Internships, Summer 2022

Following are descriptions of the GISS projects seeking intern participants during Summer 2022.

Opportunities labeled "CCRI" are part of the year-long CCRI activitity, and are seeking both an under-graduate and high school intern. All other opportunities are seeking only an undegraduate summer inter.

Note: As of Jan. 31, 2022, we have not yet received official NASA guidance regarding attendance for Summer 2022 opportunities in regards to the public health crisis. Acitivities may be on-site or may be remote (i.e., WFH).

CCRI: The Expanding Legacy of Landsat — Documenting Environmental Change Beyond Five Decades

Duty Location: NASA Goddard Space Flight Center

Project Description: The joint NASA USGS Landsat mission will turn 50 years old in July 2022. The imagery acquired by the satellite sensors from this long-term mission provide a wealth of data for understanding the Earth's land surface and near-shore waters nearly from pole to pole. With the launch of the latest Landsat 9 scheduled for late September 2021 from near where the first satellite in the series was launched from in 1972, further insights on environmental change can be assessed across space and time. Searching for powerful stories of change is the overall goal of this project including how to visualize and document such changes to ensure that the visionary thinking of the original program can be celebrated in its 50th year.

Depending on the interests of the applicant, projects can be pursued over forests, fields, aquatic, urban, and cryospheric areas. The goal will be to create visual and quantitative change assessments that can be used in educational, research, and outreach settings. Enabling people from around the globe to ‘see and understand' changes that are happening as our climate warms and ecosystems respond, will be an appropriate tribute to the continuing, long-term goals of the Landsat program.

CCRI: Connecting the Local Urban Fabric to Global Climate Change

Duty location: NASA Goddard Space Flight Center

Project Description: Urban areas are principal agents of change across our home planet. In an increasingly urbanizing biosphere, scientific understanding, and societal adaptation each require tools to accurately measure and monitor the dynamics and environmental consequences of the urban ecosystem. With over half of the world's population living in urban areas today—projected to grow to 68% by 2050—these tools, data, and scientific understanding will make significant contributions to national and international policies to ensure the sustainability of cities and settlements in the face of a changing climate. While urban areas still represent today a small proportion of Earth's land surface, urbanization can have significant impacts on hydrological cycles and microclimates of local and surrounding areas up to regional and even continental scales.

New, more detailed, and more accurate remotely-sensed data on urban areas and associated built-up surfaces can provide a foundation for a better understanding of the impacts of cities on their environment and potential improvements in the modeling of the impacts of urbanization on the energy/water/carbon cycles. The unprecedented level of spatial detail in these new data sets allows for a much improved and accurate characterization of the urban fabric (e.g., roads, buildings, open space), and their change, at a spatial scale that is directly relevant to cities and settlements and their inhabitants. This project will leverage existing and future NASA remote sensing assets to study in detail the direct connections between changes in the urban fabric and environmental changes in the Baltimore/Washington DC study area and the Chesapeake Bay Watershed. The aim is to develop, test and assess data and methodologies regionally but with potential applicability to other areas of the world. Successful applicants will work closely with the mentor and associated scientists at NASA Goddard Space Flight Center to perform work in the following suggested areas:

  • Assess quality and accuracy of the harmonized Landsat and Sentinel 2 data set for urban change monitoring in the Baltimore/Washington DC area (see
  • Develop methods and assess useability of NASA Lidar remote sensing (e.g., satellite/airborne) for urban vertical structure.
  • Assess useability of Landsat and ECOSTRESS satellite data for monitoring the urban heat island effect.
  • Use Very High Resolution commercial satellite archive at NASA for urban change detection and vertical change.
  • Perform field studies using field measurements and the GLOBE Observer mobile phone app (see to assess accuracy of data sets above. This work will involve local schools and high school students.
  • Develop maps or other cartographic products using NASA satellite data over the Baltimore/Washington DC region.
  • Work with local stakeholders to communicate science and to build capacity to use new data sets for local/regional applications.
  • Communicate findings with science community via presentations and written work.
  • Participate in NASA research proposals and publications as appropriate.

