Disciplines: 

Solid Earth science / Hydrology / Geodesy

Mentor: 

Donald F. Argus

Donald.F.Argus@jpl.nasa.gov        (818) 216 3983

Dennis Lettenmaier (Geography/UCLA)

Mentor URL: 

www.scienceandtechnology.jpl.nasa.gov/people/d_argus

Background: 

GPS is emerging as a 2nd effective technique to

estimate changes in total water at Earth's surface.

Donald Argus and David Wiese are integrating GPS and GRACE to determine a mass change at Earth's surface at a spatial resolution of 100 km, better than the 300 km resolution of GRACE alone.

Description: 

We will track changes in total water at Earth's surface

during and immediately after atmospheric rivers and hurricanes.  We and Dennis Lettenmaier (UCLA) will investigate whether different hydrology models of the water/atmosphere cycle have a greater capacity to store water in the ground, either as groundwater or as deep soil moisture.  We and J. T. Reager (JPL) will break down changes in total water into components such as soil moisture, snow, evapotranspiration, precipitation, and runoff to better understand the water cycle.

References: 

Argus, D. F., F. W. Landerer, D. N. Wiese, H. R. Martens, Y. Fu, J. S. Famiglietti, B. F. Thomas, T. Farr, A. W. Moore, M. M. Watkins (2017), Sustained water loss in California's mountain ranges during severe drought from 2012 to 2015 inferred from GPS, J. Geophys. Res. Solid Earth 122, doi:10.1002.2017JB014424.

Student Requirements: 

Strong physics / math / hydrology / communication skills desired.

Location: 

JPL Building 238 6th floor and UCLA

Disciplines: 

Atmospheric Remote Sensing, Weather and Climate

Mentor: 

Evan Fishbein (329),

Evan.Fishbein@jpl.nasa.gov

818-354-2250

UCLA Co-mentors: Prof. Kuo-Nan Liou, Dr. Yu Gu

Mentor URL: 

https://science.jpl.nasa.gov/people/Fishbein

Background: 

NASA is planning to combine program of record and new observations to improve understanding of Clouds, Convection and Precipitation (CCP). This study supports ongoing activities at JPL to improve characterizing the hydrologic cycle near cumulus clouds using hyperspectral thermal infrared spectroradiometers.

Description: 

This research continues an activity to characterize the uncertainty of scattering radiative transfer calculations near clouds in regions of the thermal infrared spectrum used to measure water vapor and temperature. The student will calculate and study a small set of high-accuracy scattering radiative transfer spectra using the UPCART algorithm. The focus is on absorption for multiply-scattered radiation and the importance of geometric thickness on radiative transfer calculation accuracy.

References: 

Aumann, H. H., et al. (2018), Evaluation of Radiative Transfer Models with Clouds, J.Geophys. Res Atmos., 123(11), 6142-6157, doi:10.1029/2017jd028063.

Liou, K.N. (2002),An Introduction to Atmospheric Radiation, Elsevier

Natraj, V., et al (2010), On the use of principal component analysis to speed up radiative transfer calculations, J. Quan Spec & Radiative Trans, 111(5), 810-816.

Student Requirements: 

Introductory knowledge of radiative transfer. Desire some familiarity with thermal infrared remote sensing, clouds and scattering

Location: 

UCLA and/or JPL

Disciplines: 

Atmospheric science, physics, engineering, or computer science

Mentor: 

Name (Section), Email and Phone

JPL Mentor: Jonathan H. Jiang (329J), Jonathan.H.Jiang@JPL.NASA.GOV, 818-207-8734

UCLA Co-Mentor: David Paige, dap@epss.ucla.edu, 310-825-4268

Mentor URL: 

https://science.jpl.nasa.gov/people/JJiang 

https://epss.ucla.edu/people/faculty/572/

Background: 

NASA is gearing up to set up a permanent lunar presence on the surface of the Moon (https://www.nasa.gov/specials/artemis) in mid-2020s through the Artemis program. The plan for the lunar base includes to explore the science and technology of building an Earth observatory on the Moon to provide a stable, serviceable, long-term, global, continuous full spectral view of the Earth from the UV to IR. This will enable a range of measurements from which the trends in the atmosphere, lithosphere, cryosphere, hydrosphere, and biosphere can be analyzed, and important sciences can be addressed, this include tracking climate variability, air pollution sources and transport, natural hazards (e.g., extreme weather, volcanic plumes, hurricanes, lightning), seasonal and secular variations in polar ice and vegetation. 

