2022 Projects

APPLICANTS: Please refer to the list below when selecting your preferred internship sites. Please note that this is not necessarily an exhaustive or final list of projects. More projects may be added after the application opens so you should check the list and, if needed, adjust your responses accordingly. Please note that participating offices are noted under the Program Details tab.  Some offices/labs/centers do not have projects identified yet.

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 Environmental Satellite and Data Information Service (NESDIS)

SmallSat Coordination Analysis

Host office/program/lab: ACIO-S

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship)

Preferred major for intern
Electrical Engineering/Electronics or Communications Engineering//RF Engineering

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.

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Improving utility of GOES-R data within NWS AWIPS application.

Host office/program/lab: GOES-R

What academic level is most appropriate for your project? Undergraduate/Graduate

Preferred major for intern:
Computer Science, Engineering, Meteorology, Atmospheric Science, Computer And Information Sciences, Meteorology, Oceanography, Physical Sciences, Remote Sensing Technology

NOAA mission goal:
Science and Technology Enterprise

Hypothesis or objectives:
The objective of this internship is to experience the science, technology, and culture of a major NOAA/NASA mission. The GOES-R series is a series of national spacecraft assets (2 are on orbit, 2 are still in development) with the mission of taking observations vital to the preservation of life and property. The science development, management, and implementation are all areas where an aspiring environmental scientist will find great intrigue. A data analysis project of the intern's choosing and mentor's approval will also be an outcome of the internship.

Duties and responsibilities:
Intern will be welcomed to the GOES-R Total Operational Weather Readiness-Satellites (TOWR-S) team, a small interdisciplinary team of computer scientists, engineers, and meteorologists who develop capabilities that will be incorporated into NOAA’s weather enterprise. He/She will be treated as a regular member of the team with expectations for frequent Q&A dialogue/discussions (part of the learning process), attendance at team meetings, and enthusiasm for starting and completing a data analysis project. Project should focus on data or software associated with GOES-R instruments: Earth-pointed instruments (ABI imager, GLM lightning), Solar-pointed instruments (SUVI imager, EXIS sensor), and in-situ space environment (SEISS and Magnetometer).

Expected outcomes:
Intern will develop satellite meteorology use cases employed by National Weather Service local offices and National Centers. This will involve interaction and collaboration with forecasters and techniques development meteorologists, as well as configuration and use of the NWS Advanced Weather Interactive Processing System (AWIPS). GOES-R is the 'eye in the sky' for weather in the western hemisphere and ultimately aims to preserve life and property. The team is a highly motivated energized group of professionals who have mentored numerous past interns who have gone on to capitalize on their intern experiences.

Guidance and supervision:
Intern will be guided by the GOES-R Product Readiness and Operations deputy lead, Maurice McHugh and TOWR-S lead Joe Zajic. Intern will spend much of time interfacing with the Product Readiness & Operations (PRO) team, which is comprised of meteorologists, systems engineers, and computer scientists. Mentors will be selected based on the intern's interests."

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GOES-R Satellite Exploitation Programming

Host office/program/lab: GOES-R

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
Computer Science, meteorology, remote sensing, any engineering major

Project Description
"The objective of this internship is to experience the science, technology, and culture of a major NOAA/NASA mission. The GOES-R series is a series of national spacecraft assets (2 are on orbit, 2 are still in development) with the mission of taking observations vital to the preservation of life and property. The science development, management, and implementation are all areas where an aspiring environmental scientist will find great intrigue. A data analysis project of the intern's choosing and mentor's approval will also be an outcome of the internship.

The intern will be welcomed to the GOES-R Total Operational Weather Readiness-Satellites (TOWR-S) team, a small interdisciplinary team of computer scientists, engineers, and meteorologists who develop capabilities that will be incorporated into NOAA’s weather enterprise. He/She will be treated as a regular member of the team with expectations for frequent Q&A dialogue/discussions (part of the learning process), attendance at team meetings, and enthusiasm for starting and completing a data analysis project. Project should focus on data or software associated with GOES-R instruments: Earth-pointed instruments (ABI imager, GLM lightning), Solar-pointed instruments (SUVI imager, EXIS sensor), and in-situ space environment (SEISS and Magnetometer).

A background and passion for computer science and remote sensing will be most helpful. Solid Python programming skills and data analysis experience will ease intern's ability to select and execute a data analysis project.

Intern will have an immersive experience in the development of environmental satellite data processing techniques. Work will include the development and deployment of applications of the GOES-R ABI and GLM instruments using the Amazon Web Services (AWS) cloud environment. Involves interaction and collaboration with National Weather Service Techniques development meteorologists, remote sensing scientists and experts from NOAA, NASA, and academia. GOES-R is the 'eye in the sky' for weather in the western hemisphere and ultimately aims to preserve life and property. The team is a highly motivated energized group of professionals who have mentored numerous past interns who have gone on to capitalize on their intern experiences.

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User Needs Assessment - Understanding Needs for Innovation

Host office/program/lab
STAR (Center for Satellite Applications and Research)

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric, Oceanography, Meteorology

Project Description
Understanding user needs for development of products and services is more important now than ever before. NOAA’s Satellite and Information Service’s community of users are diverse and their needs are continuously evolving. This project uncover a user's needs, challenges and working conditions, and develop an understanding of usage of NESDIS's products and services. Capturing user needs and developing impactful products and services that meet the needs, goals and objectives of a user, requires frequent communication and effective collaboration through an iterative process of redesigning, testing, and demonstration of the product and/or services. This intern will analyze the needs of a selected user community (selected by the intern) through a process of interviews using customer service theory. A report will be developed and findings will inform innovation processes at NOAA.

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Using oceanographic and climate data to study climate trends and correlations, such as sea surface temperature and sea surface winds, El Nino Index, and Marine Heat Waves

Host office/program/lab
National Center for Environmental Information

What academic level is most appropriate for your project? Undergraduate/Graduate

Preferred major for intern
Climate, Oceanography

Project Description
Using oceanographic and climate data to study climate trends and correlations, such as sea surface temperature and sea surface winds, El Nino Index, Marine Heat Waves, etc. You can use programming languages in either Python, R, or Matlab.

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Exploring atmospheric sounding data from small satellites

Host office/program/lab
STAR (Center for Satellite Applications and Research)

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric science/Geoscience/Earth Science

Project Description
The proliferation of small satellite launches in recent years provides great opportunities for studying the earth and its atmosphere with a number of microwave sounders on several platforms. The NOAA Center for Satellite Applications & Research is working closely with partner agencies and offices to explore small satellite data for fit-for-purpose applications. For this project, the summer intern will perform hands-on analysis of new microwave atmospheric sounder observations with Python, and evaluate the data quality by comparing with other measurements using a variety of methods, such as validation using in-situ data and intercomparison with other satellites. The pros and cons of small satellite measurements will be evaluated, and potential new applications will be explored. The student is expected to develop a presentation at the end of the project summarizing the study, which can be further improved and presented at conferences, and potentially be included in journal publications.

