic flow inside bone tissue before ending up in physical oceanography. I didn’t really know what I wanted in high school or college, but I knew physics and math were where I felt at home. So I found a way to learn the fundamentals that I’ve been able to apply in each of these jobs, and that allowed me to explore different parts of science and engineering. Not everyone will have the same chances or opportunities, but if you can find a way to really solidify the fundamentals and just as importantly seek out a breadth of experiences, you’ll be in a better position when those opportunities do come along.

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

I’m either hanging out with family, playing music, or talking with someone about how I wish I was playing more music.

What do you wish people knew about scientists or research?

By and large, science is curiosity driven, often fueled by the scientist’s own enthusiasm, and in my case also by the interests of the public. Whether it’s a new discovery, or re-codifying or quantifying something that others have observed for millennia, there’s no agenda here other than to understand what’s happening around us and share whatever pieces we can make sense of. I’ll add a sweeping generalization that scientists love to talk about their research, so don’t be afraid to ask.

January 18, 2018
by Nicole Rice 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.

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.

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.

NOAA booth at annual American Meteorological Society meeting.

January 23, 2017
by Nicole Rice 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.

NOAA Great Lakes Environmental Research Laboratory

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

Tag Archives: forecasting

Decades in the making, NOAA’s newest Lake Superior and Lake Ontario forecast systems become fully operational

GLOFS forecasts Great Lakes conditions

A major update for Lakes Ontario and Superior

GLERL has been improving GLOFS for over 30 years