CCRI: Characterizing the Urban Land Surface Temperature via an Innovative, Multi-Platformed Suite of Satellite and Ground-Remote Sensing Technologies

Duty Location: NASA Goddard Institute for Space Studies; CUNY-City College of Technology

Project Description: In light of climate change, urban micro-climates, the urban heat island effect and other urban geophysical phenomena and processes, there is a new urgency to better study, understand, and characterize urban environments. Revolutionary and innovative ideas are being considered to transform the study of the urban landscape. Fundamental changes are taking place in geophysics and in engineering to aid in the adaptation and mitigation of the environmental challenges to which cities must respond.

For this project, students will perform a local, intensive, and comprehensive surface energy balance data collection and processing initiative that will help to characterize the urban heat island, the heat index, and more particularly the land surface temperature over various local community built and natural environments. The project aims to produce high temporal and spatial resolution land surface temperatures for the local community and for New York City using the combination of satellite remote sensing observations and ground-based measurements. Students will obtain remote sensing data from multiple polar orbiting and geostationary satellites. Additionally, students will use infrared cameras and flux tower instruments to understand how urban surfaces react to solar radiation and its consequent heat. Students will be able to monitor the incoming and outgoing radiation and heat energy components using the cameras. The differences between traditional rooftop materials and new green or white roofs will be explored. Moreover, hand held temperature measuring devices, Unmanned Aerial Systems (UAS), and observations from satellite infrared observations will be collected. Using statistical approaches and data processing, the gaps in temporal and spatial coverage appropriate for the development of a heat index (effect of air temperature + humidity) will be filled. The volume of data used in this project is expected to in the range on 5TB. The added-value of this initiative is that cross-pollination between students and the local community and the transfer of knowledge between the two groups will be created and sustained long after the project ends.

Project Activities Include:

  • Monitoring thermal characteristics of urban surfaces such as concrete, asphalt, rooftop, and vegetated surfaces at different seasons and times of the day by collecting data
  • Coordinating with community partners to receive skin temperature measurements from various surfaces in the local community.
  • Obtaining and analyzing satellite land surface temperature observations from geostationary and polar orbit satellites such as from the Geostationary Operational Environmental Satellite-R Series (GOES-R), LandSat, Ecostress, Sentinel 2A, the Moderate Resolution Imaging Spectroradiometer (MODIS), etc.
  • Analyzing the collected data to define and to develop a high spatial resolution (10 m) and high temporal resolution (every 5 min) skin temperature over the local community and over New York City using several statistical approaches by fusing satellite based and ground observations.
  • Developing an online interactive server platform to disseminate the data to the local community and to scientists. Data visualization and queries will be among important features of the proposed platform.
  • Working closely with the local community on the use of the collected data to interpret and predict the strength and extent of heat wave events.

CCRI: Climate Change in the Hudson Estuary — Past, Present & Future

Duty Location: Lamont Doherty Earth Observatory; NASA Goddard Institute for Space Studies

Project Description: The Hudson Estuary is comprised of key tidal marshes, which serve to provide many ecosystem services to the large population of this important coastal region, including NYC. These services include fish nurseries, coastal protection, water purification, paleoclimatic archives, and carbon sequestration repositories. We seek to understand the records of past droughts, cold intervals, floods, and vegetation shifts along with the past shifts in carbon storage. From this information, we can better understand our present snapshot of climate/carbon, and predict future accumulation rates as climate warms and sea level rises.

CCRI: Earth Observation Applications for Resiliency — Assessing Climate Change Impacts in Urban, Agricultural, and Natural Environments

Duty Location: NASA Goddard Institute for Space Studies

Project Description: The history of Earth observation began in the 1840s, during the era of geographical exploration, when pictures were taken from cameras secured to the tethered balloons for the purpose of topographic mapping. It took another 100 years for earth observations to evolve to a platform based in space called satellites. Remote sensing is the science of obtaining information without physically being in contact with it. This process involves detection and measurement of radiation at different wavelengths reflected or emitted from distant objects or materials, by which they may be identified and categorized.

Through various remote sensing platforms such as satellites and aircraft, supplemented by surface and subsurface measurements as well as modeling and mapping, practical information about Earth's physical, chemical, and biological systems can be obtained. We seek to help urban stakeholders, agricultural leaders, and conservationists respond to the challenges presented by a changing climate by transforming a wealth of NASA Earth observation data (e.g. Landsat, MODIS) into actionable information.

