2023 Projects
**This list is provided for reference only. Please select your project from the list of 2024 projects**
APPLICANTS: Please refer to the list below when selecting your preferred internship sites. Please note that this is not necessarily an exhaustive or final list of projects. More projects may be added (at the bottom of this page) after the application opens so you should check the list and, if needed, adjust your responses accordingly. Please note that participating offices are noted under the Program Details tab. Some offices/labs/centers do not have projects identified yet and some may choose not to select interns this year.
Please also view the preferred majors/education level as a guideline and not a restriction. If you believe you possess experience related to NOAA's mission, we encourage you to apply.
National Ocean Service (NOS)
| Project Title | Deep Learning for Supporting Ocean Data Quality Control |
| Mentor name | Hassan Moustahfid |
| Host office/program/lab | Integrated Ocean Observing System or Co-hosted by IOOS RAs |
| Host/Lab location | Silver Spring, MD or one of our 11 Regional Associations |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Marine science/Data analytics and data science/Math statistics/Computer science and IT |
| Required Coding Skills | |
| Python | Interm |
| C++ | Interm |
| R | Interm |
| Linux | Basic |
| Other skills required | Data management/cloud computing and some machine learning algorithm development |
| Skills interns will learn during internship | Ocean data quality control/Data Management/cloud computing/ machine learning and deep learning algorithm development |
| Project Description | QC modules are in place to check all incoming data to RA repositories. It works but as the number of data streamed to RAs, the automated/semi-automated QC modules have started to show deficiencies. Modules such as rate of change and spike tests, designed to identify outliers, have proven insufficient. Manual interventions is tedious and difficult due to the size of the data captured. Thus, the main motivation of our project is to assist QC experts by supporting the automation/semi-automation of the QC procedures. For this purpose, AI and specifically machine learning and deep learning (ML/DL) algorithms, are used. In this project, we will explore a range of ML/DL algorithms and assess their efficiency and accuracy and recommend an optimal algorithm to implement for IOOS. |
National Marine Fisheries Service (NMFS)
| Project Title | Developing machine learning tools to aid in management of Pacific fish species |
| Mentor name | Elliott Hazen |
| Host office/program/lab | Southwest Fisheries Science Center |
| Host/Lab location | Monterey, CA |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application), Graduate (MS or PhD) |
| Preferred majors | Statistics, Computer Programming, Biology, Oceanography |
| Required Coding Skills | |
| R | Expert |
| Skills interns will learn during internship | Developing machine learning / artificial intelligence reproducible workflow |
| Project Description | OAA recently established an Artificial Intelligence (AI) Strategy, which included the formation of a NOAA Center for AI (NCAI). Broadly, the goals of the NCAI and the AI Strategy are to infuse AI techniques across NOAA’s mission areas to advance the quality and timeliness of its products and services. A critical component for proliferation of AI throughout the NOAA landscape is to further develop the AI and machine learning (ML) skills of the workforce, both within and beyond NOAA. This opportunity seeks to engage with researchers from NOAA Fisheries and the NCAI to build upon existing, and to explore new training materials around AI and ML. The NCAI seeks to create a suite of flexible technical tutorials that use NOAA’s vast resources of space, earth, and ocean data sets that serve as learning journeys for researchers and data practitioners. During the summer, the intern working on this project will: Read peer-reviewed scientific articles to understand the basis for application of machine learning algorithms to species distributions. Learn to work this the specific datasets and machine learning algorithms. Train and evaluate machine learning algorithms, including learning best practices for algorithm tuning and selection. Expand upon and develop new training materials for machine learning applications to species distribution modeling. Attend research group meetings to report progress Engage with NCAI and NOAA Fisheries scientists to maximize learning opportunities. Should COVID situations prevent an in-person internship, we could host this internship virtually. Researchers at the NOAA Southwest Fisheries Science Center have developed a suite of machine learning tools that are used to operationally aid in the sustainable management of Pacific Ocean fisheries. These tools consist of species distribution models (SDMs) that incorporate observed locations of animals (e.g., sea turtles, sea lions, and sharks) and examine the types of environmental information that can be used to predict their presence (or absence in a particular area). Initial training materials (https://htmlpreview.github.io/?https://github.com/elhazen/SDM_tutorial/blob/master/BRTandGAMM.html#introduction) have been developed for these tools and we seek to expand such materials using, for example, additional data sources, Python and R programming scripts, hyperparameter tuning examples, model selection criteria, or more. The final materials will be compiled in Jupyter notebooks or Research Compendium that are shared via the NCAI github repository. Elliott Hazen (Southwest Fisheries Science Center) and Heather Welch (Southwest Fisheries Science Center / UCSC) will guide methodological development of species distribution models. These researchers will further connect the student with the open source programming efforts throughout NOAA. |
| Project Title | Development of a strategy to operationalize data loggers to reduce impacts of sea turtle bycatch in trawl fisheries |
| Mentor name | Ellen Keane |
| Host office/program/lab | Greater Atlantic Regional Fisheries Office |
| Host/Lab location | Gloucester, MA |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Computer Science; Environmental Science; Project Management or majors similar to these. |
| Required Coding Skills | None |
| Other skills required | While GIS is not required, it could potentially be used in developing the plan. Therefore, a basic understanding is desirable. In addition, the intern should have strong oral and written communication skills. |
| Project Description | Working with the Northeast Fisheries Science Center and research partners, we (the Greater Atlantic Regional Fisheries Office) have developed technology (data loggers) to monitor the time that a fishing net is submerged (tow duration). Accidental capture in fishing gear is one of the primary threats to sea turtles, and this threat can be reduced if shorter tow durations are used in the fishery. Reduced tow durations increase the likelihood that any sea turtle that is accidentally captured in the fishing gear will survive. While the technology has been shown to be effective in pilot studies, a number of challenges exist before it can be effectively implemented in the fishery. These challenges include data transfer and storage, processes to address when tow duration has been exceeded, engaging with industry, and enforcement. Working with our office, the Science Center, and our enforcement partners, the intern will develop a strategy that details the steps needed to take the data loggers from a viable idea to a conservation tool. The strategy will also include plans for outreach and engagement with the fishing industry. This project has direct management applicability as we are considering management measures in several trawl fisheries in our region. After an orientation on the data logger project, the intern will meet with a range of NOAA Fisheries staff (science, management, enforcement, general counsel) to better understand the implementation needs. Based on these meetings, the intern will develop a draft outline and approach to developing the strategy to operationalize data loggers in the fishery. This will be refined based on input from their mentor. Working from this initial product, the intern will then fully develop an implementation strategy. If appropriate at the time, the intern may also be involved in outreach and engagement opportunities to rollout the strategy within the agency. In addition, the intern is expected to participate in a variety of opportunities offered. This includes team meetings, “Meet and Greets” with NOAA staff, and, if on-site, field trips. At the end of the internship, the student will present on their experiences to their colleagues. The intern and mentor will jointly develop a work plan. During the 10 week period, the intern and mentor will meet at least weekly to check-in on work plan progress, to make adjustments as needed, and to plan for the following week. In between meetings, the mentor will be available for questions and guidance as needed. If the intern is remote, they will check in each work day via email. |
| Project Title | Exploring the efficacy of using data from Fishbrain app to inform fisheries management and stock assessments |
| Mentor name | Melissa Monk |
| Host office/program/lab | Southwest Fisheries Science Center Santa Cruz |
| Host/Lab location | Santa Cruz/Long Beach, CA |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application), Graduate (MS or PhD) |
| Preferred majors | Fisheries, Wildlife, Marine Sciences, Natural Resources |
| Required Coding Skills | |
| R | Interm |
| GIS | Basic |
| Other skills required | Preferred skills include familiarity with GitHub, R Markdown or Quarto |
| Skills interns will learn during internship | The project will explore data collected within Fishbrain (an app anglers use to share catches of fish) for managed rockfish species off the Coast of California. |
| Project Description | Fishbrain is an app recreational anglers use to share fish catches and ancillary information including the location of the catch, fish length and weather conditions. It represents a possible source of information for fisheries biologists to better understand the recreational fishery and a citizen science or crowdsourced data stream that has not been considered by fisheries scientists before now. The intern will work collaboratively with stock assessment scientists at the NMFS Southwest Fisheries Science Center in Santa Cruz, California, a recreational fishing coordinator at the NMFS West Coast Regional Office in Long Beach, California as well as developers of the Fishbrain app. Collectively, the group will explore the efficacy of using Fishbrain’s data to inform fisheries management and stock assessments. This will include data mining, mapping fishing locations over known rocky reef habitat, comparing relative reported catch rates with those estimated from standardized recreational fishery surveys, and exploring possible biases related to voluntary reporting of information (non-probability sampling). There are a number of other aspects of the data to explore including, but not limited to, ground truthing species identification, species distribution modeling, and analyzing fisher behavior related to regulation changes. There will also be opportunities to join single-day rockfish fishing trips throughout the summer along with NMFS staff. The intern will develop summary statistics for a select number of species from the Fishbrain data and provide comparisons with estimated catches from standardized fishery surveys. Spatial analyses will provide information on the distribution of effort observed from the Fishbrain data versus known reef fish habitat. The outcome will provide insight into the efficacy of incorporating Fishbrain data into fisheries stock assessment and management and exploring how fish species may be moving in relation to climate change. The intern will be responsible for meeting with the collaborative mentor group weekly, developing R code to explore the and summarize the data, and developing maps in ArcGIS Pro or other mapping software. The intern can choose an internship location either at the Southwest Fisheries Science Center in Santa Cruz or at the West Coast Region office in Long Beach. At either location, the intern will work in person with NMFS staff and meet (at a minimum) weekly with staff from both offices. |
National Environmental Satellite and Data Information Service (NESDIS)
| Project Title | Tracking performance of NOAA satellite systems |
| Mentor name | CDR James Brinkley |
| Host office/program/lab | Office Satellite Products & Operations |
| Host/Lab location | Suitland, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Any |
| Required Coding Skills | None |
| Other skills required | A technical field of study is preferred BUT NOT NECESSARY |
| Skills interns will learn during internship | Satellite operating systems, diplomacy with international partners, familiarization with satellite products and services |
| Project Description | The National Environmental Satellite, Data, and Information Service (NESDIS) Office of Satellite and Product Operations (OSPO) provides end-to-end data acquisition and delivery of satellite derived products and services. OSPO consists of the Mission Operations Division which oversees satellite operations, the Satellite Products and Services Division which oversees delivery of products and services, the National Ice Center which provides snow and ice products, two Command and Data Acquisition facilities in Wallops Island, VA and Fairbanks, AK, and a backup site in Fairmont, WV. OSPO manages and directs the operation of NOAA's satellites; the acquisition of remotely sensed data; and the generation and delivery of associated products. It also supports launch, activation, and elevation of new satellites and the in-depth assessment of satellite and ground system anomalies. It prepares plans and procedures for responding to satellite and ground anomalies, and establishes and coordinates the schedules for satellite operation and data acquisition to meet users' needs. In order to ensure data delivery and production, OSPO also manages and directs the operation of the central ground facilities which ingest, process, and distribute environmental satellite data and derived products to domestic and foreign users. Additionally, the applicant with implement and exercise operational backup plans and related activities, participate in the development of test and integration plans, assist the Technical Director to plan for new missions and requirements, and develop operational reports and work with OSPO Corporate Services to track system(s) performance. |
