2024 Projects - National Oceanic and Atmospheric Administration (NOAA)

National Oceanic and Atmospheric Administration (NOAA)

The William M. Lapenta NOAA Student Internship Program

APPLICANTS: Please refer to the list below when selecting your preferred internship sites. Please note that some projects may not choose to select an intern next summer. Be sure to scroll all the way to the bottom. Projects are occasionally added to the list after the application has opened and may be out of order by Line Office.

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	Advancing rip current/wave forecasting and Beach Hazard Statements
Mentor	Audra Luscher
Line office	NOS
Host office/program/lab	CO-OPS Coastal Hazards Branch
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Oceanography/Physical Sciences, Risk Communication/Social Sciences
Coding skills required/level:
Python	Basic
Matlab	Basic
R	Basic
GIS	Basic
Willing to train intern in:	Yes, Python is primary language but usually work with MatLab and R
Project Description:	In honor of Bill Lapenta, this project focuses on advancing activities around rip current/wave forecasting and Beach Hazard Statements through programmatic coordination and organization between NOS-NWS. We are requesting two of the five NOS designate Lapenta Fellows. One position will be more focused on the technical aspects of wave and rip forecasting and the other position will focus on assessment of stakeholder needs and gathering feedback on beach hazard products and services through social science tools. The fellows will help to advance long standing requirements related to the following: 1)Support the development of sustained programmatic rip current and beach hazards collaboration between NOS and NWS. 2)Assess the communication aspects of the NOAA rip current model and resulting rip current forecasts and how these could be improved to better leverage the capabilities of the model, 3) Support development of surveys and focus groups to assess internal and external rip current and beach hazard communication and outreach, 4) Work with key partners across NWS, NOS, academia, nonprofits and lifesaving associations to identify potential improvements to rip current and beach hazard products and communication. The interns will be mentored by CO-OPS Chief Scientist and will be supervised by the Oceanography Division Coastal Hazards Branch Chief. High levels of engage will occur with other Meteorologists and Oceanographic Scientists experience in HAB and High Tide Flood Prediction and Outlooks.

Project Title	Developing NOS 3D operational forecast systems on NOS cloud sandbox
Mentor	Carolyn Lindley
Line office	NOS
Host office/program/lab	NOS/CO-OPS
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	physical oceanography
Coding skills required/level:
Python	Interm
Matlab	Basic
C++	Interm
Met Plus	Basic
SAS	Interm
Fortran	Interm
UNIX	Interm
Linux	Interm
Other skills required	numerical hydrodynamic coastal ocean modeling (FVCOM preferred but ROMS, ADCIRC, SCHISM acceptable)
Willing to train intern in:	Intern will need familiarity with python, shell scripting. Opportunity to apply and enhance their skills.
Other skills interns may learn:	Increased familiarity with hydrodynamic modeling.
Project Description:	Intern will work with one of the NOS 3D operational forecast systems, setting up an instance of the system in the NOS cloud sandbox or on NOAA HPC, retrieve forcing conditions and run short simulations. There may also be an opportunity to join a field team to deploy an ADCP and/or CTD to collect observations for model validation. Based on results of simulations, intern will develop a recommendation for updating the system and may have an opportunity to present findings directly to NOAA stakeholders.

National Marine Fisheries Service (NMFS)

Project Title	Assessing and improving the integration of earth system models and datasets for ecosystem-based fisheries management
Mentor	ana.vaz@noaa.gov
Line office	NMFS
Host office/program/lab	Southeast Fisheries Science Center. 75 Virginia Beach Drive. Miami FL 33149.
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Oceanography, Earth Sciences, Atmospheric Sciences, Marine Biology
Coding skills required/level:
Matlab	Interm
R	Interm
Linux	Interm
Other skills required	Experience with git, github repositories and Quarto could be helpful, but not required.
Willing to train intern in:	Yes, Matlab and R, unix, git, github repositories, Quarto.
Other skills interns may learn:	Interns will become familiar with ecosystem-based fisheries management (EBFM) applications in the Southeastern US, hydrodynamic model products, satellite data, and the NOAA-wide Climate, Ecosystem, and Fisheries Initiative (CEFI). Interns will have multiple opportunities to network, and to share their work with peers. Intern will learn to access and manipulate large data files (from earth-system models, satellite data), and gain basic data analysis skills.
Project Description:	Recent technological and computational advances have increased the capabilities to collect, model, and validate unprecedented amounts of atmospheric, climate, hydrodynamic, remote sensing, and other environmental data. Large observational datasets and a range of sophisticated and validated oceanographic model outputs are now available for the Gulf of Mexico, South Atlantic, and Caribbean Sea. The recent development of these data and models offer unprecedented opportunities to support ecosystem-based fisheries management (EBFM). However, key barriers and complexities hinder their use and operation. Currently, critical communication and understanding gaps exist between earth system, ecological, and stock assessment modelers. For example, our own experience has found that accessing earth system model datasets is complicated by server shifts, lengthy downloads, large local storage needs, lack of metadata, and quality checks for multiple products. These challenges will become important and need a structured way to be integrated into SEFSC’s operation in the coming years as the NOAA-wide Climate, Ecosystem, and Fisheries Initiative (CEFI) becomes operational. We propose to review available models and datasets for their utility for EBFM in the Southeastern US. Our overarching goal is to answer “What environmental models and datasets are appropriate to answer which EBFM questions?” Our work will further NOAA Fisheries' priorities towards adaptive and ecosystem-based fisheries management, supporting the Southeast Regional Action Plan efforts. This will be a multidisciplinary effort to further collaborations between SEFSC and AOML and provide a critical first step for identifying specific Center data needs regarding environmental models and satellite data towards developing best-practices for incorporating environmental data in EBFM modeling. Specific deliverables include the following: 1) A compilation of datasets available in the Gulf, South Atlantic, and US Caribbean; 2) Preliminary analysis of hydrodynamic models skill for our study region, 3) Intern will also support the organization and participate in a series of online SEFSC-wide oceanographic data workshops, with the participation of relevant collaborators and data experts from other NOAA line offices (e.g., AOML, NCEI, NESDIS). Finally, 4) findings from 1 & 2 will be disseminated via presentations. Additionally, the intern can contribute to a NOAA technical memorandum and a github repository which will summarize the literature review and our workshops recommendations.

Project Title	Groundfish Assessment Program Bottom Trawl Survey Progress and Temperature R Shiny application.
Mentor	emily.markowitz@noaa.gov
Line office	NMFS
Host office/program/lab	Alaska Fisheries Science Center, Groundfish Assessment Program, 7600 Sand Point Way NE, Seattle, WA 98115.
Location	This position can be remote. The intern is also invited, should timing work, to join us on survey in the Bering Sea or Aleutian Islands, which must be in person.
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Intern can be undergraduate or graduate, with experience in R. Many majors provide this background, though this intern would most likely have majored in Marine Science, Biology, or Computer Science.
Coding skills required/level:
R	Interm
GIS	Basic
Other skills required	Good communication/writing skills, collaboration in GitHub, basic SQL/Oracle data base management, and a sense of how to design a user interface. We are also prepared to teach the intern these skills.
Willing to train intern in:	Yes, R, GitHub, SQL, and Oracle data management.
Other skills interns may learn:	The intern will also learn communication skills. If the intern is able to join us on survey, they will also learn how to identify, sex, and collect specimens; how to collect physical oceanographic data; and gain field work experience.
Project Description:	The Groundfish Assessment Program conducts bottom trawl surveys in the Bering Sea, Gulf of Alaska, and Aleutian Islands to collect data on the ranges and populations of fish, crab, and other bottom-dwelling species. Communicating the survey’s progress and temperature data collected by the survey in near-real time are critical for establishing trust, transparency, and understanding between scientists and communities in the region and around the world. These data are currently shared through daily image updates on NOAA webpages and through social media. Communication of these data could be greatly improved by sharing this information through a dynamic R Shiny app. There, users could watch the survey’s progress and explore ranges and changes in temperature, while applying their own graphing aesthetics for multiple regions and years in one interactive portal. The data going into a tool like this could be updated without any user/developer intervention and as soon as data are available. AFSC staff have begun development of the code for this app, but the assistance of a skilled intern could really help us finish the project.

Project Title	Behavioral responses of sperm whales to noise
Mentor	heloise.frouin-mouy@noaa.gov
Line office	NMFS
Host office/program/lab	NOAA Southeast Fisheries Science Center 75 Virginia Beach Drive Miami, FL 33149
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Biology ; Computer Science
Coding skills required/level:
Matlab	Basic
Other skills interns may learn:	Acoustic analysis, AIS
Project Description:	Marine mammals, especially cetaceans, are highly vocal and rely on sound for almost all aspects of their lives. Thus, they are likely sensitive to anthropogenic noise. As part of the LISTEN GoMex (Long-term Investigations into Soundscapes, Trends, Ecosystems, and Noise in the Gulf of Mexico) project, acoustic recordings of marine mammals and ambient noise were collected in the western Gulf of Mexico from July 2022 to 2023. The Southeast Fisheries Science Center and research partners are interested in studying sperm whale acoustic and dive behavior, as well as behavioral responses to anthropogenic activity, including shipping and seismic airgun survey noise. Working with Science Center researchers and collaborators, the intern will help construct diving tracks of individual sperm whales using passive acoustic localization techniques. The project will also combine anthropogenic noise analyses with remotely-sensed AIS ship tracks to evaluate how sperm whales respond to vessel presence and seismic activity.

National Environmental Satellite and Data Information Service (NESDIS)

Project Title	Exploring Software Defined Radio (SDR) for Satellite Remote Sensing Applications
Mentor	Changyong Cao
Line office	NESDIS
Host office/program/lab	NOAA/NESDIS/STAR
Location	NCWCP, College Park, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Electrical engineering, computer science, astronomy, physics
Coding skills required/level:
Python	Interm
Linux	Interm
Other skills required	Signal processing skill is a plus although not required.
Willing to train intern in:	Yes. Python.
Other skills interns may learn:	Radiometry, remote sensing.
Project Description:	The relatively new technology of Software Defined Radio (SDR) has led to fundamental changes in the RF (Radio Frequency) world from telecommunications to radio astronomy, satellite downlink, hyperspectral microwave remote sensing of the earth, and more. This Lapenta project will provide the opportunity to the intern to learn SDR with hands on experiences for receiving and analyzing RF signals from radio stations, aircrafts, and satellites, getting started in designing basic systems with GNURadio. In addition, the intern will study the use of SDR in modern satellite microwave remote sensing system for Earth observations, as well as its use in radio frequency interference (RFI) mitigations, from recent and planned future NASA and NOAA satellite missions.

Project Title	Calibration, Validation and Application of the Next Generation of Satellite Observing Systems
Mentor	FLAVIO ITURBIDE-SANCHEZ
Line office	NESDIS
Host office/program/lab	NESDIS/STAR
Location	NCWCP
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Physics, Electrical Engineering, Atmospheric and Ocean Science, Earth Science, Data Science or Similar
Coding skills required/level:
Python	Interm
Matlab	Interm
C++	Basic
R	Basic
Fortran	Basic
Linux	Interm
Other skills required	GitHub or similar, Oral, Written and Interpersonal communication skills
Willing to train intern in:	No
Other skills interns may learn:	Satellite remote sensing, from calibration to applications
Project Description:	This project has the purpose to explore methods and technologies to perform exploitation and value assessment of data and technology in support of the Next Generation of Earth Observing Systems. The intern will be exposed to the NOAA's operational environment, working with experts in different areas including, Calibration/Validation, Remote Sensing, Data Assimilation as well as AI/ML. The intern will have the opportunity to participate on on-going projects or support new research projects. Main responsibilities include reporting and presenting weekly updates on the assigned research project. The intern will also have the opportunity to report final results at the end of the Internship via an oral presentation. Technical support will be provided by the mentor and group of scientists working at NESDIS in order to ensure the success of the project activities performed by the intern. Projects include, but not limited, exploring new calibration and validation methodologies for satellite data, supporting the value assessment of infrared and microwave sensors based on emerging technologies and concepts, evaluate the impact and quality of Small Satellite data. The intern will work with the mentor on the selection of the project according to the technical skills and interest of the intern. Open communication with the mentor and supporting staff will ensure the satisfactory completion of project and help to comply with the objectives of the Internship Program.

Project Title	SmallSat Coordination Analysis
Mentor	Franz Zichy
Line office	NESDIS
Host office/program/lab	ACIO-S
Location	Silver Spring
I prefer to host an intern	In person
Local transportation:
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Electronic Engineering,
Coding skills required/level:
Python	Basic
Matlab	Basic
Other skills required	Radio Frequency skills
Willing to train intern in:	MetLab or Python
Other skills interns may learn:	RF Spectrum Management
Project Description:	Objectives are to introduce the intern to concepts of spectrum management/engineering, with a focus on radio spectrum characteristics; satellite coordination; using software to model radio frequency emitting/receiving stations, budget links, antennas, signal levels, and noise levels in a 3D environment.