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Estimating hurricane intensity from satellite measurements

Host office/program/lab
STAR (Center for Satellite Applications and Research)

What academic level is most appropriate for your project? Undergraduate/Graduate

Preferred major for intern
Atmospheric science, computer science, mathematics and Physics

Project Description
Our preliminary experiment showed that hurricane surface minimum pressure can be calculated from NOAA JPSS Advanced Technology Microwave Sounder (ATMS) observations. Due to the complexity of the problem, the retrieval accuracy needs to be improved. We propose an AI based algorithm to deal with the complexity and big data.

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Calibration and Validation of VIIRS Vegetation Index and Green Vegetation Fraction Products

Host office/program/lab
STAR (Center for Satellite Applications and Research)

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
atmospheric science, or physics

Project Description
The objective for this internship is to provide validation support for VIIRS vegetation products (Vegetation Index and Green Vegetation Fraction). These data sets are produced operationally, and archived and distributed from the NOAA CLASS system. Calibration and validation of these data products is ongoing, and so far these products have been shown to be consistent with several other data sets. It is hypothesized that good consistency will also be found when comparing the vegetation products against additional data sets.

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AI-based Voice Control Frame Development for a Web-based Integrated Calibration and Validation System Long-term Monitoring System in NOAA STAR

Host office/program/lab
STAR (Center for Satellite Applications and Research)

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Computer sciences

Project Description
"The Integrated Calibration and Validation System (ICVS) Long-term Monitoring (LTM) system (https://www.star.nesdis.noaa.gov/icvs/) was established in October 2010 and benefited many domestic (NOAA and beyond) and international collaborations. It has provided over 10 years of near real time (NRT) and LT satellite spacecraft status and sensor data record (SDR) data quality monitoring for numerous operational environmental satellites, with the following key features.

• Currently monitoring 40+ satellite instruments with more than 6000 parameters online
(https://www.star.nesdis.noaa.gov/icvs/index.php)
• Used to evaluate instrument performance and Level 1b radiance/SDR product quality for SNPP and NOAA-20 ATMS, VIIRS, OMPS and CrIS, GOES ABI, COSMIC2/TGRS, AMSU, AVHRR and other satellite instruments
• Inter-satellite calibration of radiances (https://www.star.nesdis.noaa.gov/icvs/GSICS.php and https://www.star.nesdis.noaa.gov/icvs-beta/ )
• Serves as a web-based dashboard for satellite instrument status
Currently, the system performs as a static website. Users need to go to the ICVS link and manually find whatever they would be interested among over 40+ satellite sensors and 6000 monitored parameters, which is not convenience for users. It is being proposed to develop an AI-based voice-controlled ICVS LTM system to support NOAA's Joint Polar Satellite System (JPSS) missions. This allows users to find existing monitoring results (plots/images) within the ICVS by speaking out in English the name of interested satellite/sensor/parameters. The interim project is to initialize this AI-based Voice Control Frame Development with concept demonstration.


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NATIONAL WEATHER SERVICE


Examining Individual versus Collective ENSO Forecasting

Host office/program/lab
NCEP Climate Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Meteorology, Climate, Environmental Science

Project Description
The intern would help me analyze anonymized predictions for ENSO gathered since 2016. In particular we would analyze the forecast statistics provided for individual forecasters and compare them against the team average. They would learn some forecast skill scores like Ranked probability skill scores and logarithmic skill score. The results from this project would inform ENSO forecast procedures and other team-based forecasting products. Having some basic programming experience is desired.

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Evaluation of NOAA weather and air quality model performance around wildfires

Host office/program/lab
NCEP Environmental Modeling Center

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science, Computer Science

Project Description
The intern would work in the NWS/NCEP Environmental Modeling Center to evaluate Unified Forecast System (UFS) models including the Global Forecast System (GFS) and Rapid Refresh Forecast System (RRFS) sensible weather and planetary boundary layer performance in and downwind of areas impacted by wild fires and wild fire smoke. The intern would also analyze the National Air Quality Forecast Capability offline and online air quality model behaviors and biases in areas affected by wildfires.

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Wave modeling and validation

Host office/program/lab
NCEP Environmental Modeling Center

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Oceanographer, Coastal Scientist

Project Description
"development of Wave model validation script.
Data preprocessing"

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Evaluation and validation of JEDI software for NCEP’s next-generation Unified Data Assimilation System

Host office/program/lab
NCEP Environmental Modeling Center

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Atmospheric Science/Meteorology

Project Description
Data assimilation (DA) is the process of combining observations and previous short-term forecasts to produce initial conditions for improved subsequent model forecasts. The Joint Effort for Data assimilation Integration (JEDI) project is developing the next-generation DA system for use throughout NOAA and other groups in the US. In order for the JEDI software to be accepted for use by the National Centers for Environmental Prediction (NCEP), the various components must each be independently and cumulatively evaluated and verified compared to the current operational software. The intern would work with DA scientists at the Environmental Modeling Center (EMC) on developing and using tools to compute statistics, visualize and evaluate results comparing output from the new JEDI system and the current operational Gridpoint Statistical Interpolation (GSI) system.

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Tentative: Assessment of Arctic sea ice edge location forecasts using model, satellites, and ice charts to better inform polar mariners.

Host office/program/lab
NCEP Ocean Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Oceanography, Meteorology/Atmospheric Sciences, Computer Science, Cryospheric Studies

Project Description
Tentative: NOAA has expertise in model developments and the use of satellite observations to inform mariners about the likely presence of sea ice in the polar regions. This internship aims to evaluate results from predictive models, satellite observations, and expert analysts with information on the location of the Arctic sea ice edge. Earth’s changing sea ice cover is increasing the presence of thinner, less consolidated ice that is more dynamic, and thus resulting in greater variability in the ice edge position. Using its predictive capability, analysts leverage NOAA models to produce two Arctic sea ice edge forecasts: one for the Alaskan coast (by the Alaska Sea Ice Program); and one for the entire northern hemisphere (by the National Ice Center). In addition, the National Ice Center produces analyzed ice charts that delineate the ice cover using satellite observations and human expertise. In parallel, NOAA’s research groups work on automated approaches to identify the sea ice edge location using satellite observations and state-of-the-art algorithms. The goal of this internship is to assess 1) the two sea ice edge forecasts against ice charts, and 2) the automated approaches against ice charts. This internship project is in line with the International Ice Charting Working Group (IICWG) 2022 priority to make recommendations to the world’s ice services on how to characterize uncertainties in the delineation of the sea ice cover. As part of this project, the interns will gain some fundamental understanding of the polar climate system, learn how to work with Earth-observing satellite data and to carry out their thorough evaluations. The interns will also learn how to use GIS software (e.g. ArcMap and QGIS) and open source programming with Python to carry out statistical analysis, which will introduce them to a large interdisciplinary community fostering their scientific growth.