| Project Title | Calibration of Infrared/Microwave Sounders |
| Mentor name | Flavio Iturbide-Sanchez |
| Host office/program/lab | Center for Satellite Applications and Research (STAR) |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Physics |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Interm |
| R | Basic |
| Fortran | Basic |
| UNIX | Interm |
| Linux | Interm |
| Other skills required | High Communication (Oral, Written)/Teamwork |
| Skills interns will learn during internship | Data Analysis |
| Project Description | This project involves the participation in activities related to satellite remote sensing. The candidate will have the opportunity to learn about the state-of-the-art infrared and microwave sensors for weather applications and environmental monitoring. Main topics include calibration and validation of observations from infrared and microwave sounders as well as the generation and assessments of geophysical parameter, like atmospheric temperature, water vapor and precipitation, using retrieval systems. It is expected that the candidate works with subject-matter-experts in a teamwork environment. The candidate should report the scientific results in regular meeting and plan to present in appropriate conferences. |
| Project Title | Hyperspectral sensing of atmosphere and water for a new generation of satellite instrument observations |
| Mentor name | Changyong Cao |
| Host office/program/lab | Center for Satellite Applications and Research (STAR) |
| Host/Lab location | NCWCP, College Park, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Earth Science/Electrical Engineering |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Basic |
| Linux | Interm |
| Other skills required | Hands on laboratory experience would be a plus. |
| Skills interns will learn during internship | Collecting spectral samples with spectrometers, analyze satellite and in-situ data with python, run atmospheric radiative transfer models |
| Project Description | In this hyperspectral sensing project, the student will learn skills to operate a portable spectrometer, calibrate it in the laboratory, and acquire in-situ spectral samples of atmosphere and water. The student will also learn how to run atmospheric radiative transfer models to compare with satellite and in-situ hyperspectral data. This will allow the student to develop the essential skills for fully utilizing the new generation of satellite instrument observations, and a potential career in environmental remote sensing for weather, water, and climate applications. |
| Project Title | Convective Snow Quantitative Precipitation Estimation Using GOES/POES, NEXRAD, and Ground-Based Observations |
| Mentor name | Mark Kulie |
| Host office/program/lab | Center for Satellite Applications and Research (STAR) |
| Host/Lab location | Madison, WI |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Atmospheric Science, Meteorology |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Basic |
| UNIX | Basic |
| Linux | Basic |
| Skills interns will learn during internship | Remote sensing, operational cloud products produced from GOES and polar orbiting satellites, data fusion (merging satellite, radar, and ground-based in situ observations) |
| Project Description | This project will use combined GOES/POES, NEXRAD, and ground-based snowfall observations to improve a GOES quantitative precipitation estimation (QPE) product for hazardous convective snow events (e.g., lake-effect snow, shallow convective snow squalls over land) that is currently in its developmental stage. The project will involve three components (time permitting): (1) creating a database of recent convective snowfall events spanning the GOES-R series operational period, (2) improving NEXRAD-derived snowfall rates for convective snow events using ground-based in situ observations, and (3) analyzing multivariate relationships between operational cloud products (e.g., cloud height, temperature, phase, type, microphysical properties, etc.) and NEXRAD-derived snowfall rates that will form the basis for an improved GOES QPE product. The ultimate goal will be to develop the framework for a machine learning algorithm that ingests NOAA satellite observations and produces convective snowfall rates using combined GOES, POES, and NEXRAD observations and products as a training dataset. |
| Project Title | Developing a low dissolved oxygen map for the Gulf of Mexico Hypoxia Watch web product. |
| Mentor name | Courtney Bouchard |
| Host office/program/lab | NCEI - OGSSD |
| Host/Lab location | Stennis Space Center, MS |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Earth Science/Marine Science/GIS |
| Required Coding Skills | |
| Python | Basic |
| R | Basic |
| GIS | Interm |
| Other skills required | ArcMap 3d analyst - beginner-intermediate |
| Skills interns will learn during internship | ArcGIS, reading papers, learn more oceanography, presentation skills, networking |
| Project Description | Hypoxia Watch is a near-real time visualization of dissolved oxygen profiles taken aboard the Oregon II during the Southeast Area Monitoring and Assessment Program (SEAMAP) bottom trawl surveys in the Gulf of Mexico. In partnership with the National Marine Fisheries Service (NMFS), the shore-based NOAA scientists receive dissolved oxygen (DO) data in near-real time from the R/V Oregon II during the summer. While we get data for multiple depths, we currently only display the interpolated area of DO by the bottommost data points. There has been interest from stakeholders to create volumetric estimates of the hypoxic zone. The project would entail 1) learning the collection process of the NMFS/SEAMAP data, 2) create an interpolated visualization of the ship measured data using ArcMap 3D Analyst Tools, and 3) calculate the volumetric size of the hypoxic zone for specific seasons using the interpolated schema. |
| Project Title | GOES-R Cloud-based Mission Visualization Internship |
| Mentor name | Maurice McHugh |
| Host office/program/lab | GOES-R |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Computer Science and Engineering, Data Science, Scientific Visualization, Atmospheric Science, Meteorology, Oceanography, Physical Sciences, Remote Sensing Technology |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Interm |
| Linux | Interm |
| GIS | N/A |
| Other skills required | With the project focus being processing, distribution and visualization of environmental remote sensing data, candidates should be well versed in a range of computer languages, prominently including python and javascript. Experience with data manipulation, visualization, and data science will be advantageous, as will familiarity and enthusiasm for remote sensing, meteorology, and earth science. |
| Project Description | The objective of these internships is to experiment with new paradigms for rapid and collaborative integration of satellite data into Federal agency environmental service programs. Scholars will create satellite data visualization applications for new and emerging mission partners including the National Ocean Service, National Marine Fisheries Service, and the National Weather Service. Open source cloud-based technologies and techniques will be employed with the intent of creating a community-owned open-source evolving framework for such applications. Scholars will paired with a client mission professional (i.e. Forecaster or Analyst) along with technical mentors in remote sensing, computer science, and the natural sciences to create a specific rapid response interactive tool for use in the client mission. Using an agile approach with six sprints, scholars will have an opportunity to use the Amazon Web Services (AWS) framework to contribute to the development of an open-source real time multi user secure streaming system to deliver real-time satellite displays to mission users. |
| Project Title | Identification and construction of models to demonstrate cabilities of NOAA vAIP Knowledge Graph |
| Mentor name | Ryan Berkheimer |
| Host office/program/lab | NCEI |
| Host/Lab location | Asheville, NC |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Physics, Computer Science |
| Required Coding Skills | |
| Python | Interm |
| R | Interm |
| Fortran | Basic |
| UNIX | Basic |
| Linux | Interm |
| GIS | Interm |
| Other skills required | Preference for understanding of ML models and tools, knowledge graphs and interoperability, cloud computing tools and techniques, spatiotemporal computing, |
| Skills interns will learn during internship | Interoperability, OGC standards, |
| Project Description | The architect of the NESDIS Common Cloud Framework (NCCF) Access and Archive Service (NAAS) is seeking motivated and creative individuals to investigate and implement use cases leveraging the new NOAA vAIP Knowledge Graph capability. This digital twin capability provides a fully interoperable knowledge of all of NOAA’s data, from real time data streams of satellite, in situ, and other remote sensor data, to earth science study data, to models and model products, to decisions based on the data holdings in part, whole, or aggregate. With guidance from the system architect, as well as physical and data scientists, the interns will have the opportunity to identify and construct models that demonstrate the power of a fully linked NOAA enterprise - deriving new insights and predictions that showcase the power of a true earth centric wisdom sharing capability. |
National Weather Service (NWS)
| Project Title | Improving tropical cyclone forecasting through climatological data analysis and evaluation of models |
| Mentor name | John Cangialosi |
| Host office/program/lab | National Hurricane Center |
| Host/Lab location | Miami, FL (or remote) |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Meteorology |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Basic |
| MetPlus | Basic |
| Fortran | Basic |
| UNIX | Basic |
| Linux | Interm |
| GIS | Interm |
| Other skills required | Strong interest in tropical cyclones |
| Skills interns will learn during internship | Operational meteorology procedures |
| Project Description | NHC is looking for a student intern to help tabulate numerous climatological statistics of tropical cyclones to better answer frequently asked questions by the media and improve their climatology website. In addition, NHC is always trying to improve their forecasts. A part of the project would be to investigate challenging cases and provide guidance to forecasters that end up in future challenging situations. |
| Project Title | Wave modeling for coastal application |
| Mentor name | Ali Abdolali |
| Host office/program/lab | NCEP Environmental Modeling Center (EMC) |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Physical Oceanography, Coastal Engineering, Atmospheric Science |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Interm |
| Fortran | Interm |
| UNIX | Interm |
| Linux | Interm |
| Project Description | We aim to optimize wave model on unstructured meshes for coastal application. A part of this internship is about running the model and validation studies against buoy, satellite and radar obs. |
| Project Title | Design mobile friendly webpage, developing unified namelist |
| Mentor name | Jun Wang |
| Host office/program/lab | NCEP Environmental Modeling Center (EMC) |
| Host/Lab location | NCWCP, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Computer science/computer engineer |
| Required Coding Skills | |
| Python | Interm |
| Fortran | Basic |
| UNIX | Interm |
| Linux | Interm |
| Other skills required | html. github |
| Skills interns will learn during internship | cloud computing |
| Project Description | The intern will update the EMC websites to make them mobile friendly so that users can access the websites with a computer or a devices. The work includes simplifying navigation, smaller size images, static content in flexible windows, etc. developing unified namelist: The intern will unify the namelists used in several applications to have consistent coding standard. |
| Project Title | Evaluation of NWS air quality models |
| Mentor name | Jeff McQueen |
| Host office/program/lab | NCEP Environmental Modeling Center (EMC) |
| Host/Lab location | NCWCP |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Atmospheric Science, Computer Science |
| Required Coding Skills | |
| Python | Interm |
| C++ | Basic |
| MetPlus | Basic |
| Fortran | Basic |
| UNIX | Interm |
| Linux | Interm |
| GIS | Basic |
| Other skills required | planetary boundary layer, air quality |
| Skills interns will learn during internship | yes...air quality, planetary boundary layer physics |
| Project Description | This proposal evaluates the use of the GFSv16, RRFS-CMAQ at 13 km and RRFS at 3 km meteorological performance for low level fields and boundary layer processes with emphasis on air quality episodes around wildfires. For 2023, the online version of CMAQ built within the RRFS but at a coarser 13 km resolution (RRFS-CMAQ or Online CMAQ), will be transitioned to operations with tightly coupled aerosol radiative forcing . We also will evaluate this coarser Local Area Model (LAM) system that uses the GFS and possibly an advanced physics suite and higher order local boundary layer mixing scheme (Mellor-Yamada-NN ) but at similar resolutions. The NWP models will be evaluated against standard and mesonet fields averaged for various regions during September 2020 western U.S. wildfires. Note, online CMAQ with RRFS meteorology has already been run for these episodes. If time remains, we will also evaluate near real-time performance of GFS-CMAQ and Online CMAQ for Summer 2022 around wild fires. |