Project Title	Investigating microplastic ocean/coastal transport connections and potential impacts using NCEI data
Mentor	Jennifer Webster
Line office	NESDIS
Host office/program/lab	NCEI/COGS
Location	Stennis Space Center
I prefer to host an intern	Virtually
Local transportation:	When visiting Stennis the intern would need a car. There is no public transportation.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Physics, marine, or environmental science
Coding skills required/level:
Python	Interm
R	Interm
SAS	Basic
UNIX	Basic
Linux	Basic
GIS	Interm
Other skills required	Introductory knowledge of marine science or oceanography
Willing to train intern in:	Yes. GIS and/or Python or R
Other skills interns may learn:	Scientific data writing/reporting and presentation giving
Project Description:	Understanding the location of microplastics in the marine environment is important to guide researchers and managers in the understanding of the potential impacts of the marine pollutant. Microplastics can impact water quality which can cause harm to fishes and other marine life. Using data in the NCEI microplastic database and associated environmental data, this project will investigate ocean and/or coastal transport connections of microplastics to understand the possible locations of hot spots and what marine environments and habitats would be affected. The project will entail (1) learning about the suite of data at NOAA National Centers for Environmental Information (NCEI) that aid the study, forecast, and tracking of microplastics, (2) obtain, process, and visualize some of these data in a case study of the upper ocean/coastal transport of microplastics and (3) possibly develop a GIS-based environmental sensitivity map to advise resource managers of possible changes to microplastic concentrations in specific coastal marine environments.

Project Title	Information Management to Support AI-Ready Data
Mentor	Jessica Morgan
Line office	NESDIS
Host office/program/lab	ACIO-S Enterprise Data Program
Location	Silver Spring, MD
I prefer to host an intern	Virtually
Local transportation:
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	no preference
Coding skills required/level:	None
Other skills required	attention to detail
Willing to train intern in:	coding language(s) [TBD] relevant to AI-ready data analysis and/or knowledge graphs
Other skills interns may learn:	AI-ready data analysis, knowledge graphs
Project Description:	This project will conduct an assessment of the current state of NESDIS information to support next-generation analysis by AI-driven tools. Working with NOAA data professionals, the intern will 1) perform a survey of existing sources of NESDIS data, information, and products, 2) assess readiness for utilization by generative AI applications, and 3) apply knowledge graph principles to enable increased information usage. The project will be led by the NESDIS Data Management Architect (DMA), together with the NESDIS Assistant Chief Data Officer (ACDO), NESDIS Chief Technology Officer (CTO), and Data Scientists from across the NESDIS enterprise.

Project Title	Development of tool to assess the benefits and cost-effectiveness of the Earth-observing system portfolio
Mentor	Lin Lin
Line office	NESDIS
Host office/program/lab	SAE
Location
I prefer to host an intern	Virtually
Local transportation:
Academic Level	Graduate (MS or PhD)
Preferred Majors	Earth Science or Economics
Coding skills required/level:	None
Other skills required	No specialized skills required, but should be proficient in typical library research skills and office document skills.
Willing to train intern in:	Yes, depending on interest of intern. R language.
Other skills interns may learn:	Intern will become familiar with NOAA observing systems and applications.
Project Description:	The value of weather, climate, and environmental data for numerous socio-economic (SE) sectors has been studied extensively by NESDIS, OAR, the NOAA line offices and others. The ability to assess quantitatively the SE benefits and cost-effectiveness of the Earth observing system portfolio and any proposed changes thereto would provide precise data supporting NOAA requests for resources to maintain and enhance NOAA’s Earth observing systems. It is clear that a large part of the economy is affected by weather and climate. However, it is difficult to establish quantitative benefits: double blind studies are generally not possible, and it is very difficult for survey respondents to assess the impact on their decision making of these data, which are often one of many information streams supporting a decision. The goal of this project is to aid in the development of a proof-of-concept tool to assess quantitatively the SE benefits and cost-effectiveness of the Earth-observing system portfolio. Practically the intern will search the existing literature for studies which have quantified the impact of Earth system data on decision making and subsequent outcomes and then analyze those studies. The mentors will direct the intern and provide detailed regular feedback on their progress. The outcome will be a data base of the relevant studies including when possible quantitative estimates of the sensitivity of SE metrics to the quality of the Earth system data used.

Project Title	GOES-derived lake-effect snow QPE product demonstration for the Great Lakes Region
Mentor	Mark Kulie
Line office	NESDIS
Host office/program/lab	STAR
Location	Madison, WI
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science/Meteorology
Coding skills required/level:
Python	Basic
Matlab	Basic
UNIX	Basic
Linux	Basic
Other skills required	Basic unix/linux computing and programming skills are the only requirements. Programming language is flexible.
Willing to train intern in:	Yes. Python, Matlab, and/or IDL code will be used. Sample code will be available that can be modified or improved by the intern.
Other skills interns may learn:	Interpreting VIS/IR and microwave satellite observations of lake-effect snow, interpreting NEXRAD observations of lake-effect snow, developing quantitative precipitation estimation (QPE) algorithms, satellite nowcasting applications
Project Description:	This project encompasses multiple aspects of a GOES lake-effect snow quantitative precipitation estimation (QPE) product that is currently under development. This product uses NEXRAD-derived snowfall intensity estimates matched with co-located GOES observations and products within a reasonable distance from Great Lakes NEXRAD sites to train a satellite-only QPE algorithm. The main objectives of this project are to introduce the Lapenta Intern to GOES observations and derived products used for monitoring lake-effect snow events. The primary project outcome will be to demonstrate how the experimental lake-effect snow QPE product improves situational awareness for National Weather Service (NWS) Weather Forecast Offices (WFO) in the Great Lakes Region that lack suitable NEXRAD observational coverage during shallow lake-effect snow events. Specific intern duties will include analyzing multiple historic lake-effect case studies to illustrate and quantify the impact of satellite- versus NEXRAD-derived QPE in known regions that typically lack quality NEXRAD observations of lake-effect snow, interface with NWS WFO personnel to understand how this experimental product can be optimally disseminated to the operational community, and (time permitting) explore ways to improve the GOES lake-effect snow QPE product by analyzing various GOES and ancillary products that are not employed in the current algorithm. The project mentor (Dr. Mark Kulie, NOAA/NESDIS/Center for Satellite Applications and Research) will provide the following longitudinal project guidance: exhaustive background descriptions of GOES observational capabilities and operational uses, convey scientific knowledge of lake-effect snow and associated nowcasting issues associated with this type of hazardous winter weather, foster relationships between the intern and Great Lakes NWS WFO’s, and programming assistance to read and analyze the relevant project datasets.

Project Title	GOES-R Cloud-based Mission Visualization Internship
Mentor	Maurice McHugh
Line office	NESDIS
Host office/program/lab	GOES-R
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
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
Coding skills required/level:
Python	Interm
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.
Willing to train intern in:	Yes, python and/or javascript
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.

Project Title	Demonstrating capabilities of the NOAA Unified Cloud Archive
Mentor	Ryan Berkheimer
Line office	NESDIS
Host office/program/lab	NCEI
Location	Asheville, NC
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Physics, Data Science, Computer Science, Electrical Engineering, Environmental Science
Coding skills required/level:
Python	Interm
GIS	Interm
Other skills required	Communications; research and data exploration; spatial software preferred
Willing to train intern in:	Some advanced training will be provided, but interns must possess at least intermediate skill
Other skills interns may learn:	Knowledge graphs, ontology modeling, information modeling, data engineering, data fusion, data modeling, archiving, data stewardship, data fusion
Project Description:	This project will help demonstrate capabilities of the NOAA Unified Cloud Archive Workflow System, a core service of the NESDIS Common Cloud Framework. Interns will work with our science and stewardship teams to develop reusable models, capabilities, and workflows that move the new holistic NOAA knowledge capability forward. Project topics are flexible and dependent on specific intern interest, but some topics might involve: developing information models and data pipeline for archiving models, services, and APIs; developing enterprise services like OGC EDR API and GDAL libraries; developing workflow and access tools that demonstrate data fusion of physical science and social science data; developing semantic search models that provide product recommendations or make inferences.

Project Title	JPSS data access, analysis and/or economic benefit analysis
Mentor	Satya Kalluri
Line office	NESDIS
Host office/program/lab	LEO/JPSS
Location	Greenbelt, MD
I prefer to host an intern	Virtually
Local transportation:
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Remote sensing, Earth science, economics, computer science, data science, informatics, environmental engineering, civil engineering
Coding skills required/level:
Python	Interm
GIS	Basic
Other skills required	Special skills/training required: 1. Analyzing data and preparing interpretive reports. 2. Collecting, recording, and analyzing data/information. 3. Preparing reports and briefings. 4. Classes in economics, statistics and other analytic classes will be useful but are not required. 5. Writing and communications skills. 6. Proficiency with PowerPoint and Word are required. 7. Students should have basic knowledge of remote sensing and geographic information systems.8.The intern should have economic modeling/valuation experience, or basic familiarity with Python code in Jupyter Notebook and cloud data analysis. For data analysis, experience with Python packages such as netCDF4, NumPy, Matplotlib, Cartopy, MetPy, and SharpPy is helpful but not required.
Willing to train intern in:	No.
Other skills interns may learn:	The intern will learn how to: Access/download JPSS data from online data archives Open data files, understand the file structure, and read the file contents Process satellite data and apply quality/confidence flags Visualize satellite data, including use of map projections Economics intern will learn to provide cost-benefit-valuation expertise in value of low earth orbit data as its applied in a decision context across one sector.
Project Description:	The intern will assist with JPSS data access, analysis and/or economic benefit analysis and possibly develop a tool, framework, model, or database that can help collect this information from year to year. The intern should have economic modeling/valuation experience, or basic familiarity with Python code in Jupyter Notebook and cloud data analysis. For data analysis, experience with Python packages such as netCDF4, NumPy, Matplotlib, Cartopy, MetPy, and SharpPy is helpful but not required. The intern will complete training in user engagement and support the review or development of Beginner’s Guide to JPSS Data. Expected outcomes: The student will learn how to access/download NOAA satellite netCDF4 files from online data archives. The student will learn how to open netCDF4 data files, understand the file structure, and read the file contents. The student will learn how to process satellite data and apply quality/confidence flags. The student will learn how to visualize satellite data, including use of map projections. The student will work with mentors at NOAA and NASA. The student will learn how to make a scientific poster. The student will learn data management and strengthen their statistical, writing and communication skills. Economics students will learn how to apply uses and applications of data in a decision-context and apply economic modeling principles to develop cost-benefit studies. Guidance and supervision: The project will be supervised by Satya Kalluri, Office of Low Earth Orbit Observations Program Scientist. They will be co-mentored by Bill Sjoberg (NOAA/JPSS) and Jenny Dissen (NOAA CISESS NC).

Project Title	Use of lunar radiance to monitor the calibration of ABI (a satellite instrument) solar channels
Mentor	Xiangqian Wu
Line office	NESDIS
Host office/program/lab	STAR
Location	NCWCP
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Computer science; Astronomy.
Coding skills required/level:
Python	Expert
C++	Interm
Fortran	Interm
UNIX	Basic
Linux	Basic
Other skills required	Neural network; Pattern recognition.
Willing to train intern in:	IDL
Other skills interns may learn:	Satellite instrument calibration.
Project Description:	Vicarious calibration or monitoring the calibration of reflected solar channels on satellite instruments using the Moon has been reported in the past two decades. All these applications used measured and modeled lunar irradiances, both may have inherited errors. This project seeks an innovative way of using lunar radiance for satellite instrument calibration. A critical step of the project is to identify a common area in various lunar images regardless of phase angle and libration. The intern will help us to verify and improve this step using convolutional neural network (CNN) and other artificial intelligence (AI) tools. We have made progress in this step, so existing expertise can guide the intern throughout the internship. The intern can also learn how the specific knowledge can help with satellite instrument calibration, and what else can be done in NESDIS/STAR.

Project Title	Validation of hog-resolution vegetation data
Mentor	Yunyue Yu
Line office	NESDIS
Host office/program/lab	NESDIS/STAR
Location	NCWCP at College Park
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Geography
Coding skills required/level:
Python	Interm
C++	Basic
UNIX	Interm
Linux	Interm
GIS	Basic
Other skills required	interm data processing skill; data display (visualization)
Willing to train intern in:	Yes, training on Python, UNIX/Linux and some image display software are available
Other skills interns may learn:	Intern will learn about data sets commonly used for validation of satellite vegetation data
Project Description:	Intern duties and resposibilities: Generate examples of high resolution vegetation data, especially time series. Compare to ground-based data from such as PhenoCam and Ameriflux. Guidance and supervision of intern: Intern will meet with team members at least once a week to discuss project progress. At the beginning of the project, the intern will be shown how to generate high-resolution vegetation data and how to acquire ground-based data for comparison.