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Machine Learning Spanish Translations for the NWS (data analysis & risk communication)

Host office/program/lab
Office of Central Processing

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
data science, risk communication, Spanish, statistics, atmospheric science

Project Description
Details are still being worked out at this moment, however this project will entail NWS/CP collaboration with NWS/STI and OAR. Student will be exposed to working with Machine Learning/Artificial Intelligence (ML/AI) processes in addition to cloud computing, statistical analysis of survey question responses, data quality control work, production of data analysis spreadsheets and graphs. If student is minimally bilingual (knowledge of Spanish days of the week/month required) - then provide assistance with minor task work of matching English-to-Spanish NWS products into translation database spreadsheet. In addition, the student will learn about the challenges with effective risk communication via a Non-English language and associated policies through collaboration with social scientists, statisticians and bilingual meteorologists within the NWS. This project is the first of it's kind to apply ML/AI for language translation and messaging in the NWS, come along for this exciting ride!

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Develop new software for AWIPS, providing forecaster solutions for enhancements and/or defects

Host office/program/lab
Office of Central Processing

What academic level is most appropriate for your project? Undergraduate

Preferred major for intern
Computer Science and/or Meteorology/Earth Sciences

Project Description
The Advanced Weather Interactive Processing System (AWIPS) software is used by operational forecasters at all National Weather Service Forecast Offices, River Forecast Centers, and National Centers such as the National Hurricane Center, Storm Prediction Center, and the Weather Prediction Center. The Office of Central Processing provides software updates to AWIPS ranging from small software fixes to new large applications requiring multiple years of development. The Lapenta Intern selected for this position will learn how to develop within the AWIPS architecture and develop software solutions that will benefit the operational forecasters throughout the National Weather Service.


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Exploring the Predictability of Severe Weather Outbreaks at Lead Times of 6-14 Days

Host office/program/lab
Storm Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Meteorology/Atmospheric Science

Project Description
The NOAA/NWS Storm Prediction Center (SPC) is seeking a motivated student who is interested in extended-range severe weather predictability. The SPC currently issues severe weather outlooks for Days 4-8 while the NOAA/NWS Climate Prediction Center has experimented with Week 2 severe weather products. This project will explore the predictability of severe weather outbreaks using data from operational models to help inform the feasibility of issuing skillful severe weather outlooks at lead times of 6-14 days. The student will also have occasional opportunities to shadow SPC forecasters on operational shifts.

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Improving NOAA/NWS Storm Prediction Center (SPC) Fire Weather Forecast and Verification Techniques

Host office/program/lab
Storm Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Meteorology/Atmospheric Science

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.

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Verification of NOAA/NWS Storm Prediction Center Calibrated Severe Hazard Guidance and Severe Timing Guidance

Host office/program/lab
Storm Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Meteorology/Atmospheric Science

Project Description
The NOAA/NWS Storm Prediction Center (SPC) is seeking a motivated student who is interested in severe weather and forecast verification. The SPC currently produces calibrated probabilistic guidance for severe weather hazards (tornado, hail, and wind), as well as Severe Timing Guidance that blends SPC Convective Outlooks with this calibrated hazard guidance to add temporal resolution to the severe weather threat on Day 1. Verification of these guidance products is a key component to improving the algorithms, leading to better forecasts. The student will also have occasional opportunities to shadow SPC forecasters on severe weather operational shifts.

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Verification of WPC Winter Storm Severity Index

Host office/program/lab
Weather Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science

Project Description
Assess performance of the Winter Storm Severity Index (WSSI) over the past year using Local Storm Reports (LSR) and NOHRSC Snow Analysis. The goal of this project is to compare the impact forecast from the WSSI to observed impact from LSR and snow analysis data. Results will be used to help guide improvement to underlying WSSI algorithms.


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Analyze displacements and other errors in the Weather Prediction Center Excessive Rainfall Outlook using a variety of flash flood observations and proxies.

Host office/program/lab
Weather Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Meteorology or similar

Project Description
This project will analyze object-based biases in the Weather Prediction Center's Excessive Rainfall Outlook (ERO) using Practically Perfect as a verification. Objects will be identified using the Method for Object-Based Diagnostic Evaluation (MODE). Previous projects have assessed displacement biases by comparing the ERO to practically perfect objects. This project will expand upon previous results by computing statistics for all objects rather than just looking at matched objects between model and observation. In addition, multiple versions of practically perfect that rely on different flash flood observations and proxies will be verified to determine if there is any regional sensitivity to different types of observations.


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Develop preliminary metrics for verification of the Weather Prediction Center's Day 3-7 Hazards forecasts.

Host office/program/lab
Weather Prediction Center

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Meteorology or Atmospheric Sciences

Project Description
The Weather Prediction Center (WPC) issues each weekday a forecast of expected weather hazards in the medium range (Day 3-7) time frame. These include areas that may experience heavy rainfall, heavy snow, high winds and excessive heat/extreme cold, among other hazards. Currently, no verification of the various hazards exist, therefore it is difficult to assess the quality of the forecasts or determine methods to improve the product. This project will work to determine the 'ground truth' for several hazards, then begin collecting associated observational data and make initial comparisons to archived Hazards forecasts.


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Flash Flood and Intense Rainfall (FFaIR) Experiment: Conduct research related to FFaIR experimental products and models evaluated in the experiment and assist with daily operations of FFaIR.

Host office/program/lab
Weather Prediction Center

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Meteorology or Atmospheric Science (Prefer 1 to be a Graduate student and one undergrad)

Project Description
The FFaIR Experiment is a testbed experiment that has been run annually since 2012 and is part of the Hydrometeorology Testbed (HMT) at the Weather Prediction Center (WPC) in College Park, MD. The experiment is a 4 week long experiment, with new participants joining each week to use and evaluate new and experimental guidance and products in a pseudo-operational setting, focusing on the threat for heavy rainfall and flash flooding. The interns will help with preparation for FFaIR and participate in FFaIR. They will also help take notes on the discussions that occur during the forecasting and verification processes to help the FFaIR team with analysis of how participants use and subjectively evaluate guidance and products. In addition to this, the interns will work on one of three research projects depending on their interests: 1) Examination of instantaneous precipitation rate out of the FV3-CAMs and operational models. 2) Analysis of the Maximum Rainfall and Timing Product that is issued daily by FFaIR participants. Analysis would to some extent be open-ended but ideas include comparing the product to WPC’s Mesoscale Precipitation Discussion, forecasting tendencies for MCS and non-MCS events or forecaster methodology for issuing the product. 3) Verification methods for and utility of the FFaIR experimental Excessive Rainfall Outlook (ERO) that is defined via Average Recurrence Interval exceedances rather than Flash Flood Guidance Exceedances (which is how the operational ERO is defined).

For information on FFaIR see the 2020 FFaIR Final Report at https://www.wpc.ncep.noaa.gov/hmt/Final_Report_2020_FFaIR_Experiment_Nov13.pdf. The 2021 report will be available at https://www.wpc.ncep.noaa.gov/hmt/experimentsummaries.shtml in November 2021.