| Project Title | Impacts of Marine Component Feedback for Application in the Future NWS Operational Global Forecast System (GFS) |
| Mentor name | Jessica Meixner |
| Host office/program/lab | NCEP Environmental Modeling Center (EMC) |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Atmospheric Sciences, Applied Mathematics, Engineering or related field |
| Required Coding Skills | |
| Python | Basic |
| Fortran | Basic |
| Linux | Basic |
| Project Description | The NWS is moving towards a paradigm of considering the whole earth system and unifying the underlying forecast models via the Unified Forecast System (UFS). The Environmental Modeling Center (EMC) is in charge of developing and implementing these forecast models, one of which is the global deterministic model, the Global Forecast System (GFS). The next version of GFS will include ocean and ice components in addition to the existing atmosphere and wave components. This project will involve examining the impacts of a marine component (wave, ocean, ice) on the fully coupled model with the goal of improving the next GFS. |
| Project Title | Evaluation and validation of JEDI software for NCEP’s next-generation Unified Data Assimilation System |
| Mentor name | Cory Martin |
| Host office/program/lab | NCEP Environmental Modeling Center (EMC) |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Atmospheric Science; (Applied) Math; Physics |
| Required Coding Skills | |
| Python | Interm |
| Fortran | Basic |
| UNIX | Interm |
| Linux | Interm |
| Other skills required | N/A |
| Skills interns will learn during internship | High performance computing; numerical weather prediction; data assimilation |
| Project Description | Data assimilation (DA) is the process of combining observations and previous short-term forecasts to produce initial conditions for improved subsequent model forecasts. The Joint Effort for Data assimilation Integration (JEDI) project is developing the next-generation DA system for use throughout NOAA and other groups in the US. In order for the JEDI software to be accepted for use by the National Centers for Environmental Prediction (NCEP) in a future version of the Global Forecast System (GFS), the various components must each be independently and cumulatively evaluated and verified compared to the current operational software. The intern would work with DA scientists at the Environmental Modeling Center (EMC) on running the GFS components (model and DA) in different configurations, develop and use tools to compute statistics and generate plots, and evaluate results comparing output from the new JEDI-based system to the current operational Gridpoint Statistical Interpolation (GSI) based system. |
| Project Title | Understand the complex challenges to operate the world's largest marine observing network. |
| Mentor name | William Burnett |
| Host office/program/lab | National Data Buoy Center |
| Host/Lab location | Stennis Space Center, Mississippi |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Physical Sciences, Engineering, Mathematics |
| Required Coding Skills | |
| Linux | Basic |
| GIS | Basic |
| Project Description | Develop strategic plan to maintain foundational moored buoy network located globally and incorporate new types of surface and subsurface uncrewed maritime systems to expand the ocean observing network. Meet the challenges of the new Blue Economy while meeting target climate goals. |
| Project Title | Developing strategies to communicate risk-based and probabilistic information to support needs of state and federal partners |
| Mentor name | Melissa Huffman |
| Host office/program/lab | NWS Southern Region Headquarters - Regional Operations Center |
| Host/Lab location | Fort Worth, TX |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Meteorology, Climatology |
| Required Coding Skills | None |
| Other skills required | Skill with GIS is encouraged, but not required. |
| Skills interns will learn during internship | Interns will learn verbal and visual communication skills for providing decision support services to state and federal partners, including the State of Texas and the Federal Emergency Management Agency. They will also have the ability to learn National Weather Service operations and coordination with Weather Forecast Offices, River Forecast Centers, Center Weather Service Units, and Southern Region Headquarters. |
| Project Description | Interested in learning about climate information needs of decision makers? Join the National Weather Service Southern Region Headquarters - Regional Operations Center in summer 2023 to learn about how state and federal agencies use climate information. With this knowledge, participants will work with Regional Operations Center emergency response meteorologists to develop strategies to communicate risk-based and probabilistic information to support needs of state and federal partners like the State of Texas and Federal Emergency Management Agency. This project will focus on developing strategies for risk-based and probabilistic communication of climate information to state and federal National Weather Service partners in an effort to support the National Weather Service's Climate-Ready Nation initiative. Hosted at the National Weather Service Southern Region Headquarters - Regional Operations Center in Fort Worth, TX, this project will be conducted in tree phases: identifying partner information needs; developing communication methods to meet needs; and evaluating proposed methods with National Weather Service state and federal partners. Methods for communication may include (but are not limited to) ArcGIS Online StoryMaps and Map Series, briefing packages and templates created in the Google or Microsoft Suite, or standalone graphics. The project will also include time and visits for the intern to familiarize themselves with National Weather Service partners, line offices, and regional headquarters. |
| Project Title | Specialized verification of Central Pacific Hurricane Center Tropical Weather Outlook inputs and results |
| Mentor name | Chris Brenchley |
| Host office/program/lab | NWS WFO Honolulu |
| Host/Lab location | Honolulu, HI |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Atmospheric Science |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Basic |
| Linux | Basic |
| Other skills required | Tropical Meteorology |
| Skills interns will learn during internship | Collaboration, working in a team environment, data formatting, data presentation, tropical cyclogenesis identification |
| Project Description | The Tropical Weather Outlook (TWO) is a high visibility forecast product which provides users with a general assessment of activity in the tropics pertaining to tropical cyclone formation by identifying for users possible areas where tropical cyclones could develop. TWO's and their companion graphical product are issued routinely every 6 hours (synoptically) from 1 June - 30 November for the central North Pacific basin. The TWO discusses areas of disturbed weather and the potential for tropical cyclone development during the next 120 hours, and includes a categorical and numerical probabilistic genesis forecast, to the nearest 10 percent, for tropical cyclone formation within the next 48 hours and the next 120 hours. The purpose of the study is to take an in-depth assessment of TWO forecaster and forecast tools performance trends, with an aim towards identifying: 1) Best practices for forecasters, and 2) opportunities for improvement of existing forecast tools and techniques. |
| Project Title | Develop new software for AWIPS, providing solutions for enhancements and/or defects |
| Mentor name | Jim Calkins |
| Host office/program/lab | Office of Central Processing |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Computer Science or Meteorology with Computer Science background |
| Required Coding Skills | |
| Python | Interm |
| Linux | Interm |
| Other skills required | Java and Eclipse strongly preferred |
| Skills interns will learn during internship | Software Development Lifecycle |
| Project Description | The Advanced Weather Interactive Processing System (AWIPS) software is used by operational forecasters at all National Weather Service Forecast Offices, River Forecast Centers, and National Centers such as the National Hurricane Center, Storm Prediction Center, and the Weather Prediction Center. The Office of Central Processing provides software updates to AWIPS ranging from small software fixes to new large applications requiring multiple years of development. The Lapenta Intern selected for this position will learn how to develop within the AWIPS architecture and develop software solutions that will benefit the operational forecasters throughout the National Weather Service. |
| Project Title | Improving NOAA/NWS Storm Prediction Center (SPC) Fire Weather Forecast and Verification Techniques |
| Mentor name | Matt Elliott |
| Host office/program/lab | NOAA/NWS/NCEP Storm Prediction Center |
| Host/Lab location | Norman, OK |
| In person/Virtual/No preference | Virtually, In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Meteorology/Atmospheric Science |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Basic |
| Linux | Basic |
| Skills interns will learn during internship | Operational fire weather forecasting skills |
| Project Description | The NOAA/NWS Storm Prediction Center (SPC) is seeking a motivated student who is interested in fire weather and helping the SPC improve their fire weather forecast and verification techniques. The SPC is currently working on a multitude of critical fire weather-related projects, which provides the opportunity for the student and SPC to find a project that aligns with SPC/NWS interests and the student's skill sets. The student would also have occasional opportunities to shadow SPC forecasters on fire weather and severe weather operational shifts. Strong technical skill and extreme attention to detail are highly preferred. |
| Project Title | Improving NOAA/NWS Storm Prediction Center (SPC) Severe Weather Forecast and Verification Techniques |
| Mentor name | Chris Karstens |
| Host office/program/lab | NOAA/NWS/NCEP Storm Prediction Center |
| Host/Lab location | Norman, OK |
| In person/Virtual/No preference | Virtually, In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Meteorology/Atmospheric Science |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Basic |
| Linux | Basic |
| Other skills required | None |
| Skills interns will learn during internship | Operational severe weather forecasting skills |
| Project Description | The NOAA/NWS Storm Prediction Center (SPC) is seeking a motivated student who is interested in severe weather and helping the SPC improve their severe weather forecast and verification techniques. The SPC is currently working on a multitude of severe weather-related projects, which provides the opportunity for the student and SPC to find a project that aligns with SPC/NWS interests and the student's skill sets. The student would also have occasional opportunities to shadow SPC forecasters on severe weather operational shifts. Strong technical skill and extreme attention to detail are highly preferred. |
| Project Title | Cataloguing National Centers for Environmental Prediction (NCEP) probabilistic products across all National Centers (NC) |
| Mentor name | Jim Yoe |
| Host office/program/lab | NCEP Office of the Director |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Meteorology |
| Required Computer Skills | None |
| Other skills required | Forecasting, statistics, and communication |
| Skills interns will learn during internship | Collection, organization, and comparison of communication approaches to forecasting |
| Project Description | The National Centers for Environmental Prediction (NCEP) play a key role in helping the NWS protect our nation's property, lives, and economic well-being. The products and services produced by NCEP are at the core of our weather, water, and climate forecasting and operations. NCEP communicates critical environmental information in a probabilistic manner and has for decades. However, these products and services have been unique to each National Center (NC). As the NWS moves more holistically towards a probabilistic mindset in its forecast process and disseminates more probabilistic-driven products and services, NCEP desires to do this through a more unified approach. The objective of this project is to catalog National Centers for Environmental Prediction (NCEP) probabilistic products across all National Centers (NC), compare and contrast these products to determine similarities and differences, and identify best practices in communicating probabilistic information. These findings will inform NCEP in a coordinated and unified NC approach to communicate probabilistic information. |
| Project Title | Assessment of the behavior of vessels transiting the North Pacific in response to forecast extreme weather hazards |
| Mentor name | Joe Sienkiewicz |
| Host office/program/lab | Ocean Prediction Center |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Data science, GIS, behavioral science, meteorology |
| Required Coding Skills | |
| Python | Interm |
| R | Basic |
| SAS | Basic |
| GIS | Expert |
| Skills interns will learn during internship | Data analysis of vessel weather avoidance behavior |
| Project Description | In mid September 2022, multiple extreme maritime weather events threated the heavily traveled shipping lanes of the North Pacific. The weather events included Typhoon Merbok moving northward and the transition of Merbok into a massive Bering Sea storm that produced winds of hurricane force and seas in excess of 50 feet in height. Over a several day period nearly all trans Pacific shipping routes encountered extreme winds and seas. Commercial vessels are required to transmit their positions via the Automatic Identification System (AIS), the AIS messages are received by nearby ships but also via low earth orbiting satellites. AIS positions are then available for maritime domain awareness, security, and vessel monitoring in near real-time and through an archive. The NOAA Ocean Prediction accesses global AIS data via a DOT Volpe Research Lab developed web application called Seavision. Seavision has an archive of global AIS positions from which vessel tracks can used for further study. This project will access and analyze AIS histories for the time period of interest in the North Pacific to determine vessel's avoidance behavior and critical decision points when voyage plans were altered in relation to evolving and predicted extreme weather threats. If time allows, a simple economic assessment of time lost may be possible by comparing planned versus taken routes. |