National Weather Service (NWS)

Project Title	Statistical comparison of probabilistic information on forecast operations
Mentor	Chris Brenchley
Line office	NWS
Host office/program/lab	Central Pacific Hurricane Center
Location	Honolulu, HI
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology
Coding skills required/level:
Python	Interm
Matlab	Basic
C++	Basic
Met Plus	Basic
R	Basic
SAS	Basic
Fortran	Basic
UNIX	Basic
Linux	Interm
GIS	Basic
Project Description:	The project is flexible and would revolve around comparing probabilistic model data output to discover the most valuable and accurate models and investigate techniques to applying the data to operational forecasting. Additional application could entail a survey on best practices to communicating probabilistic forecast data to the local user.

Project Title	Improving NOAA/NWS Storm Prediction Center (SPC) Severe Weather Forecast and Verification Techniques
Mentor	Chris Karstens
Line office	NWS
Host office/program/lab	NOAA/NWS Storm Prediction Center
Location	Norman, OK
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology/Atmospheric Science
Coding skills required/level:
Python	Interm
UNIX	Basic
Linux	Basic
Other skills required	None
Willing to train intern in:	Occasional advice/help with any coding needs will be provided for any required language
Other skills interns may learn:	Shadowing operational forecasters at SPC
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	Support UNESCO/IOC Tsunami Ready Programme through the implementation of tsunami assessment, preparedness and response indicators
Mentor	Christa von Hillebrandt-Andrade
Line office	NWS
Host office/program/lab	Caribbean Office of the International Tsunami Information Center
Location	Mayaguez, Puerto Rico
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Geophysics, Geology, Oceanography, Geography, Social Science
Coding skills required/level:
GIS	Basic
Willing to train intern in:	No
Project Description:	Tsunamis are no-notice, fast onset natural hazards that can cause catastrophic impact. It is impossible to know when or where the next tsunami will hit, but we do know that community preparedness will save lives. If people do not evacuate in time, thousands of lives will be lost and massive economic loss incurred that will have long lasting negative humanitarian, social and economic effects. The Caribbean and Adjacent region has experienced over 80 tsunamis with more than 4500 deaths over the past 500 years. Through the Tsunami Ready project funded by USAID and executed by the Caribbean Office of the International Tsunami Information Center, the selected communities receive training and subject matter expertise, tools, services and materials to facilitate the implementation and sustainability of the programme. Countries and their coastal communities will be better prepared for the next tsunami through the implementation of the UNESCO/IOC Tsunami Ready Programme and the application of its 12 indicators for Tsunami assessment, preparedness and response. Tsunami Ready helps to save life and livelihoods from tsunamis and other coastal hazards through better planning, education and awareness. The countries which hav been tentatively selected to participate in the programme are Anguilla, Antigua and Barbuda and Honduras. The Intern will become familiar with the programme and support its implementation. Responsibilites of the intern may include using GIS to elaborate or edit evacuation maps, prepare and help with the distribution of tsunami information materials, edit and help with the procurement of signage, support the conduct of tsunami exercises. and review and help with the elaboration of local and if required, national tsunami procedures and SOPs. The intern will receive direct guidance from ITIC-CAR manager and work with the team of other students and employees.

Project Title	Accelerating the skill improvements of NWS FIM capabilities
Mentor	Edward Clark
Line office	NWS
Host office/program/lab	The National Water Center (NWC)
Location	NWC/Tuscaloosa, AL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Civil Engineering, Hydrology, Water Resources, Geospatial Information Systems
Coding skills required/level:
Python	Interm
C++	Basic
R	Basic
Fortran	Basic
Linux	Basic
GIS	Expert
Other skills interns may learn:	Project Management, Operational Hydrologic forecasting, policy development and interagency collaboration.
Project Description:	The National Weather Service (NWS) is expanding its Flood Inundation Mapping (FIM) capabilities beginning in September 2023 with 10% of the U.S. population on the way to near 100% coverage by 2026. This project will focus on accelerating the skill improvements of NWS FIM capabilities and develop tools to quickly assess the impact to transportation infrastructure such as roads and bridges before, during and after flood events. The National Weather Service (NWS) is implementing in near real-time forecast Flood Inundation Mapping (FIM) services depicting flood impacts for neighborhoods and on civil engineering infrastructure before, during and after flood events. A baseline capability has been established nationally leveraging the Height Above Nearest Drainage (HAND) terrain index however model improvements are needed to maximize the skill of these mapping capabilities to better facilitate the provisioning of Impact-Based Decision Support Services (IDSS). Multi-model mapping solutions and the integration of LiDAR derived road networks have been prototyped and demonstrated and the goal of this project will be to accelerate the integration of these capabilities into a suite of robust services for communities nationwide. Beginning in September 2023 with 10% of the U.S. population, the NWS will publicly release forecast FIM services and will expand this coverage to near 100% of the U.S. population by 2026. As capabilities are expanded, services will be transitioned from an experimental capacity to an operational capacity and this project will aid in the acceleration of this transition through the skill improvements delivered. The intern supporting this project will learn about the NWS' baseline FIM capabilities and how these capabilities and supporting tools are leveraged in operations not only at the National Water Center but also across River Forecast Centers (RFCs) and Weather Forecast Offices (WFOs). With this operational context in place, the intern will work with Office of Water Prediction geospatial developers and hydrologists alike to develop specific model skill improvements to better assess the impact of forecast FIMs. The intern will be responsible for putting forth hypotheses and an experimentation plan to guide the development effort. The intern will provide brief daily updates to colleagues to collect feedback. The intern will also participate in monthly demonstrations. The intern will be supported by the Office of Water Prediction (OWP) Geo-Intelligence Division Director as well as OWP project leads focused on implementing FIM skill improvements. The intern will also have access to OWP's chief scientist as needed to guide development efforts. The OWP Division Director will meet with the intern at minimum once every three weeks to complement the daily interactions with OWP project leads and colleagues.

Project Title	Improving NOAA Climate Prediction Center (CPC) Flash Drought Forecasts
Mentor	Hailan Wang
Line office	NWS
Host office/program/lab	NOAA/NWS Climate Prediction Center (CPC)
Location	College Park, MD
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Meteorology, hydrology
Coding skills required/level:
Python	Interm
Matlab	Basic
C++	Basic
Met Plus	Basic
R	Basic
SAS	Basic
Fortran	Interm
UNIX	Interm
Linux	Interm
GIS	Basic
Other skills required	Coding experience analyzing subseasonal-to-seasonal (S2S) dynamical model forecasts understanding of flash drought concept, flash drought indices, and post-processing techniques, as well as coding experience in analyzing subseasonal-to-seasonal (S2S) dynamical model forecasts.
Willing to train intern in:	Fortran
Project Description:	Flash droughts are characterized by unusually rapid intensification of drought conditions over a short period, with significant cascading environmental and socioeconomic impacts. Skillful and reliable prediction of flash drought is essential for preparing for and mitigating drought related impacts and costs. A key effort at the NOAA Climate Prediction Center (CPC) for the coming years is to develop the CPC probabilistic flash drought outlook using hybrid dynamical-statistical forecasts. The intern will contribute to this effort by producing and evaluating reforecasts and real-time forecasts for a select flash drought index, where statistical post-processing will be applied to subseasonal-to-seasonal (S2S) dynamical forecasts to improve the skill and reliability of flash drought forecasts. The intern will also collaborate with CPC drought folks on incorporating the findings in the development and production of CPC probabilistic flash drought outlooks. The intern will meet with the mentor on a weekly basis to discuss project progress and next steps, and when needed, communicate via email in between the weekly meetings. The intern will also attend some of CPC’s internal bi-weekly drought group meetings when relevant. Additionally, the intern is encouraged to interact with CPC drought folks, particularly drought forecasters, to better understand NOAA operational drought outlooks and how the intern’s work may contribute to improving these products. At the end of the internship, the intern is expected to present the work at CPC’s monthly Drought Work Group meeting.

Project Title	Analysis of NWS atmospheric model planetary boundary layer processes
Mentor	Jeffery McQueen
Line office	NWS
Host office/program/lab	NCEP/EMC
Location	NCWCP, College Park, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science, Computer Science
Coding skills required/level:
Python	Interm
Met Plus	Basic
Fortran	Basic
UNIX	Interm
Linux	Interm
GIS	Basic
Other skills required	planetary boundary layer, air quality class
Willing to train intern in:	fortran and unix/linux
Other skills interns may learn:	analysis/use of NetCDF data/ use of Monet or METPlus verification system.
Project Description:	The goal of this project is to evaluate the operational and experimental NOAA air quality model and NWP meteorological and atmospheric chemistry predictions over the U.S. for summer and winter AQ episodes . The EPA Photochemistry Assessment Monitoring System (PAMS) provides detailed speciated chemistry measurements that will be used to analyze model performance for periods when wildland fires were present versus more quiescent periods where air quality was primarily driven by human anthropogenic emissions. The Unified Ceiometer Network (UCN) Programs will be written or leveraged to extract the corresponding 24 hr operational and experimental model predictions at the PAMS sites and time series plots will be generated for the two periods during the summer. Outputs available: Graphical overlay maps will also be generated for fire weather cases and winter high PM events.

Project Title	Using Artificial Intelligence to improve statistically post-processed NWS guidance
Mentor	Judy Ghirardelli
Line office	NWS
Host office/program/lab	OSTI/MDL/DSD
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Any of: Meteorology, Atmospheric Science, Computer Science
Coding skills required/level:
Python	Interm
UNIX	Interm
Linux	Interm
Other skills required
Willing to train intern in:
Other skills interns may learn:	Working on a team, working on a supercomputer (Hera)
Project Description:	Intern will work with scientists to improve statistically post-processed NWS guidance generated by NWS for either aviation or fire weather forecasting. Intern will run jobs, conduct testing on different AI algorithms, and verify tests to determine technique that yield the most predictive value. Intern will work on a team, running python based codes on a supercomputer. Intern will be supervised by a research meteorologist who will act as their mentor, a co-mentor will also be available to help with the project. Intern will interact regularly with the MDL/Decision Support Division Chief.

Project Title	Evaluation of Probabilistic Wind/Wave Guidance
Mentor	Logan Dawson
Line office	NWS
Host office/program/lab	NCEP/Ocean Prediction Center
Location	College Park, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology, Marine Sciences
Coding skills required/level:
Python	Basic
UNIX	Basic
Linux	Basic
Willing to train intern in:	Experience with Unix and Python (or any other scripting/coding language) is preferred, but training will be provided as needed
Other skills interns may learn:	Intern will be provided the opportunity to shadow an operational forecaster
Project Description:	The purpose of this project is to evaluate probabilistic guidance for marine hazards and explore the overall utility of this guidance in the forecast process at the Ocean Prediction Center. Ensemble forecast systems can be used to compute probabilities of winds exceeding marine warning criteria (Gale, Storm, and Hurricane-Force), and this information can be used to guide the decision-making process for issuing marine hazard warnings. This project will assess the usefulness of available probabilistic wind and wave guidance and explore additional means of extracting reliable probabilistic signals from ensemble forecast systems.

Project Title	Develop first-guess fields for WPC Day 3-7 Hazards chart
Mentor	Mark Klein
Line office	NWS
Host office/program/lab	WPC
Location	College Park, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology or Atmospheric Sciences
Coding skills required/level:
Python	Interm
UNIX	Interm
Linux	Interm
GIS	Interm
Other skills required	If the intern has worked with GEMPAK, that would be a big plus
Willing to train intern in:	To an extent - Python/shell
Project Description:	WPC has updated the criteria for several of the hazards that are included in the Day 3-7 Hazards forecast. One way to gain efficiency in the forecast process is by providing meteorologically sound probabilistic first guess fields for selected hazards. This would reduce the manual effort required to complete the product and provide a consistent starting point for each forecaster.

Project Title	Develop python code for a translation correlation coefficient that s for needed changes in language AI models
Mentor	Monica Bozeman
Line office	NWS
Host office/program/lab	Office of Central Processing
Location	SSMC2
I prefer to host an intern	Virtually
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	computer science, data science, social & behavioral sciences, science & technology
Coding skills required/level:
Python	Interm
UNIX	Interm
Linux	Interm
Other skills required	An interest in non-English languages, bilingual proficiency in any of the following languages Spanish, Mandarin, Vietnamese, Korean, Arabic, Russian, etc is encouraged but NOT required
Willing to train intern in:
Other skills interns may learn:	translation APIs, parsing JSON files, working in cloud environments, automation with google sheets
Project Description:	The National Weather Service (NWS) Office of Central Processing has been tasked to automate the translation of key NWS text products into multiple languages using Artificial Intelligence (AI) models. This position will allow you to apply interests in AI, data science, and Python coding, to support Low English Proficient communities. Our office needs an intern to further develop upon existing python code that uses a translate API to retrieve a return translation (English to Target language back to English) and provide a translation correlation coefficient of sorts to help automate monitoring and identify sentences or text products that need significant translation corrections by human translators.