CCRI: Atmospheric Rivers in a Changing Climate

Duty Location: NASA Goddard Institute for Space Studies

Project Description: Atmospheric River events cause dramatic flooding along the western coast of the USA and populate our news headlines. These phenomena occur globally and are responsible for s~80-90% of meridional moisture fluxes in the mid-latitudes and 30-40% of meridional moisture fluxes in the Arctic. In the Arctic, moisture fluxes associated with ARs have been proposed as a means for polar amplification through latent heat fluxes as well as downwelling thermal radiation. For this project, students will use simulations from the NASA Goddard Institute for Space Studies ModelE, version 2.1 (GISS-E2.1, CMIP6) enabled with suite of tracers to diagnose the moisture source for Atmospheric River events to contrast with climatological moisture sources and amounts. Simulations will be evaluated for skill in the modern/historic period. Further simulations and analysis will then be performed with an augmented suite of simulations of both past and future climate to determine the impact of climate change on AR events.

Algorithm development for satellite retrievals of oil slicks & other substances

The retrieval of oceanic properties from satellites is a very important component of climate research. In preparation for the NASA Plankton, Aerosol, ocean Ecosystem mission (to be launched in 2023), we are developing an advanced retrieval scheme that, in addition to measurements of intensity, exploits the polarization state of the light measured by its state-of-the-art optical sensors. The retrieval scheme belongs to the class of “inverse methods”, which can be applied as solvers to the widest class of problems and have the advantage of rigorously determining the uncertainties associated with each retrieved parameter. In our case, the Python LMFIT “inversion wrapper” drives a “forward” radiative-transfer engine (written in Fortran) and will enable the retrieval of parameters descriptive of the ocean surface like its refractive index, with the primary application of detecting oil slicks or biogenic films. Through the interaction with the GISS RSP group, the intern will have the chance to be exposed to several aspects of remote sensing for climate research, from the preparation for airborne and spaceborne campaigns to their execution and subsequent data analysis.

Augmenting RSP data records with climatological variables

Airborne and satellite observations of reflected sunlight are a major source of information on aerosols, clouds and surface properties. In particular, multi-angle optical polarization measurements provide unique and accurate measurements to learn about such components of the Earth's climate system, and their interactions. Our group at GISS has long been at the forefront of this research field, especially by leveraging on measurements collected by the Research Scanning Polarimeter (RSP) airborne sensor. A crucial step for the analysis and the interpretation of these measurements is correcting the radiances measured at selected wavelengths for light absorption effects caused by gasses in the atmosphere such as ozone, methane, nitrogen dioxide, carbon dioxide and water vapor. Since some of these gases are highly variable in time and space, climatologies and/or reanalysis databases (like MERRA-2) are needed to provide accurate vertical profiles of such trace gases at the instantaneous RSP location. Furthermore, the RSP can also autonomously derive the precipitable water vapor, although this product has not been adequately validated yet. The goals of this project are to 1) develop a framework to automatically poll and download required MERRA-2 reanalysis files, 2) collocate them to measurements of the airborne RSP and the spaceborne POLDER polarimeters, 3) calculate and apply data correction, 4) compare RSP-based water vapor retrievals to those available from MERRA-2; and 5) compare drop sondes measurements of water vapor profiles from the CAMP2Ex and ACTIVATE field campaigns with RSP and MERRA-2 column integral and profiles of water vapor respectively.

Code development remote sensing of snow properties

The retrieval of snow properties and their evolution in polar regions is a very important component of climate research. We are in the process of developing a new retrieval scheme that exploits the polarization state of the light measured by satellite sensors (POLDER), in addition to measurements of intensity only (like those of MODIS). Such a retrieval scheme is composed of a “forward” radiative transfer engine (written in Fortran), driven by an “inversion” wrapper available as part of a Python package. Inverse methods can be applied as solvers to the widest class of problems and have the advantage of adding a detailed error budget estimate of the state parameters to the retrieval of their values. In this case it will enable the retrieval of parameters descriptive of the snowpack like grain shape and size, the concentration of light-absorbing impurities, but also the simultaneous determination of the properties of aerosols that might be present in the scene above the snowpack.