| Project Title | Verification of WPC Winter Storm Severity Index |
| Mentor name | Joshua Kastman |
| Host office/program/lab | Weather Prediction Ctr |
| Host/Lab location | College Park |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Atmospheric Science, Geography, Geographical Information Systems, Physics |
| Required Coding Skills | |
| Python | Basic |
| GIS | Basic |
| Skills interns will learn during internship | ArcGIS Pro, HTML, CSS, JavaScript, PHP |
| Project Description | Assess performance of the Winter Storm Severity Index (WSSI) over the past year using a combination of observations (Local Storm Reports (LSR) and NOHRSC Snow Analysis) in ArcGIS. Additionally, a subjective verification will be completed using the National Center for Environmental Information’s (NCEI) Storm Report Database. The goal of this project is to compare the impact forecast from the WSSI to noted impacts from observational datasets. This will include a count of snowfall categories, URMA vs NDFD. The results will be used to help guide improvement to underlying WSSI algorithms |
Oceanic and Atmospheric Research (OAR)
| Project Title | Tracking the ocean biological carbon pump using emerging 'omics approaches |
| Mentor name | Emily Osborne |
| Host office/program/lab | Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | Miami, FL |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Marine Science, Biology, Microbiology, Oceanography |
| Required Coding Skills | None |
| Other skills required | N/A |
| Skills interns will learn during internship | Omics laboratory techniques including sample prep, DNA extraction, PCR, etc. |
| Project Description | The student will be supporting a project aimed at improving our understanding of the biological carbon pump, the oceans main pathway for transporting carbon from the surface ocean to the deep to sequester on geologic time-scales. This project uses 'omics approaches to determine the major biological members that are exported to the deep ocean. This work will center on sediment trap time-series samples collected in collaboration with the US Geological Survey and University of South Carolina in the northern Gulf of Mexico. |
| Project Title | Water mass transformation by Tropical Cyclones in the North Atlantic Ocean |
| Mentor name | Shenfu Dong |
| Host office/program/lab | Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | 4301 Rickenbacker Causeway, Miami, FL 33149 |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | ocean science, physical science, meteorology, math |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Interm |
| Project Description | Tropical cyclones (TCs), also known as hurricanes in the North Atlantic Ocean, are strong weather events that have many societal, economic, and environmental impacts. Previous studies examined the influence of tropical cyclones on the heat uptake by the ocean surface, suggesting that TCs are responsible for up to 0.5 pettaWatts (1015 Watts) of heat uptake globally, which could be capable of sustaining a large amount of the heat transported by the ocean currents, and drive important climate variability around the globe. The way that the ocean absorbs heat from the atmosphere by TCs is by changing the upper layer of the ocean, the so-called mixed layer. As the name suggests, properties of temperature and salinity are well mixed in this layer due to strong surface forcing. The mixed layer in the tropical/subtropical regions are typically 10 to 100 meters deep. As a TC passes, mixing induced by the strong winds deepens the mixed layer. A few days after the TC passage, the mixed layer returns to its previous depth, leaving bellow the heat that was mixed within deeper layers, therefore trapping the heat in the subsurface of the ocean. The trapped heat will eventually flow with large-scale ocean currents. This heat may return to the surface in the following winter, when the mixed layer deepens, or it may stay below the surface and modify water masses along its path. This phenomenon is called water mass transformation. While water mass transformation is a well-documented process, this is the first basin-wide study focusing exclusively on the role of tropical cyclones on water masses transformation It is important to understand how tropical cyclones influence water mass transformation, since water mass characteristics are directly linked to the ocean circulation, climate, and marine ecosystems dynamics and productivity. The goal of this project is to investigate the amount and the pathways of subsurface water masses as they cross the mixed layer during the passage of TCs. For this, available daily outputs of an ocean reanalysis model, such as the HYCOM reanalysis (www.hycom.org), will be used to simulate particle pathways. The TCs locations will be defined by the International NOAA’s Best Track Archive for Climate Stewardship (IBTrACS version 3) dataset. To simulate the surface water particles directly influenced by hurricane conditions, virtual particles will be released along the path of the hurricanes using the Connectivity Modeling System (CMS). These particles will be then transported by the ocean currents and mixing, which are affected by the hurricane passage. These particles and their associated water properties will be then monitored up to one year after the events, and a comparison between years with a lower than normal, normal, and higher than normal number of TCs in the North Atlantic will be performed. The intern can perform the following main tasks: 1) Use the IBTrACS data (which contains hurricane path information) to define the years will high, medium, and low hurricane activity in the North Atlantic. 2) Separate the individual hurricane track parameters (e.g., position, time, intensity) from the selected years. 3) Assess the ocean reanalysis data downloaded to the U. Miami and NOAA computers, and separate the data for the years selected in (1). 4) Write the input files with the location, time and number of particles to be released along the hurricane paths. 5) Run the CMS code with mentors using model outputs and the input files created on step (4). 6) Analyze the outputs from the CMS program and perform some statistics on the number and location of transported particles, and define which subducted and returned to the surface. |
| Project Title | Generating a time series record of biodiversity in South Florida waters using eDNA observations |
| Mentor name | Christopher Kelble |
| Host office/program/lab | Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | Miami, FL |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Marine Science or Biology |
| Required Coding Skills | None |
| Other skills required | No |
| Skills interns will learn during internship | Yes, genomics laboratory analyses |
| Project Description | The intern will assist in DNA extraction, PCR amplification, sequencing, and bioinformatic analysis of eDNA samples collected during oceanographic expeditions to the Florida Keys National Marine Sanctuary, Florida Bay, and the West Florida Shelf. The applicant will learn and help to process collected samples in the lab at AOML, which will include performing DNA extractions, PCR reactions, and gel electrophoresis. Additionally, the applicant will be trained in bioinformatic analysis for matching sequencing data to taxonomic IDs that will be used for deriving biodiversity indicators of microbial communities and primary producers. The intern will communicate and collaborate with a team of scientists at AOML. |
| Project Title | Molecular Microbial Source Tracking and ‘Omics-Based Marine Microbiome Observations to Characterize Environmental Water Quality |
| Mentor name | Christopher D. Sinigalliano |
| Host office/program/lab | Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | Miami, Florida |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Marine Biology, Microbiology, Genetics, Ecology, Biology, Genomics |
| Required Coding Skills | |
| MatLab | Basic |
| R | Basic |
| Other skills required | Desirable but not required - Basic Bioinformatics analysis of sequencing data, basic GIS, basic Linux |
| Skills interns will learn during internship | Bacteriological culturing, eDNA extraction, qPCR, MST, DNA/RNA metabarcoding amplicon sequencing, bioinformatics, environmental sample processing. |
| Project Description | This 2023 Internship Opportunity is being hosted through the NOAA Office of Oceanic and Atmospheric Research at the Molecular and Environmental Microbiology Program of the NOAA Atlantic Oceanographic and Meteorological Laboratory located in Miami, Florida. We are seeking one graduate student intern (Masters or Doctoral student) who is interesting in learning a combination marine microbiology and ‘Omics-based molecular skills such environmental DNA extraction, quantitative PCR for specific gene detection, bacterial community DNA sequencing, and bioinformatics analysis of ‘Omics sequencing data for a project investigating microbial impacts from land-based sources of pollution on coastal ecosystems in South Florida, and how these LBSP impacts may be influenced by weather events, climate change, and sea level rise. Water Quality is critical for healthy marine ecosystems, healthy coastal communities, and a healthy economy. Coastal water quality can be highly impacted both directly and indirectly by land-based sources of pollution (LBSP), climate change, and sea level rise. Changing patterns of land-use, urban development, state of sanitary infrastructure, rainfall, storm events, coastal tidal flooding, and other drivers of LBSP that are impacted by climate change and sea level rise can have profound influence in the patterns and characteristics of the transport and discharge of land-based pollutants, including microbial contaminants, to the coastal zone. The NOAA Atlantic Oceanographic and Meteorological Laboratory (NOAA-AOML) in Miami, Florida, conducts a wide variety of research utilizing both traditional culture-base microbiology and ‘Omics-based molecular methods (including gene-specific quantitative PCR, community DNA sequencing, and metagenomic bioinformatic analysis of marine microbiome community structures) to investigate the community structure and ecosystem function of microbiomes in a wide variety of marine ecosystems, and to provide environmental intelligence to marine resource managers to help detect, track, and mitigate LBSP-associated microbial contaminants and pathogenic microbes in these marine ecosystems, and to better understand how aspects of climate change and sea level rise affects these. The Molecular and Environmental Microbiology Program at NOAA-AOML is offering an internship opportunity in FY 2023 for one Lapenta Internship graduate scholar (either a Masters or Doctoral student) to join our team working on a specific set of inter-connected projects using qPCR based microbial source tracking assays and 16S amplicon bacterial community sequencing to track LBSP-associated host-specific fecal indicator bacteria and pathogens and to characterize microbiome communities of discharge and receiving waters for the South Florida Biscayne Bay watershed and coastal inlet contributing areas. With the guidance of his/her mentors at AOML, the intern will develop a specific personal project internal to this broader effort to measure host-specific fecal bacteria indicators and document microbiome community structure at different contributing sites within this watershed and in the Biscayne Bay receiving waters. The intern will learn methods associated with microbial water quality assessment, including basic bacteriology methods of viable fecal indicator enumeration, extraction and purification of environmental DNA from marine and aquatic samples, qPCR-based molecular microbial source tracking, community amplicon DNA sequencing of bacterial 16S rRNA genes from eDNA samples, computer bioinformatics analysis of sequencing data, and the interpretation and synthesis of molecular and culture data generated from the project. The intern will analyze results in relation to the other data and metadata generated by other researchers in the larger water quality research effort. This will include the investigation of potential relationships of culture and molecular results with parameters such as nutrient concentrations, physical measurements (temp, salinity, dissolved oxygen, pH, etc.), and incidence of other special driver events (such as previous rainfall, storms or storm surges, intentional canal discharges, sewage/septic leaks, coastal tidal flooding, etc.). The intern will prepare a final report and/or presentation based upon the results of his/her directed research. This training opportunity will require in-person laboratory work by the intern at the AOML facility in Miami Florida. The intern will be trained at in all required aspects and laboratory methods for their project, and will be supervised by the primary mentor, Dr Christopher Sinigalliano (federal PI of the AOML Molecular and Environmental Microbiology Program), and by Dr. Maribeth Gidley (Environmental Health Research Physician with the Cooperative Institute for Marine and Atmospheric Studies, University of Miami). It is highly desirable that the prospective intern have some familiarity and confidence with statistical analysis using the R software package or application, and/or the MatLab software package. It is also useful if the intern has some experience with either marine biology, microbiology, ecology, genetics, genomics, bioinformatics, and/or climate science disciplines. |
| Project Title | Aircraft and mobile measurements of greenhouse gases and air pollutants in New York City |
| Mentor name | Xinrong Ren |