OFFICE OF OCEANIC AND ATMOSPHERIC RESEARCH (OAR)

Analysis of Tropical Atlantic velocity observations and their impact on sea surface temperature, weather and/or climate.

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
Oceanography, Climate, Math, Computer Science

Project Description
We have a suite of observations from a 25+ year array of moorings in the tropical Atlantic (named PIRATA) and several years of observations from a pilot project to measure currents at some of the PIRATA moorings (named TACOS). The intern could work on projects that are of interest to us, data analysis or model-data validation/evaluation to answer a focused question that we identify, or a project of their choosing that intersects with our interests. We also have data from ocean and atmospheric observations from annual cruises that could be analyzed.

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Evaluate HAFS output

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science

Project Description
Validate the microphysics parameterization processes in HAFS by comparing in-situ observations from hurricane environment. The student should have a basic training on running HAFS, processing both observations and model outputs, and basic coding skill in UFS community system. The expected outcomes should be publishable on academic journals with further research after summer intern and can be presented in scientific conferences.

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Examination of high-resolution analyses of tropical cyclones

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
atmospheric sciences

Project Description
AOML/Hurricane Research Division has created hundreds of high-resolution (in space and time) analyses of all available data (ground-based, satellite-based, aircraft-based) in tropical cyclones when Doppler radar data are available in the core. The project would involve synoptic and mesoscale investigations using these analyses to investigate intensity and structure changes in tropical cyclones, especially those involving the impact of shear on tropical cyclone structure, extra-tropical transition, and rapid intensification.

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Running and Analyzing outputs from our next-generation Hurricane Analysis and Forecast System

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Sciences

Project Description
HAFS is the next-generation hurricane model which uses multi-scale multiple storm-following moving-model domain nests, coupled with ocean/wave models. HAFS is being developed to provide an operational analysis and forecast system out to 7-day lead times for hurricane forecasters giving them reliable, robust and skillful guidance on tropical cyclone (TC) track, intensity, storm size, genesis, storm surge, rainfall, and tornadoes associated with TCs. During the Hurricane Season the modeling group at the Hurricane Research Division (HRD) of AOML conduct real-time experiments under the Hurricane Forecast Improvement Program (HFIP). Students may be able to participate and work with model developers and scientist in running this state-of-the-art model and analyzing results in real-time alongside scientists working in HRD's hurricane field program.

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Short project descriptionBoundary layer processes and hurricane intensity change in shear

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Atmospheric Science, Meteorology, Physical Oceanography, ocean engineering

Project Description
The goal of this project is to improve our understanding of the boundary layer processes that modulate hurricane intensity change in shear. Aircraft and saildrone observations will be analyzed to document both the axisymmetric and asymmetric near-surface structure at different stages of a storm under environmental wind shear. Multiscale structures during intensifying, steady-state, and weakening stages will be compared to identify key structural differences. The role of vertical turbulent mixing and boundary layer recovery in convection development and hurricane intensity change will be studied. Hurricane model forecasts will also be analyzed to fill the gaps of observations and explore key dynamical processes associated with hurricane intensity change in shear.

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Data Analysis of Aircraft and Surface Chemical and Meteorological Observations to investigate Urban Emissions of Greenhouse Gases and Short-lived Air Pollutants

Host office/program/lab
Air Resources Laboratory

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
Atmospheric science, environmental science

Project Description
In this project, the intern student will investigate urban emissions of greenhouse gas and short-lived air pollutants using the data collected on an aircraft and several surface monitoring stations in the Baltimore-Washington area. If it will be an in-person intern, the student will have the opportunity to be involved in the data collection on an aircraft and surface monitoring stations to get hands-on experience. Results from this project will provide policy-relevant scientific information for the policy makers.


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Molecular-based marine microbiome observations on the interactions of coastal microbial water quality with climate change, sea level rise, and land-based sources of pollution

Host office/program/lab
Atlantic Oceanographic & Meteorological Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Marine Biology, Microbiology, Biology, Ecology, Genetics/Genomics, or Climate Sciences

Project Description

This 2022 Internship Opportunity is being hosted through the NOAA Office of Oceanic and Atmospheric Research at the Molecular and Environmental Microbiology Program of the NOAA Atlantic Oceanographic and Meteorological Laboratory located in Miami, Florida. We are seeking one graduate student intern (Masters or Doctoral student) who is interesting in learning a combination marine microbiology and ‘Omics-based molecular skills such environmental DNA extraction, quantitative PCR for specific gene detection, bacterial community DNA sequencing, and bioinformatics analysis of ‘Omics sequencing data for a project investigating microbial impacts from land-based sources of pollution on coastal ecosystems in South Florida, and how these LBSP impacts may be influenced by weather events, climate change, and sea level rise.

Water Quality is critical for healthy marine ecosystems, healthy coastal communities, and a healthy economy. Coastal water quality can be highly impacted both directly and indirectly by land-based sources of pollution (LBSP), climate change, and sea level rise. Changing patterns of land-use, urban development, state of sanitary infrastructure, rainfall, storm events, coastal tidal flooding, and other drivers of LBSP that are impacted by climate change and sea level rise can have profound influence in the patterns and characteristics of the transport and discharge of land-based pollutants, including microbial contaminants, to the coastal zone. The NOAA Atlantic Oceanographic and Meteorological Laboratory (NOAA-AOML) in Miami, Florida, conducts a wide variety of research utilizing both traditional culture-base microbiology and ‘Omics-based molecular methods (including gene-specific quantitative PCR, community DNA sequencing, and metagenomic bioinformatic analysis of marine microbiome community structures) to investigate the community structure and ecosystem function of microbiomes in a wide variety of marine ecosystems, and to provide environmental intelligence to marine resource managers to help detect, track, and mitigate LBSP-associated microbial contaminants and pathogenic microbes in these marine ecosystems, and to better understand how aspects of climate change and sea level rise affects these. The Molecular and Environmental Microbiology Program at NOAA-AOML is offering an internship opportunity in FY 2022 for one Lapenta Internship graduate scholar (either a Masters or Doctoral student) to join our team working on a specific set of inter-connected projects using qPCR based microbial source tracking assays and 16S amplicon bacterial community sequencing to track LBSP-associated host-specific fecal indicator bacteria and pathogens and to characterize microbiome communities of discharge and receiving waters for the South Florida Biscayne Bay watershed and coastal inlet contributing areas. With the guidance of his/her mentors at AOML, the intern will develop a specific personal project internal to this broader effort to measure host-specific fecal bacteria indicators and document microbiome community structure at different contributing sites within this watershed and in the Biscayne Bay receiving waters. The intern will learn methods associated with microbial water quality assessment, including basic bacteriology methods of viable fecal indicator enumeration, extraction and purification of environmental DNA from marine and aquatic samples, qPCR-based molecular microbial source tracking, community amplicon DNA sequencing of bacterial 16S rRNA genes from eDNA samples, computer bioinformatics analysis of sequencing data, and the interpretation and synthesis of molecular and culture data generated from the project. The intern will analyze results in relation to the other data and metadata generated by other researchers in the larger water quality research effort. This will include the investigation of potential relationships of culture and molecular results with parameters such as nutrient concentrations, physical measurements (temp, salinity, dissolved oxygen, pH, etc.), and incidence of other special driver events (such as previous rainfall, storms or storm surges, intentional canal discharges, sewage/septic leaks, coastal tidal flooding, etc.). The intern will prepare a final report and/or presentation based upon the results of his/her directed research.