Description: 

Objective: To study and design full Earth-view hyperspectral observations from the Moon that go beyond what is currently available from Earth orbiting and geostationary satellites. We will use Machine Learning (ML) to support trade studies for a hyperspectral instrument design that use the Moon as a platform for Earth observations. A lunar-based Earth observatory will allow for global, continuous, full-spectrum views of Earth to address a range of Earth science questions, as well as provide instrument synergy among multiple satellites for calibration and science.

Approach:

1. We will first make use of the Earth observations from DSCOVR to determine the information content in its individual wavelength bands. The DSCOVR spacecraft has been positioned at the Sun-Earth L1 point for 5 years since June 2015, and has been producing full-disk, high spatial resolution multi-spectral images of the Earth every 68-110 minutes. These images provide rich information from which trends in the atmosphere, lithosphere, cryosphere, hydrosphere, and biosphere can be studied. Using ML, we will identify and extract information of Earth’s “hot spots”, or elevated features and extremes (e.g. volcanic eruption, wild fire, heat wave, extreme rainfall, hurricane and typhoon, and other anthropogenic activity, with high temporal frequency). Once these hot spots are identified we extract their most informative spectral range for future Deep Space Gateway (DSG) instruments expected for the Moon station.
2. We will work with JPL engineers to select existing instruments or, if required, propose new instrument designs in collaboration with the instrument engineers at Division 38. In particular, a hyperspectral sensor ranging from the ultra violet to the thermal infrared (like the current MLS, OMI, TES, and AIRS) coupled with the near-constant limb profiles of Earth will be analyzed.

Innovation: This project will develop ML capabilities that can be applied across different fields to address the detection and monitoring of extremes for Earth and other planets and support traid studies of future instruments. Our tasks have potential application to extract information from large, complex datasets and thus to aid scientific analysis and improve efficiency in hazard detection and response. From the lunar vantage point, the Earth observations present a unique opportunity to align with NASA’s Artemis lunar program.

References: 

https://iopscience.iop.org/article/10.3847/1538-3881/aac6e2

Student Requirements: 

Computer programing skill, some familiarity with Machine Learning tools.

Location: 

JPL 180-715

Disciplines: 

Earth Science, Atmospheric Science, Geography

Mentor: 

Name (Section), Email and Phone

Sassan Saatchi, 329G, saatchi@jpl.nasa.gov, 4-1666

Rong Fu (AOS/UCLA)

Mentor URL: 

http://carbon.jpl.nasa.gov

Background: 

Tropical rainforests are experiencing frequent droughts caused by severe rainfall anomaly and the temperature rise.  Over the past 20 years, these climatic events coupled with land use change and forest structural degradation have impacted the productivity of the ecosystem and influenced the exchange of water (evapotranspiration) particularly during dry season.

Description: 

The project requires students familiar with spatial climate and remote sensing data and statistics to perform several trends and anomaly analysis and spatial and temporal correlation of various climate, vegetation, and ecosystem modeling data sets.  Familiarity with statistical analysis, mathematical modeling, and Python or mathlab, R package are preferred.

References: 

Saatchi, S., Asefi-Najafabady, S., Malhi, Y., etal. (2013). Persistent effects of a severe drought on Amazonian forest canopy. Proceedings of the National Academy of Sciences, 110(2), 565-570.

Cox, P. M. (2019). Emergent constraints on climate-carbon cycle feedbacks. Current Climate Change Reports, 5(4), 275-281.

Swann, A. L., & Koven, C. D. (2017). A direct estimate of the seasonal cycle of evapotranspiration over the Amazon basin. Journal of Hydrometeorology, 18(8), 2173-2185.

Student Requirements: 

Master or PhD student

Location: 

233-305D

Disciplines: 

Atmospheric sciences, Satellite remote sensing, Radiative transfer

Mentor: 

UCLA: Jasper Kok (JIFRESSE). Email: jfkok@ucla.edu; phone: 310-825-1154

JPL: Olga Kalashnikova. Email: olga.kalashnikova@jpl.nasa.gov; phone: 818.393.0469

Mentor URL: 

https://jasperfkok.com/

https://science.jpl.nasa.gov/people/Kalashnikova/

Background: 

Understanding mineral dust emission and evolution processes is an important NASA earth science research problem, and directly aligned with an objective identified as ‘most important’ by the 2017 Decadal Survey to quantify “processes that determine the spatio-temporal structure of important air pollutants”. Recent airborne measurements have shown that coarse mineral dust (e.g. diameter ≥ 5 μm) accounts for 90-95% of total dust mass and over half of the extinction at visible wavelengths near dust source regions (Ryder et al., 2019). Recent work from Prof. Kok’s group indicates that, globally, the atmosphere contains about 15-20 Tg of coarse desert dust, which accounts for about a third of the atmosphere’s total particulate matter loading by mass, however, atmospheric models greatly underestimate its abundance by about a factor of four (Adebiyi and Kok, 2020). New advanced remote sensing capabilities are needed to understand and constrain dust size evolution from the emission throughout transport, leading to more accurate representation of dust coarse mode, and its impact on climate and air-quality.