| Host office/program/lab | Air Resources Lab (ARL) |
| Host/Lab location | College Park, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Atmospheric Science/Chemistry |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Basic |
| Other skills required | Willing to travel to New York City (~3 times, 3-5 days each) during the field deployments |
| Project Description | NOAA/ARL is seeking a summer intern graduate/undergraduate student to participate in a field project to conduct aircraft and mobile measurements of greenhouse gases and air pollutants in New York City in summer 2023. The intern student will be involved in instrument operations in the field, data collection and analysis, and presentation of results in a scientific meeting. |
| Project Title | Data visualization and/or information management for Earth Radiation Budget |
| Mentor name | Victoria Breeze |
| Host office/program/lab | Climate Program Office (CPO) |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Any are welcome to apply, but students with a background in atmospheric science, climatology, or physics would be the most familiar with ERB science foci while students with a background in public policy or environmental ethics may find ERB’s focus on climate intervention relevant. |
| Required Coding Skills | None |
| Other skills required | n/a |
| Skills interns will learn during internship | Data visualization, bibliometrics, literature review |
| Project Description | NOAA’s Earth’s Radiation Budget (ERB) Initiative is a multi-year research initiative to investigate natural and human activities that might alter the reflectivity of the stratosphere and the marine boundary layer, and the potential impact of those activities on the Earth system. ERB research focuses on: (1) Improving our understanding of the energy balance of the Earth system; (2) Establishing a capability to observe and monitor stratospheric conditions; and, (3) Detecting and accurately simulating the impacts of natural and human-caused aerosol injections in the stratosphere and troposphere on Earth’s radiation balance, weather and climate patterns, and other Earth systems. ERB welcomes any intern interested in the growing field of climate intervention research, particularly students who would like to learn more about research program management. Potential Lapenta internship projects are listed below. However, if a student has a particular skill or interest they would like to apply to ERB-relevant topics, we would work with them to incorporate that into their project. (a) Citation map starting from ERB publications matched with research gaps for climate intervention as identified by the National Academies of Sciences, Engineering, and Medicine and other recent national and international reports. (b) Project map, highlighting the four quadrants of ERB research: tropospheric vs. stratospheric and observations vs. modeling. (c) Communications-focused project that explores relevant societal benefits and areas of concern. |
| Project Title | Developing inventory of climate and justice related federal programs |
| Mentor name | Frank Niepold |
| Host office/program/lab | Climate Program Office (CPO) |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | Virtually |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Meteorology, Climate, Anthropology, Education, Sustainability |
| Required Coding Skills | None |
| Other skills required | Policy and budget analysis, writing (plain language) |
| Skills interns will learn during internship | Yes, the interns learn policy and budget analysis, landscape analysis, and climate education and empowerment program analysis |
| Project Description | The resulting catalog is the first product of its kind to look across the entire federal government. NOAA’s initiative focused on federal programs and plans designed to build capacity and increase knowledge across federal and non-federal government agencies and broadly across civil society. The greatest potential for increasing effectiveness and resource utilization, therefore, lies in identifying intersecting activities, objectives, target audiences, resources, and programs that can be improved through increased coordination and collaboration. The value of and potential for intersectional collaboration and strategic alignment of resources and programs are particularly evident in the United States, where many federal departments and agencies, plus highly diverse non-federal communities, organizations, governments, and sectors work on climate change mitigation, adaptation, and climate justice. |
| Project Title | Advancing equitable climate adaptation through research and community engagement |
| Mentor name | Sean Bath |
| Host office/program/lab | Climate Program Office - Climate Adaptation Partnerships |
| Host/Lab location | TBD (one of 12 regions) |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Environmental Sciences, Social Sciences, Geography, Atmospheric Sciences, Planning and/or Policy Studies, Natural Resources Management, Economics, Public Health |
| Required Coding Skills | None |
| Other skills required | Some level of interdisciplinary studies (e.g. intentionally mixed degrees or courses that explicitly integrate disciplinary knowledge) |
| Project Description | The NOAA Climate Adaptation Partnerships program (formerly the RISA program) advances equitable adaptation through sustained regional research and community engagement. Funded by 5-year cooperative agreements with NOAA, the work is accomplished by teams of research institutions, nonprofit organizations, and state/local/Tribal governments in multi-state regions. The Lapenta Interns will contribute to the interdisciplinary research and engagement efforts of one of the 12 regional CAP/RISA teams. The final scope of projects will be determined in negotiation between prospective interns, the CAP/RISA team, and NOAA CPO mentors. Please see the https://cpo.noaa.gov/RISA for more information about the program and its team projects. |
| Project Title | Designing outreach materials on drought for NOAA's Tribal partners |
| Mentor name | Crystal Stiles/Doug Kluck |
| Host office/program/lab | Climate Program Office/Natl Ctr for Env Information |
| Host/Lab location | TBD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Native Studies, Communications, Native Art/Language |
| Required Coding Skills | None |
| Project Description | The student would be tasked with designing NOAA outreach materials that are culturally appropriate for our tribal partners and translated (written and/or verbal) into one or more commonly-spoken Native languages and using traditional art, if appropriate. Primary tasks for the student would include: |
| Project Title | ‘Ships of Opportunity’ Ocean Acidification Spatiotemporal Analysis |
| Mentor name | Dwight Gledhill |
| Host office/program/lab | Ocean Acidification Program |
| Host/Lab location | Silver Spring |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Geographic information systems, data science, computer science |
| Required Coding Skills | |
| R | Expert |
| GIS | Interm |
| Other skills required | GIS or spatial analysis in R or QGIS, limited access to ArcGIS; Ability to work as part of a team and independently |
| Skills interns will learn during internship | Ocean acidification, ocean biogeochemistry, data communication, ocean observation and monitoring |
| Project Description | Establishing a robust ocean observing network for the U.S. Large Marine Ecosystems (LME) is essential for understanding ocean change and its impacts to ecosystems and communities that depend on them. Ships of Opportunity (SOOP) like research cruises and commercial vessels help expand our temporal and aerial coverage of these LME’s, however we lack a robust method of comparing and assessing coverage. Previous efforts provided an initial model for mapping and tracking SOOP that this internship will build upon to help inform future investment and track past spatiotemporal coverage of LMEs with respect to ocean acidification monitoring. The incoming William LaPenta Intern will further develop the modeling approach and create a uniform mapping protocol that allows spatiotemporal analyses to quantitatively compare LMEs. Some examples of modeling improvements for this project could be establishing an appropriate map projection and resolution for spatiotemporal analyses, coding to scrape data from the web, creating data maps or animations, and coding and conducting statistical analyses. This enables the NOAA Ocean Acidification Program to optimize investment opportunities for advancing the observing network to respond to long term observational needs efficiently. |
| Project Title | Great Lakes ice cover in response to a changing climate |
| Mentor name | Jia Wang |
| Host office/program/lab | GLERL |
| Host/Lab location | Ann Arbor, MI |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Great Lakes ice cover in response to a changing climate |
| Required Coding Skills | |
| Python | Basic |
| MatLab | Basic |
| C++ | Basic |
| MetPlus | Basic |
| R | Basic |
| SAS | Basic |
| Fortran | Basic |
| UNIX | Basic |
| Linux | Basic |
| GIS | Basic |
| Other skills required | No |
| Project Description | Great Lakes ice cover and thickness are not only controlled by local temperature, but also impacted by external, remote teleconnection forcing. Both Great Lakes and Arctic sea ice variability is driven by a combination of these teleconnection patterns, such as Arctic Oscillation/North Atlantic Oscillation, El Niño-Southern Oscillation, Pacific Decadal Oscillation, and Atlantic Multidecadal Oscillation, whose thermodynamic impacts are difficult to separate. GLERL intend to conduct in-depth research linking climate teleconnection patterns to the Great Lakes and Arctic climate and ice cover/thickness, leading to development of hindcast models: multi-variable non-linear regression models. The project is part of the prediction of ice cover/thickness in the Great Lakes and the Arctic in response to a changing climate on seasonal, interannual, and decadal time scales, which enables us to provide information to broader users in search and rescue operations, navigation (commercial shipping), and recreational ice fishing during winter season. These forecasts provide decision makers with tools to aid in protecting the Great Lakes and the Arctic community. |
| Project Title | Improving Simulations of Sea Level Change in Complex Coastal Regions |
| Mentor name | John Krasting |
| Host office/program/lab | OAR - Geophysical Fluid Dynamics Laboratory (OAR) |
| Host/Lab location | Princeton, NJ |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Oceanography |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Interm |
| UNIX | Interm |
| Linux | Interm |
| Other skills required | Potential candidates should have undergraduate level exposure to fluid dynamics and/or oceanography and/or related climate science fields. Familiarity with Linux and the Python programming language are preferred. |
| Skills interns will learn during internship | This internship will provide the intern with the skills to explore sea level and coastal inundation processes at weather to climate time scales. The intern will also gain experience with ocean models and comparing simulations with observations. |
| Project Description | Numerical climate models developed at NOAA-GFDL are used to simulate coastal sea level variability at hourly to multi-centennial timescales. The models are critical to understanding the drivers of observed sea level changes. In addition, future sea level projections from these models inform government and private sector coastal planning and risk assessment. Validating and improving these models requires extensive comparisons with observations from satellite altimeters and tide gauges. This project will utilize new global observational reference products to characterize the spatial structure and magnitude of biases in dynamic sea level in coastal regions and near boundary currents. The newly developed observations will be compared with simulations from a hierarchy of NOAA-GFDL global ocean models with varying horizontal resolution. The project hypothesis is that these biases can be related to errors in the representation of physical processes controlling sea level variability, including winds, river discharge, and friction at the ocean’s boundaries. These findings can then be used to better prioritize model improvements. Intern Duties/Responsibilities: During the course of the internship, the candidate will collaborate with mentors to identify key coastal regions for comparison with observations. The candidate will implement a set of computer analysis routines to compare sea level output from different NOAA-GFDL models to observational reference products. The candidate will help formulate and test hypotheses for processes underlying model-data discrepancies, which will likely include analysis of related climate variables. Expected Outcomes: The candidate will have gained knowledge about the key processes that are important to coastal sea level change and those underlying model-data discrepancies. The candidate will also have contributed to model development efforts at NOAA-GFDL through the implementation of routine monitoring of these key fields into the modeling workflow. The candidate will have gained substantial hands-on expertise in the processing, modeling, and the analysis of large, varied, datasets. At the completion of the internship, the candidate will be required to produce a final report. Guidance/Supervision: Primary guidance will be provided by Dr. John Krasting, Physical Scientist in NOAA-GFDL’s Ocean and Cryosphere Division. The candidate will also work closely with Dr. Christopher Little, a senior scientist at Atmospheric and Environmental Research, based in Lexington, MA. |
| Project Title | Applying data analysis and coding skills to analyze and visualize the data from unexplored areas of the ocean |
| Mentor name | Trish Albano |
| Host office/program/lab | NOAA Ocean Exploration |
| Host/Lab location | Silver Spring, MD / Remote |
| In person/Virtual/No preference | Virtually |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | We are open to all majors! |