This training opportunity will require in-person laboratory work by the intern at the AOML facility in Miami Florida. The intern will be trained at in all required aspects and laboratory methods for their project, and will be supervised by the primary mentor, Dr Christopher Sinigalliano (federal PI of the AOML Molecular and Environmental Microbiology Program), and by a cadre of additional mentors at AOML (Dr. Maribeth Gidley, Cooperative Institute PI of the AOML Oceans and Human Health Initiative, and Dr Stephanie Rosales, Cooperative Institute bioinformatician at AOML). It is highly desirable that the prospective intern have some familiarity and confidence with statistical analysis using the R software package or application. It is also useful if the intern has some experience with either marine biology, microbiology, ecology, genetics, genomics, and/or climate science disciplines.

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Testing autonomous eDNA collection in an urban coral reef environment

Host office/program/lab
Atlantic Oceanographic and Meteorological Laboratory

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Marine Science or Biology

Project Description
The intern will assist in planning and preparing eDNA collection devices (Subsurface Automated Samplers for eDNA - SASe) for deployment in the field, with deployment at nearshore locations around Miami. Upon retrieval, the applicant will learn and help to process collected samples in the lab at AOML, which will include performing DNA extractions, PCR reactions, and gel electrophoresis. They will document the deployment steps and downstream eDNA processing methods. The intern will communicate and collaborate with a team of scientists at AOML.

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Improving air-sea surface heat fluxes using concurrent shipboard measurements

Host office/program/lab
Atlantic Oceanographic and Meteorological Laboratory, Physical Oceanography Division

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
math, physics, oceanography, atmosphere

Project Description
The atmosphere and ocean interact at their interface through surface heat and momentum fluxes. These fluxes play an important role in the energy and water cycles of the coupled climate system. Knowledge of heat fluxes is important to understand the ocean and global heat budget and to better predict climate and weather. A number of flux products are available, but with considerable disagreement among them. Uncertainties in these products are in part due to the errors in the surface parameters (temperature, humidity, and winds) because of the limited available observations in the open ocean. Started in 2020, concurrent measurements of surface oceanic and meteorological data have been collected in the North Atlantic Ocean as part of the XBT (eXpendable BathyThermograph) project. The objective of this project is to estimate the air-sea turbulent heat fluxes using these concurrent measurements and to evaluate the existing flux products. Results from this project will improve our understanding of the contribution of uncertainties in the air-sea fluxes to the imbalance in the global energy budget. In addition, those flux estimates can also help to improve both atmospheric and ocean models (including forecast models) by providing better boundary conditions, which in turn, will improve long-term climate change assessment and weather forecast.

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Advance Climate Literacy and Empowerment Initiatives

Host office/program/lab
Climate Program Office

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
Communications/Social Science/Climate/Education

Project Description
Given the urgency of mitigating greenhouse gas emissions and enhancing resilience, a whole-of-society strategy is needed in addition to the Biden Administration’s whole-of-government approach to accelerate the nation’s climate response and advance the Administration’s climate, jobs, and justice goals. The Climate Literacy and Empowerment Initiative needs support to identify and coordinate federal efforts towards enhancing student and community climate learning, supporting job training and skills development for the green economy, and increasing engagement in democratic processes that result in the development and implementation of the nation's climate priorities.

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Influence of sea ice variability on the jet stream and surface climate

Host office/program/lab
Chemical Sciences Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric science, meteorology

Project Description
This research project, hosted at NOAA Chemical Sciences Laboratory, will analyze climate model simulations and reanalysis data to examine how Arctic and Antarctic sea ice variability affects large-scale extratropical weather patterns on seasonal to interannual timescales. A possible focus is examining whether Antarctic sea ice in early austral winter can be used to predict changes in the strength of the polar vortex and jet stream in spring and summer, which influences surface climate including wildfire weather in Australia. This project will allow the intern to learn statistical techniques for large datasets and basics of computer programming using Python. The ultimate goal is to improve subseasonal to seasonal forecasts.


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Atmospheric trace gases and aerosols: Measurement techniques, field and laboratory studies, and analysis of data from recent field intensives

Host office/program/lab
Chemical Sciences Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Chemistry, Physics, Earth or Environmental Sciences, Engineering

Project Description
The Tropospheric Chemistry program of the NOAA Chemical Sciences Laboratory (CSL) conducts research into the composition of the lower atmosphere with the goal of understanding emissions and chemical processes relevant to air quality and climate. This research includes the development, characterization and improvement of analytical instrumentation for measurement of trace gases and aerosols. It also includes the use of these instruments to understand relevant chemical processes in a laboratory environment, and the deployment of these instruments as part of field studies. Finally, it includes analysis of data from recent field studies, including the use of chemical process and dispersion models. Students who join this program for summer internships may be involved in any of the above activities. In the event that the opportunity must be held virtually in 2022, the focus will be on data analysis.

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Air Quality and Climate Communication: Creative Communication of Scientific Information for Policy-Makers and the Public

Host office/program/lab
Chemical Sciences Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric or environmental science with communications / journalistic writing experience OR Science communications/journalism with environmental science experience

Project Description
In today’s society, it is more important than ever to communicate scientific information in a way that is both engaging and accessible to a variety of non-expert audiences. This is particularly true for scientific fields with a high degree of direct impact on public health and livelihood. Two such societally-relevant fields that are receiving increasing global attention are air quality and climate. NOAA’s Chemical Sciences Laboratory (CSL) works to improve scientific understanding of air quality, climate, and their interconnections through cutting-edge laboratory and field research throughout the troposphere and stratosphere. This project will focus on developing and creating effective and engaging communications products about CSL’s research projects and scientific findings for both public audiences and policy-makers. Such products may include written web articles, short videos, data visualizations, web content, and/or multimedia digital stories that may be disseminated through a variety of news, social media, and education channels. Specific products and projects will be decided upon at the time of the internship through discussion with the project mentors based on current research activities in the lab and with consideration to the intern’s skillset and interests.