Project Title	Development of a Geospatial Hydro Program Dashboard application
Mentor	Paul Mckee
Line office	NWS
Host office/program/lab	Southern Region Headquarters/Operational Services Division/Hydrologic Services Branch
Location	Fort Worth, Texas Fritz G. Lantham Federal Building
I prefer to host an intern	In person
Local transportation:	personal transportation is recommended
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Water Resources, Civil Engineering (Water emphasis), Hydrometeorology, Geo-Intelligence, Geospatial Information Systems
Coding skills required/level:
Python	Basic
Linux	Basic
GIS	Basic
Other skills required	Python Notebooks and/or ESRI AGOLcloud tools would be beneficial but not required
Willing to train intern in:	No, but could connect them with appropriate SMEs for assistance to enhance their existing coding skills
Other skills interns may learn:	Interns will collaborate with various NWS SMEs and will become familiar with the NWS Hydro Program and related Program Management, while developing relevant ESRI AGOL applications. Interns will become exposed to NWS Hydrologic operations, Program Management, and coordination with Weather Forecast Offices and River Forecast Centers, and Southern Region Headquarters.
Project Description:	The selected Intern will learn about how local, state and federal agencies decision makers use water information that supports understanding water both as a nature hazard and as a critical resource to be managed. With this knowledge, the Intern will work with Service Hydrologists and Hydrometeorologists to develop a prototype Geospatial Hydro Program Dashboard application that enables efficient and effective management of our local Hydro Program activities. The project will combine technical, programmatic and geospatial skills with program management knowledge to develop a geodatabase, web map, and dashboard that enables users at all local, regional, and national levels quick synthesis of program status. The intern will coordinate with key internal stakeholders on application requirements while working closely with the Hydrologic Services Branch Chief for project guidance and supervision. Hosted at the National Weather Service Southern Region Headquarters - Hydrologic Services Branch in Fort Worth, TX, this project will be conducted in three phases: identifying and compiling project requirements, developing a geodatabase for storing additional hydro program data, and developing web maps and dashboard for effective display and analysis of critical hydro program management activities. Development methods and applications may include (but are not limited to) ArcGIS Online (AGOL) applications, python notebooks, and Google cloud applications. The project will also include time and visits for the intern to familiarize themselves with National Weather Service field offices and regional headquarters.

Project Title	Organizing conference to develop methods to improve hazardous weather safety and messaging to Underserved and Vulnerable Populations (UVPs)
Mentor	Sam Lashley
Line office	NWS
Host office/program/lab	WFO Indianapolis (IND)
Location	Indianapolis, IN
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Atmospheric Sciences, Meteorology, Sociology with focus on underrepresentative and vulnerable groups.
Coding skills required/level:
GIS	Interm
Other skills required	Outgoing, social skills, people person
Willing to train intern in:	No. Coding is not required
Other skills interns may learn:	Organization, time management, scheduling meetings, partner interactions and engagement
Project Description:	The student intern will work directly with NWS Indianapolis staff and will take a lead role in organizing and participating in the first National Weather Service Indianapolis Conference to Improve Hazardous Weather Safety and Messaging to Underserved and Vulnerable Populations (UVPs). This will be a one day conference that brings community and government organizations together with the NWS to identify local UVPs and then find the most effective methods to improve communication and education about hazardous weather with UVPs. Expected outcomes include identification of local UVPs and the organizations that work with them directly; develop new relationships and opportunities to work with these organizations; identify best practices for hazardous weather messaging and safety information aimed at UVPs; take results from conference and develop a UVP template for other NWS offices to use in a similar manner. In addition to organizing the conference and agenda with WFO Indianapolis staff, the student intern will work closely with the Warning Coordination Meteorologist by attending partner engagement meetings and interacting with NWS partners. The student will also be able to shadow NWS meteorologists on shift and help develop educational materials that can be used for outreach.

Project Title	Climatology of the Eastern Pacific non-tropical marine warnings
Mentor	Sandy Delgado
Line office	NWS
Host office/program/lab	NHC
Location	Miami, Florida
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology
Coding skills required/level:
Python	Basic
Linux	Basic
GIS	Interm
Willing to train intern in:	No
Other skills interns may learn:	Operational meteorology procedures
Project Description:	During the winter and spring months, the eastern Pacific Ocean experiences many weather events that produce gale to storm-force winds that last hours to days. On rare occasions, these winds can reach hurricane force. The objective of this project is to catalog all the non-tropical warnings issued by the Tropical Analysis and Forecast Branch at the National Hurricane Center for the eastern Pacific basin. This project will create an accurate climatology of the region using the archive of NHC products and the Mariners Weather Logs. The student is also expected to use GIS to create multiple ways to display the data (like by month, year, region, etc). The goal of this project is to improve our understanding and provide guidance to forecasters. In addition, it will become a source material for outreach to the countries affected and the mariners in our area of responsibility.

Project Title	Probabilistic Weather & Water Forecast Decision Support Messaging Development
Mentor	Scott Carpenter
Line office	NWS
Host office/program/lab	Western Region Headquarters - Regional Operations Center
Location	Salt Lake City, UT
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Computer Science, Statistics, Information Systems, GIS, Atmospheric Science, Social Science
Coding skills required/level:
Python	Interm
UNIX	Basic
Linux	Basic
GIS	Interm
Other skills required	Interest in Atmospheric Sciences and social sciences a plus, but not required.
Willing to train intern in:	No
Other skills interns may learn:	Regional Operations Center data management, analysis and communication skills. Compiling, assessing, repackaging, and effectively & efficiently presenting information to a broad range of federal family core partners.
Project Description:	The purpose and expected outcomes of the project center around the development of graphics for ROC(s) to use to effectively advance the effective display and use of a wide range of probabilistic weather and water forecast information for communication to core partners across the region. This project will include exposing the intern to the operations of a Regional Operations Center to best understand and predict how the use of probabilistic weather and water forecast information can assist decision making by regional scale core partners in the near future. Guidance will be provided by the entire ISD-Operations Branch staff, including the Emergency Response Specialists directly responsible for the provision of services from the Regional Operations Center. Supervision will be provided by the ISD-Operations Branch Chief who is also the WR ROC Meteorologist-In-Charge.

Project Title	Year-round pattern-based climatologies of non-convective SIGMETs issued by the AWC
Mentor	Sean Campbell
Line office	NWS
Host office/program/lab	Aviation Weather Center (AWC)
Location	Kansas City, MO
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Sciences/Meteorology
Coding skills required/level:
Python	Interm
UNIX	Interm
Linux	Interm
GIS	Interm
Other skills required	Database query skills
Willing to train intern in:	No
Other skills interns may learn:	Aviation weather hazards/forecasting, basic uses of AWIPS and N-MAP
Project Description:	Year-round pattern-based climatologies of SIGMETS (essentially, aviation weather warnings) for non-convective severe turbulence, non-convective severe icing and significantly reduced visibility due to blowing dust/sand across the contiguous United States do not currently exist. Through research, database queries, coding, etc., the student would generate year-round climatology graphics/displays of these SIGMETs. Further research by the student is expected to reveal distinct (seasonal) meteorological patterns that favor conditions resulting in the issuance of non-convective SIGMETs. The mentor/co-mentor will provide onsite science/technical guidance. End goals for the project include developing graphical pattern-based tools, developing/refining conceptual models and developing AWIPS procedures/N-MAP SPFs for AWC meteorologists to utilize as they forecast non-convective severe turbulence, non-convective severe icing and significantly reduced visibility due to sand/dust throughout the year.

Project Title	Harnessing historical data to improve tsunami response
Mentor	Summer Ohlendorf
Line office	NWS
Host office/program/lab	National Tsunami Warning Center (NTWC)
Location	Palmer, AK
I prefer to host an intern	In person
Local transportation:	It's a small town and there's not much public transportation. Potential to arrange use of a commuter bicycle, etc
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Earth, atmospheric, or ocean science, other physical science, computer or data science
Coding skills required/level:	None
Other skills required	The sophistication of the project can be tailored to the intern’s skillset. Experience with databases would be an asset, but not a requirement.
Other skills interns may learn:	The intern will have the opportunity to learn about and participate in operational geohazard monitoring, particularly in the fields of seismology and oceanography. They may also choose to learn in depth about topics like emergency ing systems and science communication.
Project Description:	Since tsunamis are infrequent but potentially catastrophic, it is important to maximize the knowledge gained from each individual event. This includes significant tsunamis as well as small local or regional tsunamis, and earthquakes that are large enough to public messages but not cause measurable tsunami waves. The NTWC has many records of operational earthquake and tsunami data and response actions that are not currently being fully utilized to inform ideal future response. NTWC seeks an intern to interrogate, organize, and analyze this information to help more accurately forecast hazards and craft messaging for future potentially tsunamigenic events. This information can be used to better target ing, reducing overing and bolstering public trust. The sophistication of the project can be adjusted to the intern’s skill level. The intern will also have the opportunity to learn about and participate in operational monitoring using seismic and ocean data, and may contribute to other developmental projects at the center as time and interest permit. The intern will be supervised by NTWC management but have the chance to collaborate with multiple NTWC duty scientists and other staff from a variety of backgrounds.

Project Title	Catalog National Centers for Environmental Prediction (NCEP) probabilistic products
Mentor	Tabitha Huntemann
Line office	NWS
Host office/program/lab	NWS/NCEP/Office of Director
Location	College Park, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	No preference - physical and social sciences encouraged
Coding skills required/level:	None
Other skills interns may learn:	Familiarity with NCEP products and services
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. Virtually all the meteorological data collected over the globe arrives at NCEP, where environmental scientists analyze this information and generate a wide variety of environmental guidance information. NCEP delivers national and global weather, water, climate and space weather guidance, forecasts, warnings and analyses to a broad range of users and partners. These products and services respond to user needs to protect life and property, enhance the nation's economy, and support the nation's growing need for environmental information. 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.

Project Title	Evaluation of probabilistic data in decision support for wildfires on the southern Great Plains.
Mentor	Todd Lindley
Line office	NWS
Host office/program/lab	OUN
Location	WFO Norman, National Weather Center, Norman, OK
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	meteorology, atmospheric science, computer science, data science, climatology, environmental science
Coding skills required/level:
Python	Interm
GIS	Interm
Other skills required	A knowledge of synoptic and mesoscale meteorology, and a strong interest in wildland fire meteorology is desired, but not necessary. The candidate will need to have the ability to retrieve data, create GIS plots, and perform basic statistical analyses.
Willing to train intern in:	No, although limited assistance may be available from onsite experts.
Other skills interns may learn:	Learn communication and presentation skills, both written and oral.
Project Description:	This project will evaluate emerging tools for probabilistic guidance in wildland fire forecasts and services. The successful candidate will coordinate with principal investigators engaged in the development of ensemble model-based fire parameters to assess performance of these tools via retrospective analyses of past high-impact wildfire episodes on the southern Great Plains.

Project Title	Optimize the use of Atmospheric River Reconnaissance Observations for Improving Precipitation Forecasts
Mentor	Vijay Tallapragada
Line office	NWS
Host office/program/lab	NCEP/EMC
Location	NCWCP, College Park
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Atmospheric Science
Coding skills required/level:
Python	Interm
Matlab	Interm
Met Plus	Interm
Fortran	Interm
UNIX	Interm
Linux	Interm
Other skills interns may learn:	NWP Modeling and Data Assimilation
Project Description:	This project aims at improving the use of Atmospheric River Reconnaissance Observations in operational modeling and data assimilation systems for improving the forecasts of atmospheric rivers and their impacts. The intern will be trained on running operational numerical models and is expected to conduct data impact experiments under close supervision of the PI and the team members, analyze results using innovative tools, and present them at appropriate venues including collaborative team meetings and internal/external conferences/workshops.

Project Title	Discover the excitement and complexities of ensuring the Nation's maritime safety through ocean observations.
Mentor	William Burnett
Line office	NWS
Host office/program/lab	National Data Buoy Center
Location	Stennis Space Center, MS
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Physical Sciences
Coding skills required/level:
Python	Basic
Matlab	Basic
C++	Basic
Met Plus	Basic
R	Basic
SAS	Basic
Fortran	Basic
UNIX	Basic
Linux	Basic
GIS	Basic
Willing to train intern in:	Maybe - Matlab
Other skills interns may learn:	Engineering, Budgeting, IT
Project Description:	The National Data Buoy Center (NDBC) operates the world's largest marine observing network from one location to ensure the nation's maritime safety. After a very successful Lapenta internship at NDBC in 2023, we intend to expand the program by integrating the Lapenta scientist into all aspects of NDBC's mission. Work with data scientists in the Mission Control Center. Work alongside technicians as they build tsunami, climate and weather buoys. Interact with engineers as they create the newest buoy payloads for long term climate and tsunami monitoring. At the same time you will be mentored by a number of well-known scientists that keep NDBC operational 24 hours a day. Desired outcome from the internship is improved data analytics to ensure NDBC stakeholders can visualize and understand complex physical observations to make informed decisions.