Exploring the dynamics of exoplanetary atmospheres & oceans with ROCKE-3D

Together we will explore details of the possible habitable states of newly discovered exoplanetary systems that the GISS ROCKE-3D group has modeled in recent years. These include Proxima Centauri b, the TRAPPIST and Kepler 1649 systems. We will compare & contrast the habitability of Earth through time with these systems. For example, the remarkable fact is that Earth has had temperate conditions for most of its history even though our Sun has increased in brightness by almost 30% in the past 4 billion years. In the same context we can look at the other terrestrial planets in our solar system modeled by ROCKE-3D (Venus & Mars) to see how their formerly habitable states can inform our thinking about these extrasolar worlds. The student will learn how to compare and contrast published ROCKE-3D NetCDF simulations using our in-house tool Panoply and how to explore and incorporate related scientific literature.

Investigating the role of dry deposition on air pollution episodes

Air pollution is very harmful to humans and is inextricably linked to climate. We are looking for a intern to investigate air pollution in a new version of the NASA GISS global climate model. This new version of the model has state-of-the-art representation of dry deposition, which is a key loss pathway of many air pollutants. Dry deposition happens when pollutants are removed by the Earth's surface, and changes with surface properties and conditions (for example, vegetation). Previous work has suggested that dry deposition may play an important role in driving poor air quality, especially during drought when vegetation is water limited. The summer intern will examine how day-to-day and year-to-year variability in air pollution (as simulated by the NASA GISS model) changes with land surface conditions like drought using the new implementation of dry deposition. Specifically, the intern will examine simulated concentrations of ozone and particulate matter in near-surface air. The intern will use programming to process large datasets generated by the model and visualize the model data in scientific figures and tables. The intern will analyze the model data to arrive at conclusions and contextualize their findings using peer-reviewed scientific literature. The intern will present their results in an oral presentation or a written paper at the end of the internship.

Machine-learning approaches to accelerate satellite retrievals of environmental parameters

The retrieval of parameters descriptive of the earth system is of obvious importance for climate science studies. In preparation for the NASA Plankton, Aerosol, ocean Ecosystem mission, we are developing an advanced retrieval scheme that exploits measurements of the polarization state of light to deliver the status of the ocean surface and the presence of possible contaminants such as oil. The retrieval scheme belongs to the class of “inverse methods”, where an “inversion wrapper” drives a “forward” radiative-transfer engine. We are interested in testing the improvement in computational speed achievable by applying neural-network trainings to look-up tables produced with the forward code. Through the interaction with the GISS RSP group, the intern will have the chance to be exposed to several aspects of remote sensing for climate research, from the preparation for airborne and spaceborne campaigns to their execution and subsequent data analysis.

Merging and analysis of multi-sensor imagery over polar regions

Advanced satellite retrievals of snow properties benefit from the synergistic exploitation of data originating from multiple sensors. For this reason, such data needs first and foremost to be co-located and merged into custom files for practicality of use when input to the retrieval algorithms. Continuing the work performed by previous interns, we will exploit available processing tools to co-locate several-years' worth of datapixels from the MODIS, POLDER, and CALIPSO sensors and run statistics of interest on pixel-based properties. Ideal candidates for this project are students with strong interdisciplinary skills, including experience with the analysis of geophysical datasets and their visualization, but also well versed in code development. High proficiency in Python is a requirement, and knowledge of system architecture concepts is considered an advantage since the batch processing of large amounts of data requires to be optimized for speed.

Ocean Color and how it affects climate and climate change

If you were asked what's the color of the ocean you would say blue. Right? Great, but you would be almost right. The color that our eyes see is the color of the sunlight reflected from the ocean after some of it is absorbed and some scattered by phytoplankton and other particles in the ocean and the atmosphere above it. NASA remote sensing measures ocean color using satellites and aircrafts and that helps scientists understand phytoplankton and how it changes. Phytoplankton has important contributions in the global carbon cycle because it takes up carbon dioxide from the atmosphere for photosynthesis while at the same time provides almost half of the oxygen we breath. NASA climate modeling simulates both the light absorption and scattering, phytoplankton photosynthesis, CO2 absorption and oxygen emissions. In this research project we aim to describe the photosynthetically active radiation (PAR) from observations and models, how PAR changes productivity and how productivity changes will feed back to PAR changes.