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Identifying Aggregated Eco-Provinces in GFDL’s COBALT Marine Ecosystem Model

Host office/program/lab
Geophysical Fluid Dynamics Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Data Science or Computational Marine Science

Project Description
"The division of ocean ecosystems into broad biogeographic biomes, or provinces, is essential to understanding how marine communities are distributed globally. Province identification can also reveal how marine ecosystems are liable to change in the future, and highlight the most vulnerable ones. In the past, marine ecosystem province delineations have either been very coarse (3 global biomes, i.e., Banse 1991), or defined more subjectively, based on a few key variables and the expert judgement (Longhurst 1995). Sonnewald et al. (2020) presented an objective, machine learning-based method to define global aggregated eco-provinces (AEPs) using model outputs from a highly complex marine ecosystem model (DARWIN) coupled to an ocean state estimate model (ECCO). The AEPs highlight key ecologically driven delineations and reveal similarities between ocean basins, and are presently in use to aid New Zealand’s Marine Protected area legislation. However, the AEPs are only available for the modern day ocean covered by DARWIN, and not available to assess climate change scenarios.

This project aims to identify the AEPs from Sonnewald et al. (2020) in the Geophysical Fluid Dynamics Laboratory’s Earth System Model 4 (ESM4). ESM4 is a model equipped for running climate change simulations; results from ESM4 have already been incorporated into the 2021 IPCC 6th Assessment Report. The COBALT ecosystem model in ESM4, with just 7 plankton types, is simpler than ECCO-DARWIN, which has 51 plankton types, allowing for long timescale climate simulations. Additionally, COBALT has been successfully used for Earth System predictions for fisheries, representing a substantial advance in the utility of Earth System Models in a new direction (Park et al. 2019). However, it is unclear whether the AEPs from ECCO-DARWIN will map onto ESM4-COBALT. Therefore, as a first step, we propose to explore a mapping between the AEPs from ECCO-DARWIN onto ESM4-COBALT. Ensuring interpretability, the initial exploration will be rooted in statistics, moving to neural networks (NNs) if appropriate. Rooted in the interpretable initial exploration, the source of predictive skill in the NN can be assessed. For this we will use Additive Feature Attribution methods (AFAs) to verify the neural networks and add transparency and interpretability to a typically “black box” procedure.

The importance of this work to NOAA would be the objective identification of features and mechanisms driving marine ecosystem variability and change in critical systems such as US Marine Protected Areas, along with other marine ecosystems globally. We are seeking applicants with skills in computational data science and interest or experience with marine ecosystem science."

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Heat uptake and transport in the Southern Ocean at high-resolution and its projected changes under increased greenhouse gas emissions

Host office/program/lab
Geophysical Fluid Dynamics Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Earth Science / Oceanography / Physics

Project Description
The Southern Ocean (SO) accounts for the majority of anthropogenic heat uptake over the last century. However, there is a large disagreement among coarse resolution model simulations on the amount and spatial distribution of heat uptake in this region. Much of this disagreement likely stems from the varying ability of coarse resolution models to represent important processes that govern ocean heat uptake and its redistribution within the ocean. Constraining the amount of heat gain in the SO is important for improving projections of sea surface temperatures, sea level rise, and ocean carbon storage. We seek an intern to quantify patterns of ocean heat uptake, storage, and transport by ocean circulation in the SO in historical (1850 to 2014) and 21st century (2014 to 2100) projections performed in a hierarchy of coupled climate models recently developed at GFDL, with ocean horizontal grid spacings ranging from 0.50° to 0.125°.


"Heat uptake and transport in the Southern Ocean at high-resolution and its projected changes under increased greenhouse gas emissions:

Between 1971 to 2018, the ocean has taken up >90% of the excess heat trapped on the planet from global warming. Despite occupying only ~25% of the global ocean area, the global ocean south of 30°S, known as the Southern Ocean (SO), accounts for the overwhelming majority of the oceanic heat uptake over the last century. By absorbing this excess heat, the SO is limiting the amount of heat humans “feel” as the climate warms.The ability of the SO to absorb excess heat is intimately tied to its unique and complex ocean circulation, where water from the world’s ocean basins converge and the strongest winds on the planet pull water from the abyss to the ocean surface.

There is a large disagreement among coarse resolution (≥ 1° ocean horizontal grid spacing) model simulations on the amount of and spatial distribution of heat uptake within the SO. Much of this disagreement stems from the varying ability of coarse resolution models to represent key ocean processes that influence oceanic heat uptake and redistribution in the SO. This disagreement amongst models in their patterns of SO heat uptake contributes to uncertainty in model projections for the 21st century. Constraining the amount of heat gain in the SO is important for improving projections of global and regional surface air temperatures, sea level rise, and ocean carbon storage.

The intern will work directly with project mentors to quantify patterns of ocean heat uptake, storage, and transport by ocean circulation in the SO in historical (1850 to 2014) and 21st century (2014 to 2100) projections performed in a hierarchy of coupled climate models recently developed at GFDL, with ocean horizontal grid spacings ranging from 0.50° to 0.125°. The project is highly adaptable and will be guided by the intern’s specific interests, skills, and motivation. Some targeted questions may include: How is heat entering the ocean surface from the atmosphere? In which vertical layers and ocean basins is heat stored? How does ocean circulation redistribute heat within the ocean? How do these change as the climate warms? How does this impact global and regional sea level?

The intern will use existing python modules to analyze GFDL model output and produce figures and statistics to address questions surrounding SO heat uptake and storage in climate simulations. Potential candidates should have an interest in oceanography, climate change, physics, and/or math. Experience in computer programming will be advantageous."

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Investigate the relationship between Great Lakes or Arctic ice and atmospheric teleconnection patterns from seasonal to decadal time scales

Host office/program/lab
Great Lakes Environmental Research Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Arctic or Great Lakes ice cover and teleconnections

Project Description
Establish statistical relationship between lake/sea ice and teleconnection patterns, and build regression models to hindcast lake/sea ice using external forcings.

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Rapid detection of cyanobacteria harmful algal blooms (cyanoHABs) in the Great Lakes with emerging technologies such as airborne and uncrewed aircraft systems.

Host office/program/lab
NOAA GLERL

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Physical Sciences or Engineering

Project Description
The Great Lakes experience episodic cyanobacteria harmful algal blooms (cyanoHABs) that are currently monitored by on-water sampling, moored instrumentation, space-borne satellite, and airborne systems that are both crewed and uncrewed. The focus will be on coordinating with the airborne observations team on software development, mission planning, field deployment, and retrieval of aircraft and UAS hyperspectral camera data collection systems during the cyanoHAB growing season. Newly developed phytoplankton community composition algorithms to separate out groups of phytoplankton, using hyperspectral imagery from the crewed and uncrewed systems, will also be explored as a final product of the internship. Imagery is currently used to inform drinking water intake managers when a cyanoHAB bloom is close to their intake systems and additional phytoplankton types are of interest for water treatment facilities.

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Development of scale-aware boundary layer turbulence schemes for use in operational models.

Host office/program/lab
Global Systems Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science

Project Description
This project focuses on improving the representation of turbulence at all scales for any weather regime. Specific short-term emphasis is on improving the turbulence related to clouds as well as subgrid-scale (SGS) clouds produced by turbulence. High-fidelity coupling to other model components is a must, so this research extends to the proper representation of SGS precipitation processes as well as the coupling of the SGS clouds to radiation. This work is applicable to both weather and climate applications.