| Required Coding Skills | |
| R | Basic |
| GIS | Basic |
| Other skills required | N/A |
| Skills interns will learn during internship | Increased knowledge of R and GIS; ecological modeling; statistics; data management; data analysis; deep sea biology/ecology |
| Project Description | NOAA Ocean Exploration is dedicated to exploring the unknown ocean, unlocking its potential through scientific discovery, technological advancements, partnerships, and data delivery. We are seeking a Lapenta Intern who would be interested in either applying or learning data analysis and coding skills to analyze and visualize the data that NOAA Ocean Exploration collects in these unexplored areas. The intention of this student opportunity is to have a Lapenta Intern shape NOAA Ocean Exploration's data management, analysis, and visualization efforts by supporting the office's Data Analytics and Synthesis Team. The data analytics and visualization effort is a new priority the office is undertaking, so the incoming intern will have the opportunity to choose from a wide variety or create their own project (the team will also provide ample guidance). We are looking for a Lapenta intern with interest and experience in data analysis, visualization, and coding, however, we are willing to teach the incoming intern the tools to analyze and visualize data (including coding and GIS skills). The project and priorities can also be tailored to the student’s background and interests. The Lapenta Intern will have an opportunity to look at these data and be the first to provide insights to the ocean-interested community on unexplored areas of the ocean. The intern will also gain skills in understanding deep-sea habitat and data types including seafloor mapping data, environmental data, and data on species occurrences and distributions. |
| Project Title | Investigating similarities and differences in the diagnosed reflectivity field between the Multi-Radar/Multi-Sensor (MRMS) and GOES Radar Estimation via MachineLearning |
| Mentor name | Jeff Duda |
| Host office/program/lab | Global Systems Laboratory |
| Host/Lab location | Boulder, CO |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Meteorology |
| Required Coding Skills | |
| Python | Basic |
| Fortran | Basic |
| UNIX | Basic |
| Linux | Basic |
| Other skills required | Familiarity with basic forecast verification principles |
| Skills interns will learn during internship | Numerical weather prediction, advanced forecast verification techniques |
| Project Description | The intern will investigate the similarities and differences in the diagnosed reflectivity field between the Multi-Radar/Multi-Sensor (MRMS) and GOES Radar Estimation via MachineLearning to Inform NWP (GREMLIN) systems to determine the utility of the GREMLIN field in verifying NWP forecasts. The student should rely primarily on object-based techniques to assess the texture, shape, size, and magnitude of reflectivity objects in both data sets using a large sample of cases for statistical robustness. Currently, synthesized mosaics of radar reflectivity rely almost entirely on data generated by WSR-88Ds and TDWRs for measurement of precipitation location, coverage, intensity, and internal storm structures in the US and neighboring countries. By definition, this means that sample coverage is restricted to land-based areas where the radar sites are constructed and operated - radar data is not collected over large expanses of water. However, this fact only applies to the MRMS system, which is the sole means of verifying reflectivity forecasts from modern sophisticated NWP forecast systems such as the High-Resolution Rapid Refresh (HRRR). Advanced satellite products are available from the GOES satellite network which can be useful in obtaining information on cloud location and structure over water. The GREMLIN system uses machine learning and GOES observations to create simulated radar data over land and water, and initial tests using GREMLIN data for radar data assimilation in convective-scale NWP simulations has proven promising. While GREMLIN data tend to have a coarser spatial structure, the added coverage could provide for the ability to verify models such as the HRRR (and other modeling systems such as the Rapid Refresh Forecast System) in areas where it currently cannot be performed. For example, use of GREMLIN as a verification source would provide opportunity for tropical cyclone and thunderstorm verification over the Caribbean. The ability of the GREMLIN data to serve as useful for verification has not yet been tested, however. The Lapenta intern would perform a mostly object-based comparison between MRMS and GREMLIN data to quantify the similarities in the spatial structure of reflectivity data for the purpose of using GREMLIN data for verification of high-resolution reflectivity forecasts over water (and possibly over land, too). In the object-based approach to verification, contiguous regions of grid points are grouped into a single object, thus compressing the regular grid into a smaller group of objects, each of which is linked to several quantities describing the location, shape, size, intensity, etc. of each object. |
| Project Title | Evaluating ensemble hub-height wind forecasts |
| Mentor name | Dave Turner |
| Host office/program/lab | OAR / Global Systems Laboratory |
| Host/Lab location | Boulder, CO |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Meteorology, atmospheric science, mathematics, physics |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Basic |
| Linux | Basic |
| Other skills required | no |
| Skills interns will learn during internship | Analysis techniques |
| Project Description | Hub-height (e.g., 80 m AGL) wind forecasts from NOAA's operational weather prediction models serve as the foundational forecasts for the renewable energy (RE) community. The RE community has traditionally only used deterministic forecasts in their applications, but desired more probabilistic information. This project will evaluate the spread-skill score of the Rapid Refresh Forecast System (RRFS), a 3-km horizontal grid spacing ensemble model, using observations from a range of different land-based and offshore locations. Traditionally, only convective evaluations have been performed for ensemble modeling systems like RRFS, so this effort represents a new chapter in addressing other user communities and their needs for probabilistic forecasts. |
| Project Title | Evaluation of convection-allowing forecasts in preparation for version 2.0 of the Rapid-Refresh Forecast System |
| Mentor name | Ligia Bernardet |
| Host office/program/lab | Global Systems Laboratory |
| Host/Lab location | Boulder, CO |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Atmospheric science or meteorology |
| Required Coding Skills | |
| Python | Basic |
| UNIX | Basic |
| Linux | Basic |
| Skills interns will learn during internship | Interns will learn how to run the state-of-the-art SRW App and contribute to evaluation and planning of the next version of the RRFS |
| Project Description | Collaborate with scientists from the Developmental Testbed Center (NOAA and the National Center for Atmospheric Research) to evaluate convection-allowing, CONUS-wide forecasts in preparation for operational implementation of version 2.0 of the Rapid-Refresh Forecast System, a specific configuration of the state-of-the-art, Unified Forecast System Short-Range Weather Application (SRW App) that will provide lifesaving information in face of tornadic storms, aviation hazards, and other life threatening events. Work will include running the SRW App, evaluating output, and assessing key metrics like bias and error of RRFS prototype configurations. |
| Project Title | Analysis of Texas Ozone Profiles |
| Mentor name | Gary Morris |
| Host office/program/lab | Global Monitoring Lab |
| Host/Lab location | Boulder, CO |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | meteorology, chemistry, physics, environmental science, mathematics, statistics |
| Required Coding Skills | None |
| Other skills required | Being able to code in any language would be useful; also, familiarity with spreadsheets and making graphs/figures would be helpful |
| Skills interns will learn during internship | atmospheric structure, atmospheric chemistry, presentations, making effective graphics |
| Project Description | Ozone is an important atmospheric trace gas found mainly in the stratosphere that protects the surface from high energy ultra-violet sunlight. But ozone is also found in the lower atmosphere near the surface, where it is a pollutant that causes respiratory issues and damages crops. Since 2004 more than 1000 weather balloons specially instrumented to measure meteorological parameters and ozone concentrations from the surface to nearly 30 km altitude were released from sites around Texas, including Houston (the largest dataset), Austin, Beaumont, El Paso, and San Antonio. After completing a quality check of these data (including comparisons with nearby surface measurements and satellite observations) and applying the appropriate corrections recommended by the World Meteorological Organization, we will explore these data for long-range transport events (i.e., biomass burning influences), stratospheric intrusions, and local production (both natural and human induced). Transport modeling and remote sensing data can aid us in identifying upwind source regions where ozone precursors may have influenced the vertical distribution of ozone in our balloon samples. |
| Project Title | Studying marine biogeochemistry and the impacts of climate change on ocean ecosystems with biogeochemical Argo floats |
| Mentor name | Andrea Fassbender |
| Host office/program/lab | PMEL/OCRD/GOBOP (https://www.pmel.noaa.gov/gobop/) |
| Host/Lab location | Seattle, WA |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | chemistry, engineering, computer science |
| Required Coding Skills | None |
| Other skills required | It would be ideal if the intern had some basic experience with coding in Matlab, Python, or R. |
| Skills interns will learn during internship | Critical scientific thinking, presenting scientific information orally |
| Project Description | Biogeochemical processes that transform and transport chemical elements in the ocean are critical for supporting marine food webs and regulating climate on Earth. These processes (such as: primary production, organic matter export to the deep ocean, denitrification, etc.) can be quantified by measuring the concentrations of various dissolved tracers in the ocean environment and by using models, ranging from simple to complex, to evaluate the mechanisms governing tracer concentration changes over time. Persistently observing the ocean environment is critical for understanding marine ecosystems and how they may be impacted by a changing climate. Particularly because this understanding provides the foundation upon which the coupled Earth system models, used to make future climate projections, are built. Observational density in space and time has been a major challenge for the study of marine biogeochemical processes. Traditionally, measurements from sporadic research cruises with limited spatial coverage have been used for model validation and to draw conclusions about processes operating on longer timescales and across much larger spatial areas. Biogeochemical (BGC) Argo floats (https://www.youtube.com/watch?v=30XfCTb6ja0) have recently emerged as a solution to this problem. BGC Argo floats are drifting autonomous profiling robots that measure at least one of six biogeochemical parameters (dissolved oxygen, pH, nitrate, particle backscatter, chlorophyll fluorescence, and downwelling irradiance) from the ocean’s surface to a defined depth (usually ~2,000 m). BGC Argo floats collect profiles at high temporal resolution (typically once every 10 days) and for a long period of time (average life span of ~4 years). Our group at PMEL specializes in using observations from these profiling floats to study the transformation and transport of chemical elements at the ocean surface and within the ocean interior to understand how marine ecosystems function and may be impacted by climate change. Key processes of interest include the production of organic matter by marine phytoplankton in the sunlit upper ocean, the export of that organic matter to the dark abyss by gravitational sinking and ocean physics, the exchange of carbon dioxide with the atmosphere, and the acidification of ocean waters caused by anthropogenic carbon accumulation. We are seeking an intern who is interested in using biogeochemical data collected from these floats to investigate processes including (but not limited to) primary production, carbon export, air-sea carbon dioxide exchange, and ocean acidification. This is an exciting opportunity to use cutting-edge instrumentation and techniques to study oceanographic regions that are extremely important for global biogeochemical cycling. Interested applicants are encouraged to learn more about the international Argo Program here: https://www.arcgis.com/apps/Cascade/index.html?appid=a170a0d522bb42f1a019e4e473cf1bdd |
| Project Title | Using saildrone observations to validate numerical predictions and other global datasets in the tropics (hurricanes) and Arctic (sea ice). |
| Mentor name | Chidong Zhang |
| Host office/program/lab | PMEL |
| Host/Lab location | Seattle |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | STEM |
| Required Coding Skills | |
| Python | Interm |
| UNIX | Basic |
| Other skills required | Basic statistical analysis |
| Skills interns will learn during internship | Processing of in situ and global gridded data, scientific research procedures and communication |
| Project Description | In this project, interns will join a NOAA science team to process data from saildrone observation missions in the tropics and Arctic. Saildrones are uncrewed surface vehicles powered by solar and wind energy with zero emission. They have been deployed in the tropics to observe hurricanes and in the Arctic to observe environmental conditions near sea ice. In both cases, the accuracy of numerical prediction models and other global datasets (i.e., satellite observations) are little known because of a lack of in situ observations. Saildrone observations have provided special data to fill the gap. Interns will also have opportunities to participate in real-time operation of saildrone deployment. |
