2016 Summer Fellows Position Descriptions

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Applications due 26 February 2017, 11:59 pm EST

Position Descriptions
1. Spatial analysis of dreissenid mussel populations
Mentors: Ashley Baldridge (NOAA-GLERL; ashley.baldridge@noaa.gov), Mark Rowe (UM-CILER;mark.rowe@noaa.gov)
Project Description: This project will advance our understanding of dreissenid mussel spatial distribution, population growth, and ecosystem impacts. The fellow will work with mentors to update NOAA dreissenid density and biomass maps for Lake Michigan (and other Great Lakes, if time permits). It is clear that quagga mussel densities in the Great Lakes have undergone dynamic changes in the past decade, but less is known about how depth, substrate, and lake-specific factors are influencing the observed trajectories. The student will investigate a research question of interest that addresses these changes. Activities will include, but are not limited to: (1) organizing and summarizing long-term data sets; (2) analyzing ecological data using advanced spatial analysis techniques; and (3) assisting with other field and lab projects in the Benthic Ecology Lab as desired to broaden exposure. Over the course of this project, the fellow will also have the opportunity to interact with other NOAA ecologists and modelers at GLERL in Ann Arbor and the NOAA-GLERL Lake Michigan Field Station. Strong candidates will have prior scientific programming, data analysis (R preferred, also Matlab, Python, IDL), and/or experience with geographic information systems (GIS); be organized and conscientious; and have an interest in Great Lakes ecology.

2. Great Lakes ice data ArcGIS scripting
Mentors: Jia Wang (NOAA-GLERL; jia.wang@noaa.gov), Anne Clites (NOAA-GLERL; anne.clites@noaa.gov)
Project Description: NOAA-GLERL archives data on Great Lakes ice cover from the National Ice Center each year. This project provides the basic data used to provide information to a broad audience of users and decision makers for search and rescue operations, navigation (commercial shipping), and recreational ice fishing during the winter season. This important data set is also used for research on improving the prediction of ice cover in response to a changing climate on seasonal, interannual, and decadal time scales. In addition to storing the original asci grid files, GLERL also imports the data into ArcGIS, saving shapefiles and jpegs associated with each asci grid file. These are archived on our website and made available to the public here: http://www.glerl.noaa.gov/data/pgs/glice/glice.html. Our current method for importing data into ArcGIS is in need of automating. We are seeking a good Python scripter who has at least some familiarity with ArcGIS to automate our current processing procedures. In addition, we intend to conduct in-depth research linking to Arctic climate teleconnection patterns to Great Lakes climate and ice cover. The project is part of the prediction of ice cover in response to a changing climate on seasonal, interannual, and decadal time scales, which enables us to provide information to a broader user base in search and rescue operations, navigation (commercial shipping), and recreational ice fishing during the winter season. These forecasts provide decision makers with tools to aid in protecting the Great Lakes and the public. Qualifications include programming in Fortran, and/or other programming skills are plus such as C, GIS, Matlab, R, and scripts. Data analyses and statistics background is desired.

3. Using an ecosystem-based model to study the impacts of remediation on the food webs
Mentors: Doran Mason (NOAA-GLERL; doran.mason@noaa.gov), Ed Rutherford (NOAA-GLERL; ed.rutherford@noaa.gov), and Hongyan Zhang (UM-CILER; zhanghy@umich.edu)
Project Description: Our research team is using ecosystem-based models to study how the Great Lakes ecosystems respond to the natural and anthropogenic stressors, and provide scientific support for lake and fisheries management. This fellow will work closely with the team to calibrate and apply an ecosystem-based model (the Atlantis ecosystem model) to study the temporal and spatial impacts of remediation actions (e.g., invasive species, habitat restoration, or nutrient reduction) on food web dynamics in Lake Erie. Activities may include, but are not limited to: 1) calibrating the model with observational data; 2) scenario simulations, result analyses and discussion. Candidates should have a strong interest or background in one or more of the following areas: programming, Great Lakes ecosystems, ecosystem modeling, and fish ecology and fisheries.