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Web interface for dictionary of Common Community Physics Package (CCPP) Standard Names

Host office/program/lab
Global Systems Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Computer science or any other major (e.g., meteorology) if candidate has a strong background/interest in CS

Project Description
In the earth sciences, we often use "standard names" to identify physical quantities and to standardize what is meant by various quantities. A well-known set of standard names is the Unidata CF (Climate/Forecasting) convention, which is used, for example, in international climate change reports. In this project, the student will familiarize themselves with the dictionary of standard names used with the Common Community Physics Package (CCPP), which enables collaboration in the development of physical parameterizations between NOAA and the external community. The student will collaborate closely with atmospheric and computer scientists from NOAA and the National Center for Atmospheric Research (NCAR) to create a web interface for displaying and querying the CCPP Standard Names dictionary.

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Evaluate and verify high-resolution forecasts of vertical profiles using rawinsonde data to assess the ability of the SRW App to accurately depict different meteorological scenarios

Host office/program/lab
Global Systems Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Meteorology/atmospheric science

Project Description
The progression toward the eventual RRFS implementation requires coordinated development across several, interconnected areas spanning the dynamic core, data assimilation, and chosen physics suite. Integral throughout this process is careful objective and subjective diagnostic analysis of forecast output in the form of case studies and metrics. To this end, the goal of this project is to compare legacy systems to future configurations of the unified system with the hopes of retiring frozen applications while at the same time testing and evaluating the chosen physics suite for the future RRFS implementation as incremental changes are made throughout the project.

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GeoFLuid Object Workbench (GeoFLOW) Application and Development: Using high order numerics to model atmospheric gas dynamics in regional and whole-earth models on large-scale CPU and GPU computing architectures

Host office/program/lab
Global Systems Laboratory

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship)

Preferred major for intern
Computer science, physics or math

Project Description
Intern will learn how to configure and run the GeoFLOW framework on a variety of parallel (and serial) systems on 2D and 3D grids, and in the process, gain a working understanding of the mechanics of job submission and output retrieval and data management on parallel computing platforms. They will be responsible for a number of software updates and modifications to the GeoFLOW system, and will thereby enhance their programming skills in C++ and C as well as gain practical experience in a managed, dynamic collaborative software development and testing environment. Candidate will gain familiarity with parallel performance characterization and optimization by learning and examining the ideas of strong/weak scaling, parallel efficiency, and computational intensity as these may be applied to GeoFLOW. In the course of this work, the candidate will also become at least acquainted with modern parallel programming techniques such as MPI, OpenMP, CUDA, and/or OpenACC. In addition to these "hard" software engineering skills, candidate may also participate in analysis of fluid systems by examining numerical data phenomenologically and/or theoretically to produce results that might be suitable for publication. The balance between time spent on software engineering tasks and time devoted to examining fluid physics will depend on the interests and experience of the candidate, as well as on the needs of the project at the time of the internship.

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SPN1 Instrument Calibration--help develop code and methodology

Host office/program/lab
Global Monitoring Lab

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Atmospheric Science, Earth Science, Environmental Science.

Project Description
The SPN1 Pyranometer is an instrument used to make precision solar radiation measurements. Some preliminary test data has been collected using a shaded/unshaded method to calibrate individual SPN1 sensors without removing the shade pattern. Code will need to be developed along with a method to pick the shaded and unshaded times from the output data and apply a calibration. More complex characterizations of azimuthal angle dependence of the sensors may also be necessary. While some data has been collected, more data will likely need to be taken to fully test this code. The potential mentee will have the opportunity to work with mentors to gain experience in the field setting up an instrument and ingesting the data in the office. Intermediate experience in R/Python is required, but there will be training opportunities to work with mentors to improve programming skills.

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Acquisition, processing, analysis, visualization, and stewardship of Ocean Exploration data

Host office/program/lab
Ocean Exploration & Research

What academic level is most appropriate for your project?
Graduate (MS or PhD), Undergraduate (at least sophomore status at time of application)

Preferred major for intern
Math, physics, data science, oceanography, marine science, computer science and engineering. Other STEM majors relevant to NOAA's mission will be considered.


Project Description
More than 95% of the world's oceans remain unexplored. With limited exploration resources, it is critical that Ocean Exploration is focused on the areas with highest discovery potential. The selected intern will assist with and help shape the office’s data visualization and GIS products using NOAA Ocean Exploration's data including seafloor mapping and ROV data. We are looking for a Lapenta intern with experience in data analysis, visualization, and coding who would contribute to the ongoing projects in the office to develop ocean exploration assets prioritization. In particular, the selected intern will be involved with the project to develop a GIS portal to help inform future exploration priorities. This project will require the intern to learn about the details of seagoing operations, equipment and technologies used in the ocean exploration, become familiar with various types of data that are collected by Ocean exploration vessels and will enable the student to learn about mapping and ROV data analysis workflows. The GIS portal will combine existing data layers, along with the thematic layers consisting of US policy priorities across various different disciplines. The intern will work closely with the NOAA Ocean Exploration staff to develop an automated / semi-automated method to infer Ocean Exploration priorities that can be then matched closely with the capabilities of available ocean exploration assets.

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Winds of change: comparing Doppler lidar wind observations with balloon-borne methods in field deployments

Host office/program/lab
National Severe Storms Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science or related

Project Description
The National Severe Storm Laboratory (NSSL) supports the deployment of many mobile platforms to collect observations regularly as part of its mission. In recent years, NSSL has begun to focus more specifically on boundary-layer profiling, and using Doppler lidars to observe low-level winds––initially in far-field conditions and now in more near-storm environments. These deployments beg the question: how similar are Doppler lidar wind observations to those historically collected by balloon-borne radiosondes? It is not clear that they are or should be similar at all given different principles of operations. To answer this question and guide future interpretation of Doppler lidar observed wind data, this study proposes to use Doppler lidar retrieved horizontal winds alongside radiosonde winds from colocated balloon releases to present each platform’s perspective of low-level winds in calm conditions and in the vicinity of convective storms. Since radiosondes are commonly used for low-level environment characterization and their results well documented, this work serves the purpose of introducing the Doppler lidar platform and establishing the reliability of its observations for the meso-to-storm-scale community accustomed to applying radiosonde data to research problems. Additionally, this comparison allows us to explore differences in the physical representativeness of each platform.

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Analyzing field observations of hurricanes and/or the Arctic

Host office/program/lab
Pacific Marine Environmental Laboratory

What academic level is most appropriate for your project?
Undergraduate (completed at least sophomore year at time of internship), Graduate (MS or PhD)

Preferred major for intern
STEM


Project Description
The interns will analyze data from different observing platforms (saildrones, seagliders, aircraft dropsondes, and others) taken from NOAA field programs during the 2021 hurricane season and past years in the Arctic, use them to explore air-sea interaction and to validate numerical model output. Through the research, the interns are expected to learn how unscrewed observing systems work, how the ocean and atmosphere interact with each other, and how well or poorly numerical models can reproduce the observations. The model validation part is directly connected to NWS. The hurricane observations of saildrones are partially supported by OMAO.