Project Title	Develop new software for AWIPS, providing solutions for enhancements and/or defects
Mentor	Jim Calkins
Line office	NWS
Host office/program/lab	Central Processing/TEAM Team
Location	SSMC2
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Computer Science/Meteorology
Coding skills required/level:
Python	Basic
UNIX	Interm
Linux	Interm
GIS	N/A
Other skills required	Java programming experience (Interm or Expert)
Other skills interns may learn:	AWIPS usage and development; coordination with NWS field offices
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 in Java and/or Python that will benefit the operational forecasters throughout the National Weather Service.

Project Title	Climatology, forecasting, and impacts of Super Fog
Mentor	Jon Zeitler
Line office	NWS
Host office/program/lab	WFO EWX - Austin-San Antonio, TX
Location	New Braunfels, TX
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology or Atmospheric Science
Coding skills required/level:
Python	Basic
Linux	Basic
Other skills required	No
Willing to train intern in:	Yes if needed, Python and Excel
Other skills interns may learn:	Statistical analysis, specialized forecasting tools, and collaboration with a number of NWS offices.
Project Description:	Super fog is formed by an excess of consdenation nuclei from smoke, and is a serious hazard to transportation. Super Fog occurs during the cool half of the year (October to April), and historically has been associated with prescribed burns and wildfires. However, recent celebrations (e.g., New Year's Eve) with fireworks have caused significant tens of vehicles accidents. The intern will be part of a team developing a climatology of these events, forecast tools and techniques, and outreach and presentation materials for public safety partners and other NWS offices.

Project Title	Identifying common characteristics across prepared and resilient “socially vulnerable” communities
Mentor	Wendy Marie Thomas
Line office	NWS
Host office/program/lab	AFS
Location	AFS
I prefer to host an intern	Virtually
Local transportation:	Not necessary
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Journalism or Science Journalism
Coding skills required/level:
GIS	Basic
Other skills required	Journalism, Writing, Science
Other skills interns may learn:	Communication, Listening, Cultural communication nuances, Economic realities
Project Description:	The project is intended to highlight vital factors that lead toward survival and rebuilding among “socially vulnerable” communities in the face of extreme weather. This project will enhance our ability to perform the NWS’ Mission which is the “protection of lives and property, and enhancement of the national economy.” We seek to increase our effectiveness in reaching the most vulnerable who may experience social barriers or stressors that may limit their ability to hear, act or respond to our warnings. While many of these barriers are beyond the NWS’ Mission to resolve, we nevertheless must continually find ways to successfully reach out to all. We believe that there are some key factors that are common to “socially vulnerable” communities that have successfully prepared and responded to our warnings. This project is an exploration of that hypothesis. The Intern(s) will work with NWS HQ staff and co-conduct interviews of NWS Offices (one from each Region), and the intern will write a one-page synopsis of each interview. Upon interviewing all Regions, the intern will work with the NWS HQ staff to identify the common factors that lead to successful actions. The end goal is to draft this report that NWS offices can send out to their communities during meetings and other engagements, to help foster awareness on the pre-event steps that lend to a prepared and resilient population.

Project Title	Support integration and calibration of weather reference instrumentation
Mentor	Ashby Hawse
Line office	NWS
Host office/program/lab	Sterling Field Support Center
Location	Sterling , Va
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorological, Physicals Scientist, Industrial Engineering, Electronics Technician
Coding skills required/level:	None
Other skills required	Mechanically Inclined
Other skills interns may learn:	Yes, Calibration Certification
Project Description:	This project plans to introduce candidates into weather instrumentation evaluation for acquisition expectance by the Nation Weather Service. Candidate will participate in activates to develop test planning, procedures, and resources scheduling. Additionally, candidates will be introduces to and participate in pressure standard calibration at the Sterling National Pressure laboratory. Other areas will include but not limited to; Wind Tunnel Operations, Environmental Chamber application, Altitude Chamber familiarization, Participate in Balloon Born sounding evaluation, and other hands on application as required.

Project Title	Lightning Safety and WRN Ambassador Outreach
Mentor	Charlie Woodrum
Line office	NWS
Host office/program/lab	Weather Forecast Office
Location	Pueblo, CO
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology / Atmospheric Sciences
Coding skills required/level:
Fortran	Basic
UNIX	Basic
Linux	Basic
GIS	Interm
Other skills required	We are looking for a Lapenta intern to help our office WRN Ambassador and lightning safety programs by building relationships with our parks (local/state/national) and wilderness areas.
Other skills interns may learn:	Interns will learn outreach skills, AWIPS, observations, and basic forecasting.
Project Description:	Our Lapenta intern to supercharge our office WRN Ambassador and lightning safety programs by building relationships with our parks (local/state/national) and wilderness areas. This will help build weather preparedness throughout our communities and wilderness areas. The intern will help organizations work through the Lightning Safety Toolkits to make a plan for lightning safety. The student will work with staff to learn the insides and outs about lightning safety and how to build a Weather-Ready Nation. This will involved working closely with our forecasters and WCM.

Oceanic and Atmospheric Research (OAR)

Project Title	NOAA's role in the Great Lakes Water Quality Agreement
Mentor	Deborah Lee
Line office	OAR
Host office/program/lab	GLERL
Location	Ann Arbor, MI
I prefer to host an intern	In person
Local transportation:	Transportation could possibly be arranged with other CIGLR interns
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	water resources management and policy
Coding skills required/level:	None
Other skills required	Ability to write, develop presentations and coordinate with others
Other skills interns may learn:	Great Lakes water quality and quantity policy and NOAA's role
Project Description:	The purpose of this project is to give the intern a deep understanding of the Great Lakes Water Quality Agreement (GLWQA) and NOAA's role in meeting the Agreement's objectives. The expected outcome will be a detailed presentation(s) to be used in training NOAA staff and briefing senior leadership. Duties will consist of reviewing the Agreement and other documentation, interviewing current NOAA staff supporting the Agreement, attending high-level binational Great Lakes Executive Committee (GLEC) meetings, Annex meetings and lakewide action and management plan meetings. The intern will work directly with NOAA's representative on the GLEC (GLERL Director and Regional Team Leader) and be supported by GLERL's Information Services (IS). The intern will meet twice a week with the Director and/or IS for guidance, mentoring and presentation support.

Project Title	Connection between Great Lakes and Arctic Ice Cover in Response to Teleconnection Forcing
Mentor	Jia Wang
Line office	OAR
Host office/program/lab	GLERL
Location	Ann Arbor, MI
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology, physical oceanography
Coding skills required/level:
Python	Basic
Matlab	Basic
R	Basic
Fortran	Basic
Willing to train intern in:	R or Python
Other skills interns may learn:	Basic statistics course, and R/Python programming
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. CIGLR and 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	Gaps Analysis on Risk Management and Hazard Mitigation
Mentor	Sandy Starkweather
Line office	OAR
Host office/program/lab	Arctic Research Program
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Earth science, social science, environmental policy
Coding skills required/level:	None
Other skills interns may learn:	They will learn about risk management and hazard mitigation in the state of Alaska, they will learn about a tool we use to assess observing system gaps related to this topic, they will likely also learn about climate justice
Project Description:	The US Arctic Observing Network seek to improve the availability, integration, quality and usability of observing systems in the Arctic. It is conducting a gaps analysis in the Alaskan Arctic to improve outcomes in the areas of Risk Management and Hazard Mitigation. The Alaskan Arctic is on the front lines of climate change, experiencing some of the most dramatic changes in landscapes, ecosystems and human security. This exercise will help us to understand the current state of the observing system, how observations are used and to identifying specific actions to improve observations to support climate resilience. Our intern will have an opportunity to review the content of dozens of interviews, learn about and help us advance progress on our mapping exercises using our BENEFIT tool and analyze early results towards concrete recommendations for NOAA and other federal agencies.

Project Title	Quality controlling meteorological data sets, and climatological records to determine scientific trends
Mentor	David Marshall
Line office	OAR
Host office/program/lab	GML
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Meteorology, Engineering, Earth Science
Coding skills required/level:	None
Project Description:	The purpose of this internship is to assist in the analysis and quality control of meteorological data in support of climate science research. The duties and responsibilities of the intern would be to QC a backlog record of meteorological data from a handful of remote observatories, complete ongoing daily QC of data, analyze for non-physical characteristics and assist in the remote troubleshooting of met data collection systems with observatory staff. Training, guidance, and supervision on QC methods, system functions, etc. will be provided by members of the NOAA OAR Global Monitoring Laboratory (GML) Observatory Operations (OBOP) Met Team. Expected outcomes of project would be completion of the backlog of meteorological data QC, Learning about how meteorological data support climate science research in NOAA/OAR, and developing experience for the intern to assist in future career development.

Project Title	Sampling and Analyzing Data from In Situ Balloon Profiling of the Atmosphere
Mentor	Gary Morris
Line office	OAR
Host office/program/lab	GML
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	any STEM major
Coding skills required/level:	None
Other skills required	being able to program in one language would be helpful. Also, familiarity with functions on Google Sheets/Excel would be helpful.
Willing to train intern in:	Excel, IDL
Other skills interns may learn:	ozonesonde preparation and launching
Project Description:	Participants in this project will learn to prepare, calibrate, and safely fly ozonesondes -- balloon borne instruments that measure ozone concentrations as they ascend from the surface to altitudes of ~30 km. In addition, participants will learn how to implement quality controls and quality checks on the measurements. Having gathered the data and quality checked the data, participants will then analyze the data through comparisons with long-term records, remote sensing (i.e., satellite) data, and ground-based observations. Data to be analyzed include ozone (concentration and total column amount), pressure, temperature, humidity/water vapor, wind speed, and wind direction as a function of altitude.

Project Title	Analyzing balloon-based measurements of water vapor to evaluate SAGE III and MLS sensors
Mentor	Elizabeth Asher
Line office	OAR
Host office/program/lab	GML
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Atmospheric Sciences, Earth and Space Sciences, Statistics, Physics
Coding skills required/level:
Python	Interm
Other skills required	familiarity with descriptive statistics (basic), excel (basic), time management skills (Interm), writing and communication skills (Interm)
Willing to train intern in:	Yes (python)
Other skills interns may learn:	Injection of satellite data, QA of balloon-based data, Statistical data analysis, Satellite validation, Writing and presentation skills, & Assistance preparing a frost point hygrometer for flight and assistance with a balloon launch
Project Description:	Expect outcomes: present at OZWV group meeting, submit an abstract to present at a regional or national geosciences meeting (with expectation that they will present at that meeting following period of study), be a contributing author on a publication on satellite validation. Guidance: 2x+ per week 1- hour meetings with project supervisor to discuss approach, challenges and next steps. Duties listed below.

Project Title	Verification of the real-time smoke and dust simulations produced by NOAA's RRFS-SD model
Mentor	Ravan Ahmadov
Line office	OAR
Host office/program/lab	GSL
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	atmospheric science, computer science or related field
Coding skills required/level:
Python	Interm
UNIX	Basic
Linux	Basic
Other skills required	Forecast model verification
Willing to train intern in:	No
Other skills interns may learn:	Smoke modeling, air quality data, satellite observations
Project Description:	NOAA/GSL in collaboration with other laboratories is developing next-generation coupled weather-smoke-dust forecast model RRFS-SD: https://rapidrefresh.noaa.gov/RRFS-SD The model simulates smoke and dust concentrations at high spatiotemporal resolution over North America. The intern will work with the GSL and CIRES scientists to compare the experimental model forecasts with the surface and satellite based remote sensing observations. The intern will be use the MELODIES-MONET verification tool (written in Python) to perform the model verification. The model output will be provided to the intern.