The Student will be able to acquaint themselves with bibliography about ocean color, remote sensing of ocean color as well as climate modeling of ocean color. More specifically, the student will

  • Survey bibliography & databases and obtain the relevant data for PAR, light absorption and scattering coefficients for different sites in the ocean from satellite and in situ measurements
  • Evaluate PAR and the light absorption and scattering in climate simulations of current climate
  • Estimate feedbacks between PAR changes and productivity

Technical skills: programming familiarity with analysis and visualization software such as R, MATLAB and/or python, as well as netcdf file format are highly required.

Majors: physics, mathematics, engineering, earth sciences

Parameterization of phytoplankton absorption for NASA/PACE retrievals

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a NASA Earth-observing satellite mission that is scheduled for launch in 2023. The NASA/PACE spacecraft will carry three state-of-the-art instruments to monitor changes in oceanic and atmospheric particulates. Two of these instruments, the Ocean Color Instrument (OCI) and the Spectro-Polarimeter for Planetary Exploration one (SPEXone) will take ultraviolet (UV) pictures of the Earth. This is the first time that NASA will make such UV pictures to study changes in the plankton population, offering new opportunities to study how our oceans are changing on a global scale, which is both exciting and challenging. A proper exploitation of the UV data collected by the NASA/PACE sensors requires models that simulate the sensitivity of the “UV color” of the ocean to the particulates suspended in the seawater. The purpose of this project is to help create such models by providing parameterizations of phytoplankton absorption spectra. To this end, we are looking for an intern who will be tasked with using statistical models to help parameterizing an existing dataset of 700+measurements for phytoplankton absorption spectra.

Pathways of Saharan Dust

Dust aerosols are soil particles lifted by strong winds. The smallest particles can travel thousands of kilometers downwind where they degrade air quality but replenish important nutrients in soil. In this project, we consider the transport of Saharan dust during the Godzilla Dust Event pf June 2020 and how this dust arrived in the southeastern US.

RSP Data Management, App Development, and Code Conversion

NASA GISS Airborne Research Scanning Polarimeter (RSP) is often flown in field deployments. It remotely collects data to measure aerosol and cloud properties. During a field deployment RSP needs to be monitored to make sure it is healthy and collecting data properly. In addition, we can do real time retrievals so our team can contribute to the discussion of interesting scenes we observe so that perhaps we can look into it deeper. Currently, the data is processed using code written in IDL (Interactive Data Language). IDL is not commonly known language and also it is not easily portable as it requires license which most people do not have. We would like to convert this code to Python. This way the code becomes more usable and shareable. Other programming codes may need to be converted to Python as well. After the flight the data is placed on GISS web site. The site needs to make it easy for others to select relevant data of their interest. It shows ground tracks and filters data given criteria of interest. It also needs to display the data (pseudo image and plots) for a quick analysis. The intern may work on the RSP website and app.

Science & Art in the Time of Coronavirus

Science & Art intern will develop an interdisciplinary education workshop for elementary school children in New York City, building on NASA GISS's Climate Change Research Initiative (CCRI). The education workshop will target Title 1 schools. It will highlight remote sensing data from NASA missions and climate change projections from NASA climate models. In this workshop, students will have the opportunity to interact with NASA scientists and draw their own connections between science and art. The Science & Art intern will be responsible for organizing the workshop's presentations, agenda, and outreach. The intern will work directly with the Science & Art Team at NASA GISS. Background Information: During the time of the coronavirus, the NASA GISS Climate Impacts Group took on an initiative to connect our climate research with artistic expression. This collaborative project includes research visualizations from the Climate Impacts Group's three pillars — agriculture, urban areas, and conservation and development. Using paint on canvas and videography, artist Kate Doyle and NASA scientists have transformed scientific research into art creations — all during the isolation period of the pandemic in New York City. The Science & Art Team will host an Exhibition to educate participants, encourage creative thinking, and share our scientific and artistic products. The Science & Art intern will be proficient in Microsoft PowerPoint and Excel. Skills working with Canva and Prezi are a plus. The ideal candidate will have experience in developing education workshops, understanding, and presenting of scientific data, and excellent verbal and written English communication skills. Requirements include the capability to accomplish tasks independently and in small groups (virtually), strong attention to detail, and effective time management. Experience with art is a plus.

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