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Observations by uncrewed robotic systems: data analysis and model validations

Host office/program/lab
Pacific Marine Environmental Lab

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
STEM

Project Description
PMEL has deployed various unscrewed robotic observing systems (e.g., saildrones, underwater gliders and floats) in many regions of the global ocean, including the Arctic and tropics. In 2021, our saildrones took observations inside major (Category 4) hurricane Sam and other tropical storms. In many cases, our saildrone observations were made in coordination with observations from other types of robotic observing systems (gliders, aircraft dropsondes and floats). In this project, these observations will be analyzed and compare against output from numerical model forecasts and simulations.

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physics, math, meteorology, climate science, physical oceanography


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Uncrewed Surface Vehicles (USV) and the NOAA/NCEP modified Coupled Forecast System v2: Model “observations” to guide future tropical Pacific field campaigns and USV observations to guide model improvement

Host office/program/lab
NOAA/OAR/PMEL

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
physics, math, meteorology, climate science, physical oceanography

Project Description
This study has two objectives; the student may choose which objective to pursue. The first objective of this study is to use the NOAA/NCEP modified Coupled Forecast System v2 model to guide a future tropical Pacific field campaign involving Uncrewed Surface Vehicles (USV) and enhancements to the Tropical Pacific Observing System moorings. The second objective would be to use historic Saildrone USV air-sea interaction observations to assess the CFS model. Previous analyses identified a potential bias in clearsky solar radiation in the model which may provide a starting point for this study.

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Impacts of storm characteristics on uncertainty in quantitative precipitation estimates (QPEs) in complex terrain

Host office/program/lab
Physical Sciences Laboratory

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric Science and/or Hydrology

Project Description
Quantitative precipitation estimates (QPEs) represent our best estimates of precipitation for a given area, and can be produced using either remotely sensed or in-situ observations, or a blend of observations from multiple platforms. Hourly QPEs are highly uncertain in the complex terrain of the western US. Additionally, meteorological conditions have been shown to impact the performance of operational QPEs in this region. Quantifying and understanding the uncertainty of QPE products, as well as conditions under which individual products may be more or less reliable is of interest to the National Weather Service, and many stakeholders with interests in understanding just how certain the supposed “ground truth” observations really are. This project seeks to identify storm characteristics that may impact the performance of hourly QPE products in complex terrain. QPEs will be evaluated against gauge measurements for various precipitation events. Events or regions where the QPE products compare poorly to the gauge observations will be examined for common meteorological characteristics, which can include the presence or absence of a bright band, wind direction (both in general and relative to terrain), or precipitation type, among others.

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Data StoryTelling: Sharing the results of Social Science Hurricane Projects to an external audience

Host office/program/lab
Weather Program Office

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Interdisciplinary degrees; Social Sciences; Environmental Science, Atmospheric Science

Project Description
The "Weather Act,” as well as the 2018 “Disaster Supplemental Appropriations” provided NOAA with a unique and important opportunity to integrate the social, behavioral and economic sciences into NOAA’s tropical products, information, and services, and incorporate risk communication research into the design and communication of its products. The Weather Program Office worked side by side with the National Weather Service to fund 4 projects to reach these goals. In particular, these projects are helping NOAA better understand their emergency management, broadcast meteorologist, and public needs as it relates to product or service gaps, understanding of probability communication, website design, and changing risk perceptions. Our Lapenta intern will help us take the findings of these projects and create a story map to share the results with an external audience. The intern should bring the social science research to life through dynamic and compelling storytelling to move an audience to engage more deeply with the content. This may include using approaches such as infographics and/or online digital tools (e.g., ArcGIS, StoryMap, Miro, Flourish and others). Experience with social science data may help, but is not required. With mentors from the Weather Program Office Social Science and Disaster Supplemental Programs, as well as from the NWS, our intern will sharpen their online communication skills and increase their understanding of the societal impacts within a tropical domain.

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Policy and communications project with the Earth Prediction Innovation Center (EPIC)

Host office/program/lab
Weather Program Office/EPIC

What academic level is most appropriate for your project?
Undergraduate (at least sophomore status at time of application)

Preferred major for intern
communications, policy, government, political science, or other social science fields

Project Description
The EPIC program has several policy, management, and communications related projects available to a Lapenta summer intern interested in communications, policy, government, political science, or other social science fields. Potential projects include assisting in the planning and execution of the Annual EPIC Community Workshop and integrating student opportunities into the workshop; leading the analysis, synthesis, writing, and publication of the community workshop report; and assisting in secretariat support of the Community Modeling Board and/or the NOAA Modeling Board. Communications-related projects include the design and development of an internal-NOAA central repository on Google sites and developing videos and other communications materials to increase the engagement of students in community modeling activities involving EPIC and the UFS. The summer project will be tailored to the students interests and skills.

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Cloud-ready UFS and JEDI Graduate Student Tests development

Host office/program/lab
Weather Program Office

What academic level is most appropriate for your project?
Graduate (MS or PhD)

Preferred major for intern
Atmospheric, Oceanic, Computer Science and other science and engineering fields


Project Description
"The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth system modeling with multiple applications such as Medium Range Weather (MRW), which targets predictive time scales 1-14 days, Short Range Weather (SRW), and others that span local to global domains and predictive time scales. The suite of the UFS consists of pre-processor and initial conditions, forecast model (Finite-Volume Cubed-Sphere (FV3) dynamical core), post-processor (the Unified Post Processor (UPP)), workflow and build system.

Joint Center for Satellite Data Assimilation (JCSDA) released the first open-source version of the Joint Efforts for Data assimilation Integration (JEDI) system (JEDI-FV3 1.0.0) in October 2020. The UFS Marine Renalysis (1979-2019) data set,a global sea ice ocean coupled reanalysis product, will be released soon based on the UFS model prototype version-6 and the Next Generation Global Ocean Data Assimilation System (NG-GODAS) release of the JEDI Sea Ice Ocean Coupled Assimilation (SOCA).

The EPIC program would like to invite applications from students in atmospheric, ocean, computer sciences or other science and engineering fields to work on projects focusing on one or more aspects of the Unified Forecast System and its integration with JEDI. Potential projects include developing and testing new UFS Graduate Student Tests (with or without integration with JEDI) in a multi-cloud environment, conceptualizing metadata for UFS reanalysis and reforecast products, running a fully cycled NG-GODAS marine JEDI-SOCA DA system on the cloud and developing prototypes of post-processing tools that can be integrated with verification, validation, and visualization packages, as well as evaluating and developing documentation using innovative approaches such as Jupiter notebooks integrated with UFS Continuous Integration and Continuous Delivery pipelines.