Project Title	Evaluate and enhance an inline Python-based data assimilation monitor for both real-time and retrospective runs
Mentor	Terra Ladwig
Line office	OAR
Host office/program/lab	GSL
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	There exists good public transportation, but bringing a car or bike will be good.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science, Computer Science, STEM
Coding skills required/level:
Python	Expert
Fortran	Basic
UNIX	Basic
Linux	Interm
Willing to train intern in:	Yes, Python
Other skills interns may learn:	Data Assimilation, Python
Project Description:	Atmospheric data assimilation can make the best use of observations and background information to yield a better initial condition for weather forecasts or a better analysis of the current atmosphere for situation awareness. Consequently, data assimilation has been prominently featured as one of the three core pillars within the Priorities for Weather Research (PWR) report, released by NOAA in December 2021, which outlines a strategic investment plan for weather research over the coming decade. Safeguarding invaluable data assimilation investments necessitates vigilant monitoring of data assimilation performance, both in real-time and retrospective scenarios. In the operational or operational-like real-time/retrospective realms, numerous unforeseen factors can potentially disrupt the smooth execution of data assimilation processes. For instance, critical observation types may be absent, incorrect parameters might be inadvertently configured, or unnoticed data corruption in static files could occur. Such anomalies may remain undetected for an extended period. A complementary data assimilation monitoring system serves as a proactive sentinel, swiftly uncovering any potential issues in the data assimilation workflow, thereby saving substantial human effort and computational resources. This kind of monitoring system can also be valuable in providing quick feedback on new data assimilation algorithms. The NOAA Global Systems Laboratory has been developing a Regional Rapid Forecast System (RRFS) and a 3-D Real-Time Mesoscale Analysis (RTMA) system. The data assimilation component plays a key role in both systems, demanding substantial development efforts. Currently, there is a notable absence of a complementary tool to effectively monitor the data assimilation process within the RRFS/3DRTMA workflow. Developers/users find it difficult to check even some basic data assimilation diagnostics, such as the histograms of OmB (observation minus background) and OmA (observation minus Analysis) statistics. There is an ongoing Unified Graphic project at GSL to provide some data assimilation diagnostics on the cloud. This project will leverage available resources from the Unified Graphic project, evaluate and enhance the Python-based data assimilation monitor. This project aims to welcome one or two interns. The project's ultimate outcomes are structured into multiple tiers, contingent upon the number of interns involved and their level of proficiency in Python programming. Completion of the Tier 1 requirement will signify the success of this internship. Tier 1: Evaluate and enhance the framework for the package, incorporating the capability to furnish observation statistics, including counts of assimilated, monitored, and rejected observations, alongside observation histograms, as well as time series data for OmB and OmA statistics. This information can be accessed both locally or from the online NOAA GSL MATS system to meet different user needs. Tier 2: spatial distribution map of observations, model counterparts, analysis counterparts, OmB, OmA, AmB Tier 3: cloud analysis monitoring: Interim data assimilation products, like proxy reflectivity converted from lightning, processed NASA LaRC cloud products before ingestion by cloud analysis, radar reflectivity observations, background and analysis, etc. Tier 4: Evaluate and compare data assimilation statistics between different RTMA/RRFS versions.

Project Title	Improving the Prediction of Southern Ocean Boundary-Layer Clouds
Mentor	Anders Jensen
Line office	OAR
Host office/program/lab	GSL
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Atmospheric Science / Meteorology
Coding skills required/level:
Python	Expert
Fortran	Interm
UNIX	Interm
Linux	Interm
Other skills required	No
Willing to train intern in:	Yes, Python and Fortran
Other skills interns may learn:	Yes, interns will learn to work with weather model output in various formats, and they will learn to run a single-column model.
Project Description:	Boundary-layer clouds over the Southern Ocean are a critical component of Earth’s radiation budget, yet weather models struggle to accurately predict these clouds. For this project, observations of these clouds and their environment from a 2018 field campaign (SOCRATES) will be used to evaluate NOAA’s weather models. Model errors and model sensitivities to environmental parameters gleaned from this evaluation will be further quantified using a single-column modeling framework. These results will provide valuable feedback to GSL physics developers, and ultimately help improve NOAA forecasts.

Project Title	Evaluating the impact of radar data availability on gridded precipitation products in the Colorado Rockies
Mentor	Janice Bytheway
Line office	OAR
Host office/program/lab	PSL
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science, Meteorology, Physical Science, Earth Science
Coding skills required/level:
Python	Interm
Linux	Interm
Willing to train intern in:	Willing to assist with coding and provide some existing code for the project, but intern should have some proficiency with Python
Project Description:	Recent research into operational high-resolution gridded precipitation estimates and forecasts in the Colorado Rockies has found these products to underestimate total cold season precipitation. However, in some basins, precipitation was overestimated. It is hypothesized that these basins may be in regions where NWS radars are not blocked by terrain. This study seeks to combine a variety of NOAA operational gridded multisensor precipitation estimates and forecasts with observations to assess how well the gridded products can represent cold season precipitation as a function of the availability and quality of radar data. The results of this project are anticipated to provide important contextual information to forecasters and the hydrologic modeling community, communicated via conference presentation and peer reviewed publication. The intern will be responsible for analysis of the data and familiarizing themselves with the relevant literature. In addition to the primary mentors, the intern will have access to the entire Physical Sciences Lab (PSL) Hydrology Applications Division, made up of world-class experts in precipitation and hydrologic observations, modeling, and processes.

Project Title	Improving NOAA's weather forecasts by assimilating a new snow cover product in our land data assimilation system.
Mentor	Clara Draper
Line office	OAR
Host office/program/lab	PSL
Location	Boulder, CO
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Hydrology, data assimilation, math, atmospheric sciences.
Coding skills required/level:
Python	Basic
Fortran	Basic
UNIX	Basic
Linux	Basic
Other skills required	An interest in data assimilation is required.
Willing to train intern in:	Python, Fortran, HPC usage.
Other skills interns may learn:	They will become familiar with data assimilation, and analyzing remotely sensed observations and model output.
Project Description:	NOAA currently assimilates Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover observations into our operational weather forecasting system. These observations improve our low-level temperature forecasts over snowy regions. IMS provides a daily high quality snow cover data set with complete daily Northern Hemisphere coverage. However there can be delays in representing changes in snow cover in the IMS data. By comparison, Visible Infrared Imaging Radiometer Suite (VIIRS) snow cover observations are less processed and do not have complete daily coverage, but do not have the same complications with delays in representing changes in snow cover. This project will compare the impact of assimilating the IMS and VIIRS snow cover observations, leading to recommendations as to whether NOAA should pursue updating our global weather forecasting system to assimilate VIIRS observations in place of the IMS observations. The intern will be responsible for preparing the VIIRS data, running the assimilation experiments, evaluating the assimilation output, and making an initial recommendation on potential future use of the VIIRS snow cover observations. They will meet with their mentor as needed, and will be given required assistance in the use of NOAA’s land data assimilation systems, and associated software, and in presenting the experimental results.

Project Title	Analysis of HAFS model forecast
Mentor	Sundararaman Gopalakrishnan
Line office	OAR
Host office/program/lab	AOML/ Hurricane Research Division
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric, Ocean Science, Engineering or Data Sciences
Coding skills required/level:
Python	Expert
Matlab	Basic
Fortran	Basic
UNIX	Expert
Linux	Expert
Other skills required	Passion for working with hurricane models and outputs (e.g. HAFS).
Willing to train intern in:	Will help in learning NOAA components of the HAFS system
Other skills interns may learn:	NOAA HAFS modeling system, hurricane forecast and analysis
Project Description:	AOML’s Hurricane Modeling Group was founded in 2007 to advance hurricane forecast models through development and targeted research on understanding of hurricanes. Our scientists are from the diverse fields of meteorology, hurricane modeling, computer and data sciences. From inception, the team has worked to improve NOAA’s hurricane modeling systems; first with the legacy Hurricane Weather Research Forecast (HWRF) model, and now with our next generation Hurricane Analysis and Forecast System (HAFS). HAFS is jointly developed between EMC/NCEP and AOML. The intern will be involved in learning HAFS, analyzing outputs from HAFS for providing improved understanding of the outputs for further improvements of the model.

Project Title	Tracking the ocean biological carbon pump using emerging 'omics approaches
Mentor	Emily Osborne
Line office	OAR
Host office/program/lab	AOML
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Marine science, microbiology, biological oceanography, oceanography
Coding skills required/level:
R	Interm
Other skills required	Microbiology and/or clean laboratory experience is preferred but certainly not required.
Willing to train intern in:	Support in R and in bioinformatics processing (e.g. Tourmaline)
Other skills interns may learn:	genetic lab protocols on eDNA extractions and PCR, 'omics data processing via Tourmaline, opportunity for a 2-day research cruise to service the sediment trap mooring.
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. The intern will support and ultimately lead the DNA extraction from recovered sediments and water column filters. The intern will have the opportunity to work with already sequenced eDNA data to practice with 'omics data workflows. The intern will have two primary supervisors (Emily Osborne and Luke Thompson) and will be joining a larger project team with other opportunities for developing mentorship relationships.

Project Title	Quantifying impacts of stressors on scleractinian corals
Mentor	Ian Enochs
Line office	OAR
Host office/program/lab	AOML/OCED
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Biology
Coding skills required/level:
R	Basic
Other skills required	Omics experience requested
Other skills interns may learn:	Omics sample processing, data analysis, lab experiments
Project Description:	The NOAA Atlantic Oceanographic and Meteorological Laboratory's (AOML) Coral Program aims to advance knowledge in reef conservation and health through an interdisciplinary approach. This is done through a variety of lab-based experiments that involve molecular techniques ('omics) and cutting-edge technology as a way to expand upon current research and inform future events. Special skills/training required: General understanding of coral physiology and reef ecology Experience with or willingness to learn coral aquaculture and/or husbandry Willingness to participate in lab and/or fieldwork Willingness to learn any applicable skills that may be required for the project Ability to organize and meet deadlines

Project Title	Evaluating the abundance of the bacteria Aquarickettsia in the coral Acropora cervicornis during heat stress.
Mentor	Stephanie Rosales
Line office	OAR
Host office/program/lab	AOML/OCED
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	biology
Coding skills required/level:
R	Interm
Linux	Basic
Other skills required	molecular lab work
Willing to train intern in:	R
Other skills interns may learn:	molecular lab skills, and data processing
Project Description:	Coral disease often follows thermal stress or bleaching events. Our previous work shows that in addition to Symbiodiniaceae expulsion, Aquarickettsia may be lost with coral bleaching, either through expulsion or starvation due to host resource depletion. The loss of this abundant taxa opens a wide niche in the microbial community that may make A. cervicornis especially susceptible to disease. However, we recently determined that genotypes with low Aquarickettsia may instead be more susceptible to thermal stress, especially under high- nutrient regimes. Although Aquarickettsia spp. are a promising indicator genus for stress response in A. cervicornis, more robust testing needs to be conducted. Comparisons between low vs. high genotype Aquarickettsia abundance have been unbalanced (i.e., low [n=2] vs. high [n=14]). This is because few A. cervicornis genotypes have been identified with less than 50% relative abundance of this putative parasite. We propose to survey coral nurseries to uncover more A. cervicornis genotypes with a low-Aquarickettsia microbial profile, allowing us to tease apart the contributions of this bacterial taxon to coral survival, disease susceptibility, and thermal stress response. As thermal stress increases the severity of both disease and nutrient stress, and is a leading cause of A. cervicornis mortality, for this proposed work we will experimentally test the utility of using Aquarickettsia abundance to predict thermal stress survivorship in a balanced experiment.

Project Title	Characterizing microbial diversity in south Florida habitats using repeated environmental DNA observations
Mentor	Enrique Montes
Line office	OAR
Host office/program/lab	AOML
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	Marine science, molecular biology
Coding skills required/level:
Python	Interm
Matlab	Basic
R	Interm
UNIX	Interm
Other skills required	The intern should be familiar with DNA extraction methods and DNA amplification using PCR techniques. Basic notion of bioinformatics would be ideal.
Willing to train intern in:	The intern will be trained in the use of UNIX command line and Python coding for bioinformatic applications
Other skills interns may learn:	The intern will become familiar with eDNA extraction techniques using Opentrons liquid handling robots, a KingFisher instrument for DNA sample purification, use of plate readers for DNA quantification, and 16S and 18S eDNA amplification using Eppendorf Thermocycler PCR machines.
Project Description:	The core goal of this project is to investigate amplicon sequence data applications to characterize microbial communities in south Florida waters and their biodiversity, and assess how environmental drivers shape spatial and temporal distributions of microbes in the study area. The project combines in situ metabarcoding and image-based observations with satellite time series records to examine biogeographic characteristics of microbial assemblages and secondary producers. The work integrates sequence observations of 16S and 18S ribosomal RNA genes, image-based plankton annotations, and field biogeochemical measurements with satellite remote sensing data to evaluate how shifts in seasonal to interannual physical and biogeochemical conditions affect the abundance and composition of primary producers, including harmful algal blooms (HABs), and zooplankton assemblages, and understand how changes in plankton communities may impact food web dynamics and ecosystem health. The program will provide a variety of data synthesis products and indicators to address information needs of state and federal agencies including the Florida Keys National Marine Sanctuary, the National Coral Reef Monitoring Program (NCRMP), Florida Department of Environmental Protection, and NOAA’s Iconic Reef program.