| Project Title | Quantifying and understanding subsurface persistence of marine heatwaves from Argo floats |
| Mentor name | Zachary Erickson |
| Host office/program/lab | PMEL |
| Host/Lab location | Seattle, WA |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | scientific or computing field |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Interm |
| Other skills required | Experience programming in either Python or Matlab, or another high level scripting language with willingness to learn Python or Matlab |
| Skills interns will learn during internship | Observational physical oceanography |
| Project Description | Marine heatwaves (MHWs), or periods of prolonged increased sea surface temperature, can dramatically impact marine ecosystems. These detrimental effects are more pronounced when MHWs persist for more than one year, such as the 2013-2016 “Blob” in the Gulf of Alaska that led to massive die-offs in salmon and other economically important fisheries. Although MHWs are defined by ocean temperature at the surface, the subsurface temperature plays an important role in how long MHWs persist. Recent research from the University of Washington and NOAA/PMEL (Scannell et al., 2020) showed that mixing of warm surface waters during the “Blob” downward into the subsurface ocean played a key role in prolonging this particularly catastrophic MHW. This summer project will look more broadly at global MHWs to determine how the mixing of warm surface waters into the subsurface ocean affects the persistence of MHWs throughout the world oceans. The main dataset to be analyzed is a set of monthly near-global maps of ocean temperature and salinity from the surface to 2000 m from a vast network (>4000) of ocean drifters called Argo floats, many of which are deployed by PMEL. The intern will use already-established metrics to determine when and where MHWs have occurred over the past two decades and the depth to which they extend in the water column, and will then determine to what extent sub-surface heat anomalies caused by downward mixing of high surface temperatures leads to stronger or more persistent (i.e., multi-year) MHWs. |
| Project Title | Developing and assessing gustiness models for air-sea flux applications |
| Mentor name | Meghan Cronin |
| Host office/program/lab | NOAA / OAR / PMEL |
| Host/Lab location | Seattle |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Atmospheric Sciences, Meteorology, Physics, Oceanography, Engineering |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Basic |
| UNIX | Basic |
| Other skills required | Undergraduate level coursework in Mathematics and Physics is required for this project. Experience using scientific scripting and visualization computer languages is required. |
| Skills interns will learn during internship | The student will become familiar with how to quantify the exchange of heat, moisture, and momentum between the ocean and atmosphere; with a range of open ocean high-resolution wind data; with some coupled ocean-atmosphere dynamics; the global ocean observing system; numerical weather prediction models and surface satellite-based products; and with analysis tools and scientific programming languages. The student will be expected to write a comprehensive final report that may lead to a peer-reviewed publication. The student is also expected to present the results at either PMEL or PSL and the Hollings Scholar conference. |
| Project Description | The objective of this study is to develop and assess gustiness models for bulk air-sea heat, moisture, momentum and gas flux algorithms. The project will use historic high-resolution buoy, ship, and/or Uncrewed Surface Vehicle (USV) observations to measure the full wind variance for different temporal and spatial averaging, and the missing variance if averaged wind products are used instead. The project may also test various model parameterizations of the missing variance when averaged wind fields are used to estimate bulk fluxes. It is hoped that this work will contribute to the next generation of bulk air-sea flux algorithms used in NOAA’s coupled and uncoupled prediction models, satellite algorithms, and reanalysis products of weather, climate, and the ocean. |
| Project Title | Assessing air-sea fluxes and their state variables in Numerical Weather Prediction models and satellite products |
| Mentor name | Meghan Cronin |
| Host office/program/lab | NOAA OAR PMEL |
| Host/Lab location | Seattle |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Meteorology, physical oceanography, air-sea interaction |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Basic |
| UNIX | Basic |
| Other skills required | Undergraduate level coursework in Mathematics and Physics is required for this project. Experience using scientific scripting and visualization computer languages is required. Knowledge of MATLAB or python would be very helpful. |
| Skills interns will learn during internship | The student will become familiar with the Earth’s energy budget, law of the wall, methods by which bulk latent and sensible heat fluxes can be estimated, and challenges associated with ocean observations. The student will become familiar with satellite products, NWP models, and data from the global ocean observing system. The student will be expected to write a comprehensive final report that may lead to a peer-reviewed publication. |
| Project Description | The objective of this study is to use high measurements of air-sea heat and momentum fluxes and their state variables (wind speed and direction, air temperature, humidity, sea surface temperature, rain rate, barometric pressure, incident solar radiation and longwave radiation) as reference time series to assess the quality of these variables in Numerical Weather Prediction (NWP) analyses and reanalyses and in satellite products. The assessments will be performed by comparing co-located matchups in the reference data and the gridded products. |
| Project Title | Diurnal cycle of the atmospheric boundary layer stability in NCEP models |
| Mentor name | Meghan Cronin |
| Host office/program/lab | NOAA OAR PMEL |
| Host/Lab location | Seattle |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Meteorology, physics, oceanography, climate sciences |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Basic |
| UNIX | Basic |
| Other skills required | Undergraduate level coursework in Mathematics and Physics is required for this project. Experience using scientific scripting and visualization computer languages is required, ideally python, although Matlab, GrADS, R, or similar would also be suitable. |
| Skills interns will learn during internship | The student will learn about the Earth’s energy budget, physics of air-sea interaction, basics of atmospheric turbulence, methods by which bulk latent and sensible heat fluxes can be estimated, and strengths and weaknesses of NWP models. In addition, the student will become familiar with in situ measurement systems and data from the global ocean observing system. The student will be expected to write a comprehensive final report that may lead to a peer-reviewed publication. They will also gain experience presenting to other scientists. The student will develop their programming skills and knowledge of statistics. |
| Project Description | The objective of this study is to assess the high frequency variations of air-sea temperature differences and heat fluxes in Numerical Weather Prediction (NWP) simulations. Hourly model output will be used to understand the amplitude and phase of the diurnal cycles of ocean surface and lower atmosphere quantities. Where possible, in situ observations may be used to judge the realism of the simulations. The student will work on a day-to-day basis with the PMEL Ocean Climate Station (OCS) team, led by PI Dr. Meghan Cronin and Dr. Dongxiao Zhang (UW CICOES), and will be co-mentored by Dr. Jack Reeves Eyre (NWS/NCEP/CPC, ERT). This study is part of a collaborative project with Drs. Arun Kumar, Jieshun Zhu and Jack Reeves Eyre of NOAA/NWS/NCEP/CPC. |
| Project Title | Become the First Ever Student Ambassador for the Unified Forecast System (UFS)! |
| Mentor name | Leah Dubots |
| Host office/program/lab | Weather Program Office (WPO) |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Meteorology, Atmospheric Science, Environmental Science, Computer Science, Environmental Engineering, or related fields, Political Science, Policy, and Social Science fields will also be considered |
| Required Coding Skills | |
| Python | Interm |
| MatLab | Basic |
| MetPlus | Basic |
| Fortran | Basic |
| UNIX | Basic |
| Linux | Basic |
| Other skills required | None |
| Skills interns will learn during internship | Leadership, communications, community engagement, stakeholder engagement |
| Project Description | Join the Earth Prediction Innovation Center (EPIC) and Unified Forecast System (UFS) teams this summer as our first ever student ambassador for the UFS! This summer you will have the unique opportunity to evaluate the UFS Short Range Weather (SRW), Medium Range Weather (MRW), or Hurricane Analysis and Forest System (HAFS) applications for its usability in an academic setting and document your experience to inform future training, tutorials, and support for UFS applications made available through EPIC. You will also assist in developing an engagement and outreach plan tailored to getting students more involved with the UFS, provide a student perspective to UFS and EPIC-based strategies to ensure that academic and industry stakeholders are represented and engaged, and inform the development of the second annual Unifying Innovations in Forecasting Capabilities Workshop (UIFCW). Students participating in this project will finish the summer with a better understanding of how to set up a cloud environment to run a UFS application, work with stakeholders to translate their needs into technical requirements, and develop improved communication and leadership skills. |
| Project Title | Analysis of Benefits and Potential for Transition of Innovations to NOAA Operational Weather Forecasting Systems |
| Mentor name | Jose-Henrique Alves |
| Host office/program/lab | Weather Program Office |
| Host/Lab location | |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Earth Sciences, Applied Science, Engineering, Innovation, Management |
| Required Coding Skills | None |
| Other skills required | Interest in Earth Sciences, Innovation, Risk Analysis, Community Engagement, Public Policy |
| Skills interns will learn during internship | Program Management, Social Sciences and Science & Technology Research & Development, Impact Analysis of Innovations, Risk Analysis |
| Project Description | NOAA’s Weather Program Office (WPO) is launching the Innovations for Community Modeling Competition integrating four of its Programs: Joint Technology Transfer Initiative (JTTI), Earth Prediction Innovation Center (EPIC), Subseasonal to seasonal (S2S) and Atmospheric Composition (AC). The competition will select 10-15 projects with cutting-edge technical and scientific solutions for challenging needs to improve NOAA’s current operational weather forecasting systems. Projects will focus on developing the Unified Forecast System (UFS) of the future. The Lapenta Internship candidate will be involved in integrated aspects of Earth Sciences Innovations in Weather and Climate Forecasting, Program Management, Social Sciences and Science & Technology Research & Development. The objective is to develop a preliminary analysis of awarded projects, exploring concepts of innovation, risk, perception of projects in terms of their potential for transition of innovations to operations, and benefits to NOAA and society. |
| Project Title | Analyzing stakeholder data to improve WPO funding opportunities |
| Mentor name | Tamara Battle |
| Host office/program/lab | OAR Weather Program Office |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Social Science, Statistics, User Experience & Design, Education |
| Required Computer Skills | None |
| Other skills required | For an optimal internship experience, a basic understanding of statistical analysis is required. Some experience with social science data and qualitative/quantitative data analysis is strongly encouraged, but not required. This might include experience with R, SPSS, or other qualitative data analysis software (e.g., MAXQDA, NVIVO) |
| Skills interns will learn during internship | This internship project will provide a unique opportunity for the selected student to gain an internal perspective on the grant funding process at a leading environmental agency, as well as sharpen and/or broaden their skills in statistical analysis, customer experience, and stakeholder outreach. |