4. Safety and environmental management system
Mentor: Kim Kulpanowski (NOAA-GLERL; kim.a.kulpanowski@noaa.gov)
Project Description: This fellow will bring a fresh set of eyes to GLERL’s safety and environmental compliance program and work to develop and implement any number of program management enhancements based on their interests and abilities. This fellow will be oriented to the current program management approach, then hone in on opportunities for improvement and develop them to advance the overall successful management of the program. Project ideas include but are not limited to:

  • incorporation of the latest technology tools and concepts in training programs
  • chemical inventory management system improvement/redesign
  • safety inspection reporting system redesign
  • universal and/or hazardous waste management/minimization
  • safety and environmental program planning/management system design

Candidates should have a strong interest or background in one or more of the following areas: information technology, industrial hygiene, safety, hazardous waste and/or universal waste management, continual improvement quality concepts, project planning. Regulatory familiarity with OSHA safety and health standards and EPA and MDEQ environmental regulations would be helpful for some of the projects.

5. Meteorological data analysis
Mentors: Ayumi Manome (UM-CILER; ayumif@umich.edu), Eric Anderson (NOAA-GLERL; eric.j.anderson@noaa.gov)
Project Description: The fellow will support the GLERL ice and hydrodynamic modeling team by improving the historical meteorological dataset over the Great Lakes. Specifically, the fellow will work on incorporating meteorological reports from ships operating across the Great Lakes into the existing gridded datasets based on data from the National Data Buoy Center and the Coastal Marine Automated Network. Special focus will be given to winter, when buoy data are usually not available. Tasks will include 1) excluding erroneous data based on systematic criteria, 2) tabulating the quality statistics for each ship ID, 3) create a new gridded dataset that includes quality-controlled ship reports, 4) inter-comparison with atmospheric re-analysis (e.g., North American Regional Reanalysis, Climate Forecast System Reanalysis). The candidate should have knowledge of basic meteorology, including that of the surface layer (the constant flux layer). Preference will be given to candidates that have experience with atmospheric data analysis, Fortran, a Linux/Unix computing environment.

6. Hydroclimatological modeling
Mentors: Chuliang Xiao (UM-CILER; cxiao@umich.edu), Brent Lofgren (NOAA-GLERL; brent.lofgren@noaa.gov).
Project Description: This project will involve using the Weather Research and Forecasting (WRF) model to study the hydroclimate in the Great Lakes region. Coupled Model Intercomparison Project Phase 5 (CMIP5) provides projections of future climate change in different future emission scenarios in a global perspective. Based on CMIP5 outputs, a state-of-the-art regional climate model, WRF coupled with a sophisticated lake model, is implemented to conduct dynamic downscaling projections in the Great Lakes region. Activities may include, but are not limited to: 1) gaining an understanding of the concept of dynamic downscaling; 2) comparing the future climate projections (e.g., temperature, precipitation, evaporation) between two scenarios (high and moderate emissions); 3) calibrating a routing model to project the water level change in the future. Candidates should have knowledge of basic meteorology, and experience with data analysis (e.g., Fotran, GrADS, NCL, R).

7. GLANSIS (Great Lakes Aquatic Nonindigenous Species Information System)
Mentors: Rochelle Sturtevant (NOAA-GLERL; rochelle.sturtevant@noaa.gov), Ed Rutherford (NOAA-GLERL; ed.rutherford@noaa.gov)
Project Description: The summer fellow will work with an online database serving information relating to nonindigenous species in the Great Lakes region. The fellow will be expected to take leadership for a specific, defined research project component of GLANSIS, such as updating the range expansion list and developing fact sheets for the added species. Tasks will include reviewing scientific literature to develop or update species fact sheets, reviewing the system for consistency and improving content, data entry, working with external scientists to identify and evaluate new species for possible addition to the database, and updating of outreach materials. There is also a possibility of making presentations at public events. Candidates must have excellent written communication skills (technical and non-technical writing). Familiarity with database programs and/or invasion biology a plus, but not essential.

8. Invasive species, fisheries and foodweb dynamics
Mentor: Ed Rutherford (NOAA-GLERL; ed.rutherford@noaa.gov)
Project Description: This fellow will participate in an ongoing study of invasive species impacts on Great Lakes fisheries and food webs. In particular, the fellow will assist with intensive diel surveys in Lake Michigan and use new sampling technology (MOCNESS sampling system) to describe the physical structure of the water column in nearshore and offshore waters, and its effect on fine spatial distributions and densities of chlorophyll, native zooplankton species, the predatory cladoceran Bythotrephes, and fish larvae densities, growth and survival. The fellow will be expected to conduct a gear efficiency study comparing traditional plankton sampling gears with the more modern MOCNESS sampling system. Candidates should have a strong background in Great Lakes or marine science and be experienced working on boats in variable weather conditions.