Project Title	Remote sensing and Biogeochemical Argo applications
Mentor	Jennifer McWhorter
Line office	OAR
Host office/program/lab	AOML
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Remote sensing, Meteorology, Oceanography, Geography
Coding skills required/level:
R	Basic
Other skills required	Remote sensing and meteorological data applications, beginner/intermediate
Willing to train intern in:	Yes, R
Other skills interns may learn:	Biogeochemical Argo data applications
Project Description:	The project entails remote sensing coupled with the Biogeochemical Argo array to expand the application of various data resources in multiple dimensions from the atmosphere to the ocean surface to the water column. The focus of this research will be in the Gulf of Mexico.

Project Title	Modify an instrument software to control a syringe pump to do titrations.
Mentor	Denis Pierrot
Line office	OAR
Host office/program/lab	AOML/OCED
Location	Miami, FL
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	computer science, electrical engineering
Coding skills required/level:
C++	Interm
Willing to train intern in:	yes, CVI (C for Virtual Instrumentation)
Other skills interns may learn:	He will learn about a Alkalinity Titration system and instrument interfacing
Project Description:	The state of the art system to measure seawater alkalinity at a climate quality level performs a titration of a seawater sample by adding acid in controlled increments and measuring the e.m.f of an electrode placed in the sample. The addition of acid is currently controlled by a Metrohm® Dosimat. The latest Dosimat models do not work with the system anymore. The idea is to replace the Dosimats with Kloehn syringe pumps. The code of the controlling software needs to be modified to control these Kloehn pumps.

Project Title	Lightning Observations with the Long Wavelength Array
Mentor	Vanna Chmielewski
Line office	OAR
Host office/program/lab	NSSL
Location	Norman, OK
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology, Physics, Computer Engineering, or a related field
Coding skills required/level:
Python	Basic
Linux	Basic
Willing to train intern in:	Python, Linux
Other skills interns may learn:	Visualization work in Python, working with unique datasets
Project Description:	The Long Wavelength Array (LWA) is a radio telescope located in New Mexico which, in addition to stars, is extremely effective at observing lightning. The radio signals arriving at the array antennas can be combined to make the most sensitive maps of lightning available today. This high sensitivity is particularly important for observing the positively charged lightning leader tips. The array is also capable of detecting and imaging multiple simultaneous point emitters and extended emission regions, something that no other lightning detection system can do right now. In this project, the intern will participate in lightning observations with the LWA during the 2024 summer storm season. Duties will include working with the LWA operators when lightning is overhead of one of the arrays, and post processing data to produce maps of lightning. The resulting maps can then be examined and combined with other collaborative observations, including other lightning detectors and radar, to do detailed studies of individual flashes. Of particular interest is investigating extended radio emission structures, and examining the difference in behavior between the negatively and positively charged channels in the lightning flash. Training and guidance will be provided for every step of the process and all tasks, including in python and linux. The project will offer the opportunity to learn about lightning and lightning physics, as well as how to work with and visualize large, complex datasets. There may also be additional, optional opportunities to be involved with data collection in the Oklahoma region.

Project Title	Revealing patterns of sea level variability and connections to the coast
Mentor	Jacob Steinberg
Line office	OAR
Host office/program/lab	GFDL
Location	Princeton, NJ
I prefer to host an intern	In person
Local transportation:	public transportation exists, but is infrequent
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	civil/environmental engineering, computer science, oceanography, physics, applied mathematics
Coding skills required/level:
Python	Basic
UNIX	Basic
Linux	Basic
GIS	N/A
Other skills required	scientific data analysis skills preferred (not required), introductory background in climate science and/or fluid dynamics
Willing to train intern in:	yes (Python), but at the intermediate level (an introductory skillset is preferred)
Other skills interns may learn:	scientific data analysis (development and application of techniques to interrogate model output and observations and gain physical understanding of the ocean)
Project Description:	This project will focus on regional sea level variability at seasonal and longer timescales. Leveraging state-of-the-art global climate models, we will partition the ocean into regions of 'like' variability with the aim of connecting any coastal location to a local region-of-influence. In taking this approach, we will gain understanding of the physical processes responsible for coastal sea level change and their spatio-temporal extent. This analysis will be carried out using readily available, simple clustering (machine learning) tools that evaluate patterns of sea level covariance. Results will highlight regions of sea level co-variability across timescales and provide a basis for understanding underlying physical mechanisms. As the nature of sea level variability changes, especially at the coast, this analysis will importantly provide a means to link coastal sea level to larger scale changes in ocean mass, heat content, and circulation. Initial project goals include: evaluate sea level variability at seasonal timescales, develop clustering data analysis skills/tools, and apply toolkit in disentangling complex patterns of climate variability. Depending on the interests and skillset of the intern, project direction will change and may focus on software toolkit development, model-observation comparison, or clustering approaches.

Project Title	Ocean Water Property and Circulation Analysis Using Argo Data
Mentor	Gregory C. Johnson
Line office	OAR
Host office/program/lab	PMEL
Location	Seattle, WA
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Applied Mathematics, Physics, Oceanography
Coding skills required/level:
Python	Interm
Matlab	Interm
Other skills required	Programming skills will be important.
Willing to train intern in:	I can help train coding in Matlab.
Other skills interns may learn:	Some physical oceanography,
Project Description:	The large and increasing number of 0-2000 m profiles of ocean temperature and salinity reported by the Argo Program (http://www.argo.ucsd.edu/) array of floats opens up a wide range of scientific investigations in observational physical oceanography that are relevant to NOAA Mission Goals of "Climate Adaptation and Mitigation", "Healthy Oceans", or "Weather-Ready Nation". For instance, the vertical extent of the ocean’s seasonal response to the reversing monsoon winds in the Arabian Sea is one possible topic. Another possible topic would be the subsurface expression of marine heatwaves.

Project Title	Compare saildrone observations with data from satellites, numerical models, and other uncrewed robotic platforms
Mentor	Chidong Zhang
Line office	OAR
Host office/program/lab	PMEL
Location	Seattle, WA
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric and oceanic sciences or related fields (environmental science, physics, etc.)
Coding skills required/level:
Python	Interm
Other skills required	Basic statistics, data processing experience
Willing to train intern in:	Yes (Python), but not from scratch.
Other skills interns may learn:	Processing large quantities of data (including numerical model output and satellite data, and UxS observations); woking as a project team member as well as independently.
Project Description:	NOAA has successfully deployed about 30 saildrones (uncrewed, robotic sea surface vehicles) to observe Atlantic hurricanes in three years since 2021. These saildrones made unprecedented observations near and inside several hurricanes, including major hurricanes Sam (2021), Fiona and Ian (2022), and Idalia and Lee (2023). These observations have been used to compare with numerical model predictions, satellite data, and data from other oceanic and atmospheric observing platforms (e.g., airplanes, drifters, floats, ocean gliders, moored buoys). Selected Lapenta interns would have opportunities to work with scientists in PMEL to processing these and other saildrone data (e.g., from the Arctic) and to participate in the planning for the saildrone hurricane observations in 2024. The main goal of the research is to quantify, evaluate, and interpret similarities and differences between datasets from different observing platforms to inform their applications to environmental prediction and future development of observing technologies. Interns will discuss with their mentors to design focused research projects to fit their interests. Interns are expected to join weekly intern team meetings to update their progress and help each other with research issues.

Project Title	Opportunity to learn behind-the-scenes and be a part of a science research program working on Air Quality and Climate
Mentor	Monika Kopacz
Line office	OAR
Host office/program/lab	Climate Program Office/ AC4 Program
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science, Earth System Science, Climatology, Physics, Mathematics, Public Policy, Environmental Science, or any related fields
Coding skills required/level:	None
Other skills interns may learn:	Project and program management, data visualization, bibliometrics, literature review
Project Description:	The Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) program at NOAA's Climate Program Office (CPO) focuses on advancing our understanding of the Earth's atmospheric composition, particularly related to greenhouse gases, aerosols, and their impact on climate change. As a competitive research program, AC4 supports research efforts that enhance our process-level understanding of the Earth System through observation, modeling, analysis, and field studies, to support the development and improvement of models, and provide valuable insights for carbon and air pollution management efforts. Internship with the AC4 program offers a great setting in which to learn about a broad range of topics within climate science and applications across NOAA and beyond, especially about atmospheric composition. AC4 welcomes any intern interested in atmospheric science research, particularly students who would like to learn more about research program management. AC4 has a number of projects in which the intern could engage: 1. Assist with the development of AC4 research project focussed webstory 2. Assist in the expanded front-end science content development of the AC4 Website 3. Literature review and analysis of AC4 supported publications matched with AC4 research themes 4. Data analysis and data-visualization for AC4-relevant research topics (for e.g., Analyzing the relationship between extreme temperatures and urban air quality through historical data analysis) If a student has a particular skill or interest they would like to apply to AC4-relevant topics, we would work with them to incorporate that into their project. Work will be tailored to the interests of the intern.

Project Title	Expanding the Steps to Accelerating Community Climate Action
Mentor	Frank Niepold
Line office	OAR
Host office/program/lab	Climate Program Office
Location	Silver Spring
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Communications, Education, Environmental Studies, Anthropology, sociology, public policy, public administration
Coding skills required/level:
GIS	Basic
Other skills required	Social research (Undergraduate), policy research (Undergraduate), education (Undergraduate)
Project Description:	U.S. states, cities, businesses, and other leaders are stepping up to the challenge; groups and governments committed to climate action represent over half of the U.S. population, over half of the American economy, and 35 percent of nationwide GHG emissions. Sub-national climate actions across the United States are already helping to reach the nation’s climate goals and set the groundwork for a clean energy and decarbonized future for decades to come - but we still need to dramatically accelerate action on all fronts to avoid the worst impacts of climate change and adapt to what we’ve already set in motion. The Steps to Accelerating Community Climate Action guide provides practical steps and resources to build the social infrastructure for collaborative, just climate action across communities, states, and regions. Any government or community can use the Steps to Accelerating Community Climate Action tool to initiate or enhance climate action with their community.

Project Title	Applied, interdisciplinary research and engagement to advance equitable climate adaptation
Mentor	Sean Bath
Line office	OAR
Host office/program/lab	Climate Program Office
Location	TBD
I prefer to host an intern	In person
Local transportation:	Varies by project location
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Geography, Atmospheric Science, Meteorology, Environmental Science/Studies, Political Science, Hydrology, Sociology, Anthropology, Indigenous Studies
Coding skills required/level:	None
Other skills required	Excellent written and verbal communication skills; stakeholder engagement experience preferred, strong organizational skills, social science method knowledge preferred, interest or knowledge in concepts like climate adaptation planning, indigenous knowledge
Willing to train intern in:	Some capacity for training GIS
Other skills interns may learn:	Stakeholder engagement, social science methods, communication
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 13 regional CAP/RISA teams or work on a fact finding project with the national team. 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/CAP-RISA for more information about the program and its teams. Past Lapenta projects have included engagement in community planning workshops with stakeholders in Wyoming, literature review and analysis on transformational adaptation, and scoping of a new regional team in the U.S. Caribbean.

Project Title	Optimizing discrete oxygen sampling schemes on ocean acidification research cruises
Mentor	Dwight Gledhill
Line office	OAR
Host office/program/lab	OAP
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Data science, environmental statistics
Coding skills required/level:
Python	Interm
Matlab	Interm
R	Interm
Other skills required	Ability to work as part of a team and independently. Experience accessing and analyzing large environmental/scientific data sets. A strong basis in statistical methods applied to environmental datasets.
Willing to train intern in:	No, can provide some support in MATLAB and Python
Other skills interns may learn:	Ocean acidification, ocean biogeochemistry, data interpretation and communicating results in a real world setting, ocean observation and monitoring, field research administration and decision-making
Project Description:	Designing a robust ocean observing network is essential for characterizing ocean change and its impacts to U.S. Large Marine Ecosystems (LMEs) and the communities that depend on them. Research cruises provide a unique opportunity to study broad-scale ocean change, including phenomena such as ocean acidification and deoxygenation. However, due to limited resources, it is critical that research cruises are efficient and effective. NOAA’s Ocean Acidification Program (OAP) has identified a need to optimize discrete oxygen sampling schemes on their research cruises. The incoming William LaPenta Intern would assess discrete oxygen sampling schemes from previous OAP-sponsored cruises and statistically determine through power analysis and/or AI the optimal sampling density in terms of oxygen sensor calibration, spatial and temporal coverage of data, and resource allocations.