| Project Description | To obtain critical stakeholder feedback, NOAA's Weather Program Office (WPO) will host a WIlliam M. Lapenta Intern during the summer 2023 to analyze previously collected customer experience, satisfaction, and engagement data to improve the ease of applying to WPO's funding competitions. The intern will also conduct additional stakeholder analyses to determine previous recipients of information about funding opportunities and, more importantly, identify new and diverse stakeholders to increase WPO’s efforts in marketing and outreach. The Weather Program Office (WPO) funds world class weather research with the ultimate goal of saving lives, protecting property, and enhancing the national economy. Each year, WPO hosts funding competitions encouraging the submission of research proposals from academic and private sector stakeholders, with an aim to conduct and transition weather research, improve knowledge, and develop products and services for the advancement of weather forecasting. WPO staff continually strive to streamline the proposal submission process for its funding competitions. However, without baseline information on how applicants currently interpret, understand, and view WPO’s funding opportunities and proposal submission processes, it is difficult to make informed decisions on how to refine these processes for future competitions. To obtain this critical stakeholder feedback, WPO will host a WIlliam M. Lapenta Intern during the summer 2023 to analyze previously collected customer experience and satisfaction data. This internship project will provide a unique opportunity for the selected student to gain an internal perspective on the grant funding process at a leading environmental agency, as well as sharpen and/or broaden their skills in statistical analysis, customer experience, and stakeholder outreach. This fall, WPO will distribute an Applicant Customer Experience and Satisfaction (ACES) Survey at the close of the current proposal submission window (November 2022) that will provide important usability data about WPO’s funding competition and application process. As a follow-up, WPO plans to engage applicants and stakeholders in Spring 2023, to better understand other aspects of the funding competition process not addressed by the ACES Survey. The selected Lapenta Intern will assist the office by analyzing the collected quantitative (i.e., survey data) and qualitative (i.e., interview and focus group data) customer satisfaction feedback. The findings from these analyses will help WPO improve its funding competition processes and the overall user experience for applicants submitting proposals to WPO competitions. The intern will also work alongside a diverse group of mentors across the office’s programs to conduct additional stakeholder analyses to determine previous recipients of information about funding opportunities and, more importantly, identify new and diverse stakeholders to increase WPO’s efforts in marketing and outreach. For an optimal internship experience, a basic understanding of statistical analysis is required. Some experience with social science data and qualitative/quantitative data analysis is strongly encouraged, but not required. This internship project is only the beginning of a much larger, strategic effort to increase WPO’s engagement and outreach with the broader Weather, Water, and Climate Enterprise. |
Office of Marine and Aviation Operations (OMAO)
| Project Title | Projects in NOAA's Office of Marine and Aviation Operations |
| Mentor name | TBD |
| Host office/program/lab | Office of Marine and Aviation Operations |
| Host/Lab location | TBD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate, Graduate |
| Preferred majors | TBD |
| Required Coding Skills | TBD |
| Other skills required | TBD |
| Project Description | It is anticipated that OMAO will host up to two interns. Project descriptions pending |
Additional projects added after October 1:
| Project Title | Collection, measurement, time series analysis of atmospheric trace gases using airborne platforms |
| Mentor name | Bianca Baier |
| Host office/program/lab | NOAA/OAR/Global Monitoring Lab |
| Host/Lab location | Boulder, CO |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Atmospheric science; chemistry; physics; computer science; or related field |
| Required Computer Skills | |
| Python | Basic |
| MatLab | Basic |
| Linux | Basic |
| Skills interns will learn during internship | Interns will learn how to conduct air sampling and/or measurements of atmospheric trace gases in the laboratory or field using multiple platforms; analyze air samples using spectroscopic or gas chromatography techniques; and/or how to calibrate instrumentation. |
| Project Description | NOAA's Global Monitoring Laboratory has been collecting data for more than 60 years for over a hundred different atmospheric gases that not only hold valuable information about the atmosphere itself but the ocean and land processes that are changing our atmosphere. Potential projects include measurement method or platform development; collection or measurement of atmospheric trace gases using airborne platforms in the laboratory or field setting; and/or time series analysis of data records. |
| Project Title | Objective Mapping of Biogeochemical Argo Data the Gulf of Mexico |
| Mentor name | Jennifer McWhorter |
| Host office/program/lab | OAR/Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | Miami, FL |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Oceanography, Computer Science, Environmental Science |
| Required Computer Skills | |
| Python | Interm |
| Skills interns will learn during internship | Working with Argo data, kriging, and geospatial mapping. |
| Project Description | Join a dynamic and insightful team of researchers in either lively Miami or classy San Diego. Successful applicants will combine data from state-of-the-art biogeochemical earth system models and robots to create an estimate of time varying oxygen, nitrate, chlorophyll, temperature, and salinity in the Gulf of Mexico. The mapping products will be useful for future fisheries and climate science questions. There may be travel opportunities to deploy ocean observing robots at sea. Students will gain programming skills and experience mapping geo-spatial data using kriging as well as lifelong friendships and happy memories. Please choose us! |
| Project Title | Using the fossil record preserved in marine sediments to understand climate change impacts on the Gulf of Mexico region |
| Mentor name | Emily Osborne |
| Host office/program/lab | OAR/Atlantic Oceanographic and Meteorological Laboratory |
| Host/Lab location | Miami, FL |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Ocean-related major, environmental science |
| Required Computer Skills | None |
| Project Description | Using a network of sediment cores collected across the Gulf of Mexico, marine microfossils will be used to reconstruct climate-scale records of ocean conditions to understand the progression of warming, ocean acidification, and deoxygenation. The student will be trained and responsible for processing sediment core samples, extracting microfossils, and conducting laboratory analysis on them, namely micro-CT scanning. |
| Project Title | Integrating social science data and weather data |
| Mentor name | Jonathon Mote |
| Host office/program/lab | OAR/Weather Program Office/Social Science Program |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | In person |
| Academic Level | Undergraduate or Graduate |
| Preferred majors | Social Science, Computer Science |
| Required Computer Skills | |
| Python | Basic |
| R | Basic |
| SAS | Basic |
| GIS | Basic |
| Other skills required | Interest or experience in data science. |
| Skills interns will learn during internship | Python |
| Project Description | A significant new endeavor of WPO is the Societal Data Insights Initiative (SDII), an effort to modernize the use of SBES data in NOAA by advancing the use of big data infrastructure and data science tools. As a data science intern, you will work with the SDII development team to apply your analytical and coding knowledge and skills on high-visibility, high-impact projects. You will explore the integration of new or existing SBES and weather data sources to create analyses and visualizations that turn disparate data points into tangible answers to help users and the public make more informed weather decisions, as well as better determine the impact of NOAA products and services. |
| Project Title | Establishing a cloud service that creates and disseminates tailored National Digital Forecast Database and National Blend of Models forecast verification reports |
| Mentor name | David Ruth, Dana Strom |
| Host office/program/lab | NWS/Modeling Development Lab |
| Host/Lab location | Silver Spring MD |
| In person/Virtual/No preference | Virtually |
| Academic Level | Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Atmospheric Science, Meteorology, Computer Science, Engineering, Mathematics, Environmental Science |
| Required Computer Skills | |
| Python | Basic |
| Linux | Basic |
| Other skills required | HTML/Javascript, BASH are helpful |
| Skills interns will learn during internship | The candidate will build a web application utilizing (HTML/JavaScript) and how to interact with and query terabytes of data within a PostgreSQL database. |
| Project Description | MDL's Digital Forecast Services Division (DFSD) would like a student to help establish a cloud service that creates and disseminates tailored National Digital Forecast Database and National Blend of Models forecast verification reports to registered users on a routine basis. |
| Project Title | Assist with transition of applications from legacy compute Farm to on-premise cloud including upgrades and scheduling (NWS) |
| Mentor name | Susanne Keveney |
| Host office/program/lab | Office of Dissemination |
| Host/Lab location | Silver Spring MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Graduate (MS or PhD) |
| Preferred majors | Computer Science |
| Required Computer Skills | |
| Python | Interm |
| Linux | Interm |
| GIS | Basic |
| Other skills required | none |
| Skills interns will learn during internship | Project Management |
| Project Description | The NWS has a legacy computer farm that will reach end of life in June 2024. Some of our critical applications for product and information dissemination still reside on this compute farm. The NWS is in the process of upgrading these applications to lift-and-shift them to the on premise cloud environment, and some to the public cloud if time permits. The recoding will be almost completed by mid-2023, but we will be at a critical point where the recoding is shifting to the onboarding and we will require additional support to ensure that is a smooth process and that all details are fully addressed. |
| Project Title | Choose your own adventure at NSSL: process-based studies of the pre- and near-storm environment and conditions that support it using recent field observations of the lowest levels of the atmosphere (OAR) |
| Mentor name | Elizabeth N. Smith |
| Host office/program/lab | National Severe Storms Lab |
| Host/Lab location | Norman, OK |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or graduate |
| Preferred majors | Meteorology, Atmospheric Science |
| Required Computer Skills | |
| Python | Basic |
| Other skills required | none |
| Skills interns will learn during internship | Intern will learn about research-grade observation platforms; as needed Intern may learn about mesoscale modeling |
| Project Description | Over the last few years, the National Severe Storms Laboratory has grown its near-surface observation capabilities. Across several field campaigns and projects, a plethora of data supports myriad science directions for researchers at all levels. Lapenta interns will undertake process-based studies focused on conditions related to, prior to, or in the vicinity of severe and/or high-impact weather. Under close guidance of the mentor team, interns will explore available data and recent science programs to select a subset of observed data on which to focus their research; several potential focus areas have already been identified by the mentor team. The project will be conducted under group mentorship with regular meetings and open access to the mentoring team. The mentoring team aims to meet interns where they are in terms of technical skills and help interns develop their skills along the way. Outcomes are expected to include: 1) interns learning about the modern observation methods and platforms operated at the National Severe Storms Laboratory, 2) development of unique research questions and/or goals appropriate for the Lapenta project time period, 3) contribution of techniques or tools and/or case study or science outcomes based on the datasets provided. |
| Project Title | Introducing AI into MDL's operational guidance suite of products |
| Mentor name | Mamoudou Ba |
| Host office/program/lab | NWS/Modeling Development Lab |
| Host/Lab location | Silver Spring, MD |
| In person/Virtual/No preference | No preference |
| Academic Level | Undergraduate or graduate |
| Preferred majors | Computer Science, Mathematics, Atmospheric Science/Meteorology |
| Required Computer Skills | |
| Python | Interm |
| UNIX | Basic |
| Linux | Basic |
| Other skills required | AI/ML experience desired but not required |
| Skills interns will learn during internship | Experience developing AI/ML techniques, utilizing Cloud Computing and supercomputers |
| Project Description | MDL has decades of experience in developing calibrated, statistically post-processed guidance for critical weather such as thunderstorms. This internship will facilitate acceleration of prototype development of AI-informed statistical post-processed guidance that supports NWS users and stakeholders utilizing a Convolutional Neural Network (CNN) for thunderstorm prediction using High Resolution Rapid Refresh (HRRR) model output and Multi-Radar Multi-Sensor (MRMS) radar observations. |
| Project Title | Probabilistic forecasting for Aviation Weather |
| Mentor name | Ryan Connelly |
| Host office/program/lab | Aviation Weather Center |
| Host/Lab location | Kansas City, MO |
| In person/Virtual/No preference | In person |
| Academic Level | Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application) |
| Preferred majors | Meteorology, Atmospheric Science |
| Required Computer Skills | |
| Python | Basic |
| Linux | Basic |
| Other skills required | none |
| Skills interns will learn during internship | Aviation meteorology (turbulence, icing), fundamentals of producing intuitive figures for public consumption |
| Project Description | The National Weather Service has embraced probabilistic forecast messaging for many different weather phenomena. The aviation sector has sometimes been considered the last bastion of deterministic forecasting. Over the next few years, the Aviation Weather Center will embrace probabilistic forecasting as part of an overall re-emphasis on collaborative aviation forecasting, known as CAF. For this project, the student will develop a prototype forecast product assessing the probability of turbulence and/or aircraft icing during the Day 2 forecast period. A basic familiarity with Python is required. A basic familiarity with aviation weather is preferred, but not required. |