Project Title	Delving Deeper into Qualitative Customer Feedback to Improve WPO’s Funding Application Process
Mentor	Cassandra Shivers-Williams
Line office	OAR
Host office/program/lab	WPO
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	a Social Science discipline; an interdisciplinary major like Environmental Sciences, Geography, and/or Sustainability; a major with either qualitative data or statistics experience; a physical science student (e.g., Meteorology, Atmospheric Sciences) with an interest in social science
Coding skills required/level:
R	Basic
SAS	Basic
Other skills required	qualitative data analysis (basic to intermediate), statistical analyses (basic to intermediate), experience with social science data (basic to intermediate), social science protocol development (basic to intermediate)
Willing to train intern in:	no, no coding is needed.
Other skills interns may learn:	Interns will learn social science skills related to quantitative and qualitative data analyses, protocol development, report writing, and science communication with diverse audiences
Project Description:	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. We now have baseline information on how applicants interpret, understand, and view WPO’s funding opportunities and proposal submission processes from previous competition(s). Survey results showed some things worked really well, while other things did not work so well and could improve. WPO has begun implementing recommendations revealed by these previous data analyses and refining its processes for future competitions, but more follow-up work is needed. In particular, WPO is looking to obtain deeper qualitative feedback from applicants regarding the second half of the proposal submission process—namely the proposal peer review process. To continue obtaining this critical stakeholder feedback, WPO will distribute another Applicant Customer Experience and Satisfaction (ACES) Survey at the close of the current proposal submission window (November 2023). WPO will host a William M. Lapenta Intern during Summer 2024 to assist the office by analyzing this second set of quantitative and qualitative customer satisfaction feedback. The primary objective of this project will be to develop a focus group protocol aimed at revealing deeper, contextual insights to complement the collected survey data and, more importantly, focus on evaluating WPO's proposal peer review process. WPO is planning to conduct these focus groups in Spring 2025, and as such, the development of a focus group protocol will aid in the planning of this engagement. The findings from these analyses and the creation of the protocol will help WPO continue to improve its funding competition processes and the overall user experience for applicants submitting proposals and subsequently receiving feedback on any proposals submitted to WPO competitions. For an optimal internship experience, a basic to intermediate understanding of statistical analysis is required. Experience with social science data, qualitative/quantitative data analysis, and protocol development is strongly encouraged, but not required. This internship project is the next step in a much larger, strategic effort for WPO to continually engage with the broader Weather, Water, and Climate Enterprise.

Project Title	Unified Forecast System (UFS) Student Ambassador
Mentor	Jennifer Vogt
Line office	OAR
Host office/program/lab	WPO
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
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
Coding skills required/level:
Python	Interm
Matlab	Basic
Met Plus	Basic
Fortran	Basic
UNIX	Basic
Linux	Basic
Willing to train intern in:	Yes, we are willing to train students in Fortran, Unix, Linux, and METplus.
Other skills interns may learn:	Leadership, communications, community engagement, stakeholder engagement
Project Description:	Join the Earth Prediction Innovation Center (EPIC) and Unified Forecast System (UFS) teams this summer as the student ambassador for the UFS! This summer you will have the unique opportunity to work directly with the UFS Communications and Outreach (C&O) Working group, UFS Chief Science Advisor and EPIC Community Engagement Team to help get more students involved with the UFS. This may include assisting with the UFS Roadshow where you would have the opportunity to travel to a university and teach/promote the UFS in an academic setting. This student will also have the opportunity to take and evaluate at least 1 training/tutorial of a UFS application (Short Range Weather Application or Hurricane Analysis Forecasting System) to increase their knowledge and understanding of running their own weather model and help other students do the same. As the UFS Student Ambassador you will also assist in planning a parallel track (or student panel) gauged towards undergraduate and graduate students at the third annual Unifying Innovations in Forecasting Capabilities Workshop 2024.This opportunity will build upon the work done by previous Lapenta interns and utilize the UFS Student Engagement Plan which was developed by the 2023 Lapenta Intern. Students participating in this project will finish the summer with a better understanding of how to run their own weather model application, collaborate with our communication and outreach teams to further engage students with the UFS, and develop improved communication and leadership skills.

Project Title	Advancing the use of big data infrastructure and data science tools through the Societal Data Insights Initiative (SDII)
Mentor	Jonathon Mote
Line office	OAR
Host office/program/lab	WPO
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	computer science, mathematics, statistics, data science, engineering, computational social science
Coding skills required/level:
Python	Interm
Matlab	Basic
R	Basic
SAS	Basic
GIS	Basic
Other skills required	The candidate will ideally have: 1) Experience with statistics, data mining, or AI/machine learning in academic or professional settings, 2) Experience with R, Python, Javascript or other programming languages, 3) Experience with cloud tools on Amazon, Google, Microsoft or similar platforms, 4) Some experience with knowledge graphs or graph databases, 5) Some experience with arcGIS for spatial analysis, and 6) Currently a third-year undergraduate, recently graduated and enrolled in a graduate program, or a first-year graduate student in computer science, mathematics, statistics, data science, engineering, computational social science, or a related field preferred
Other skills interns may learn:	Data integration and analysis
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 engineering intern, you will work with the SDII development team to apply your analytical and coding knowledge and skills to develop a high-visibility, high-impact project focused on inland floods and flood inundation mapping/modeling. You will explore the integration of new or existing SBES and weather data sources to create pipelines and workflows to conduct analyses and make 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	Analyzing and visualizing ocean exploration data
Mentor	Katharine Egan
Line office	OAR
Host office/program/lab	OER
Location	Virtual
I prefer to host an intern	Virtually
Local transportation:	N/A
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Biology, Statistics, Math, Engineering
Coding skills required/level:
R	Basic
GIS	Basic
Willing to train intern in:	Yes, in the R programming language. We are also willing to teach the intern how to use ArcGIS Pro.
Other skills interns may learn:	Cleaning, analyzing, and visualizing complex ocean exploration datasets
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 is interested in either applying or learning data analysis and coding skills to analyze and visualize the data that NOAA Ocean Exploration collects in unexplored areas of the ocean. The intention of this student opportunity is to have an intern shape NOAA Ocean Exploration's data management, analysis, and visualization efforts by working alongside 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 or create their own project, with ample guidance and dedicated mentorship from the Data Analytics Team Lead and the Data Analyst on the team. They will also have the opportunity to interact and meet other ocean explorers in the office. We are looking for an intern with 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). Experience with the R programming language and Shiny apps is preferred, but not necessary for this internship. The project content and priorities can also be tailored to the student’s background and interests. The intern will also gain skills in understanding deep-sea habitat and data types including seafloor mapping data, environmental data, and data on deep-sea species occurrences and distributions. Finally, the incoming intern can expect a completed data analytics and visualization project by the end of their internship that contributes to building out this effort in NOAA Ocean Exploration. We require interns to submit a final report to the program, which will form the basis for a peer-review manuscript to submit to a scientific journal. If contributing to a larger data analytics project, the intern will receive co-authorship on subsequent peer-review publications. Although not guaranteed, we will also submit a funding request for the intern to attend one scientific conference to present on their work.

Project Title	Supporting NOAA's science, service, and stewardship mission through Behavioral Health and Wellness of NOAA personnel
Mentor	Candice T. Karber
Line office	OAR
Host office/program/lab	CFO/ESD
Location	Silver Spring, MD
I prefer to host an intern	In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Social and Behavioral Sciences: Including but not limited to Social Work, Anthropology, Psychology, Sociology, Public Health, Communications, International Studies, Education, Public Policy, Marketing, Computer Science, etc.
Coding skills required/level:	None
Other skills required	Basic knowledge of Google Platforms is required, with a preference for intermediate to advanced. Basic data analytics experience is preferred, but not required. Project management, writing, and research skills are a necessary foundation for this opportunity as well as an openness to feedback, coaching, and self-reflection.
Other skills interns may learn:	Project Management; Collaboration; Wellness; Coordination, Planning, Program Development, Data Analysis; Networking; Rapid Rapport Building; and Recruitment. Will also have the opportunity to broaden their network within NOAA by working with representatives from offices and regions across NOAA.
Project Description:	Intern will work closely with the Office of Atmospheric Research (OAR) Behavioral Health and Wellness (BHW) Officer on developing, planning, executing, and analyzing wellness-related services and systems. Duties and responsibilities of this internship will focus on using information and data from the BHW Officer and OAR’s Wellness Council Workgroup to help coordinate programmatic goals and collect metrics.

Additional Projects

Project Title	Improving NOAA/NWS Storm Prediction Center (SPC) Fire Weather Forecast and Verification Techniques
Mentor	Matt Elliott
Line office	NWS
Host office/program/lab	NOAA/NWS Storm Prediction Center
Location	Norman, OK
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Meteorology/Atmospheric Science
Python	Interm
UNIX	Basic
Linux	Basic
Willing to train intern in:	Occasional advice/help with any coding needs will be provided for any required language
Other skills interns may learn:	Shadowing SPC operations
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	Working on Regional Wave Prediction System (RWPS) with WW3 model
Mentor	Ali Salimi Tarazouj
Line office	NWS
Host office/program/lab	NCWCP
Location	College Park
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD)
Preferred Majors	Ocean Engineering, Coastal Engineering
Python	Interm
Matlab	Expert
C++	Basic
Fortran	Interm
UNIX	Expert
Linux	Expert
GIS	Basic
Project Description:	RWPS is/will be built using unstructured grids. This provides us with a flexibility of increasing model grid resolutions in our areas of interest and fine tuning wave model physics for enhanced forecast skill in those areas. This proposed project will consider the use of a grid generation tool to explore different grid configurations for use with RWPS domain(s). Further optimization of model physics can then be explored for improved forecasts of specific extreme events on selected grids.

Project Title	A student with a communications background to support NHC Public Affairs and work on communications activities related to the NHC. To provide practical experience, establish a professional network, and provide broad exposure to our organization to prepare college students and postgraduates for their future career in communications. Technical support media interviews, writing, strategic messaging for social media and the NHC website
Mentor	Maria Torres
Line office	NWS
Host office/program/lab	National Hurricane Center
Location	Miami, Florida
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Communications & Journalism
Other skills required	Skill in written and oral communication sufficient to develop new information materials including news releases, online feature content (including web and social media), fact sheets, and brochures; Some knowledge of meteorology sufficient to interpret and communicate hurricane forecasts, skills in arranging media interviews and handling different social media platforms. Familiar and proficient with email communications.
Other skills interns may learn:	Develop and prepare a wide range of written products to communicate and generate media and public interest in/understanding about the science and service of NHC. These include news releases, fact sheets, and online feature content (for web and social media platforms). Provide support to NHC Communications by assisting with the NHC Media Pool during an active storm and update contact information during an event.
Project Description:	NHC is seeking a student in communications, journalism, public relations, or related field to support National Hurricane Center (NHC) Public Affairs and work on NHC communications activities related to the NHC. The purpose of this internship is to provide practical experience, establish a professional network, and provide broad exposure to our organization to prepare college students and postgraduates for their future career in communications. This would be a 27-week internship during the Hurricane season months (June - November) working alongside NHC Public Affairs, and assisting NHC leaders with a number of communications activities during the season, including: technical support for media interviews and writing strategic messaging for social media and the NHC website. Some of the National Weather Service’s most exciting and high-profile work happens at NHC, including hurricane monitoring, forecasting, impact-based decision support services, and high-impact media engagement. The intern will strengthen NHC’s relationships with the media networks throughout the hurricane season by arranging media interviews and creating online written content related to tropical weather forecasting. The intern will receive guidance on and experience first-hand how the media operations are conducted at NHC during active storms. A work space with laptop access will be provided for the student at NHC to conduct media collaboration and to perform duties and complete assignments.

Project Title	Aircraft and Mobile Measurements of Air Pollutants and Greenhouse Gases in Salt Lake City in Summer 2024
Mentor	Xinrong Ren
Line office	OAR
Host office/program/lab	Air Resources Lab
Location	College Park, MD
I prefer to host an intern	In person
Local transportation:	I recommend bringing a car to get to/from work and/or grocery stores, etc.
Academic Level	Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)
Preferred Majors	Atmospheric Science, Chemistry, Climate change
Python	Basic
Matlab	Basic
Willing to train intern in:	Either Matlab or Python
Other skills interns may learn:	Operations of atmospheric instruments that measure air pollutants and greenhouse gases.
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 Salt Lake City in summer 2024. The intern student will be involved in instrument operations in the field, data collection and analysis, and presentation of results at a scientific meeting. Field travel to Denver, CO and Salt Lake City, UT are required.

Project Title	Atmospheric profiles in convection
Mentor	Chris Fairall
Line office	OAR
Host office/program/lab	PSL
Location	Boulder, CO
I prefer to host an intern	Virtually, In person
Local transportation:	Adequate public transportation exists near my lab/office
Academic Level	Graduate (MS or PhD)
Preferred Majors	engineering, physics, meteorology
Python	Basic
Matlab	Basic
Other skills required	Knowledge of atmospheric boundary layer helpful. Matlab or Python useful.
Other skills interns may learn:	atmospheric modeling
Project Description:	We are interested in limits of standard similarity theory in near-surface profiles of wind, temperature, and humidity under convective conditions. The issues involve departures of profiles from MO theory, connections to mixed-layer properties, wind gustiness, and choice of averaging. This relates to application of bulk flux algorithms in high resolution numerical models such as LES.