Description: 

An undergraduate intern will work on parameterizing the optical properties of coarse dust for satellite retrieval applications. This is a complex task because dust is substantially more aspherical than previously thought (Huang et al., 2020) and cannot be handled effectively at large size parameters (i.e., large particle sizes) by the current optimization techniques proposed for the next generation of hyperspectral and polarimetric sensors. The intern will use the discrete-dipole-approximation code (ADDA; Yurkin and Hoekstra, 2011) to model ensemble-averaged optical properties for coarse dust particles with realistic shape under the guidance of the UCLA group, and test these parametrizations with JPL remote sensing retrievals.  The intern will gain experience on optical properties modelling, AERONET ground data analysis, and reading and processing MISR satellite datasets.

This work will facilitate the adaptation of coarse atmospheric dust models in aerosol retrievals of current satellite missions (MISR, MODIS), and will help to develop advance corrections for confounding effects of nonspherical coarse dust for future JPL missions (MAIA, EMIT). Our long-term vision of using advanced remote sensing for coarse atmospheric dust would support future dust-related airborne and satellite mission concepts.

References: 

Adebiyi, A.A., and Kok, J.F. (2020). Atmospheric models miss most of the warming coarse dust, Sci. Adv., in press.

Huang, Y, J. F. Kok et al. (2020). Climate models and remote sensing retrievals neglect substantial desert dust asphericity, Geophys. Res. Lett., in press.

Ryder, C.L. et al. (2019). Coarse and Giant Particles are Ubiquitous in Saharan Dust Export Regions and are Radiatively Significant over the Sahara. Atmos. Chem. Phys. 19, 15353–76.

Yurkin, M. A., and A. G. Hoekstra (2011), The discrete-dipole-approximation code ADDA: Capabilities and known limitations, J. Quant. Spectrosc. Radiat. Transf., 112(13), 2234–2247, doi:10.1016/j.jqsrt.2011.01.031.

Student Requirements: 

Undergraduate student with strong physics, math, and programming skills. Knowledge of radiative transfer and aerosol science.

Location: 

UCLA campus

Disciplines: 

Atmospheric Science, Climate Science

Mentor: 

UCLA: Gang Chen. Email: gchenpu@ucla.edu; Phone: (310)206-9956

JPL: Hui Su. Email: hui.su@jpl.nasa.gov; Phone: (818)393-7388

Mentor URL: 

www.gchenpu.com

https://science.jpl.nasa.gov/people/Su/

Background: 

The Arctic surface temperature is increasing at a faster rate than global mean temperature under global warming, known as Arctic Amplification.  Recent winters are often characterized by Arctic sea ice decline and cold midlatitude continents.  However, climate models show discrepancies in their atmospheric responses to Arctic sea loss, and the influence of Arctic Amplification on the midlatitudes remains an issue of debate.  Polar satellites provide valuable remote sensing products such as water vapor, clouds and precipitation that help constrain the climate model simulations.  The combination of models and observations will be used to understand the key physical processes in the Arctic-midlatitude interactions on intraseasonal and interannual time scales.

Description: 

The student will analyze a coordinated set of numerical model simulations under the Polar Amplification Model Intercomparison Project (PAMIP) that are designed to understand the model consistencies and discrepancies in the atmospheric response to sea ice loss.  The intraseasonal variability and Arctic sea ice-forced variability in these model simulations will be evaluated with observations including satellite remote sensing products. This project will help to identify key processes for the Arctic-midlatitude interactions and help formulation of future missions (such as an Earth-Venture mission) that observe these processes.

References: 

Screen, J. A., Deser, C., Smith, D. M., Zhang, X., Blackport, R., Kushner, P. J., et al. (2018). Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nature Geoscience, 11(3), 155–163.

Smith, D. M., Screen, J. A., Deser, C., Cohen, J., Fyfe, J. C., García-Serrano, J., et al. (2019). The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP6: investigating the causes and consequences of polar amplification. Geoscientific Model Development, 12(3), 1139–1164.

Student Requirements: 

The student should some basic knowledge in atmospheric or climate sciences.  Proficiency in python, matlab or other programming language is preferred.

Location: 

Department of Atmospheric & Oceanic Sciences, UCLA

Disciplines: 

Physical Remote Sensing

Forest Ecology

Mentor: 

Name (Section), Email and Phone

Liang Xu (329G)

alan.xu@jpl.nasa.gov

818-393-5976

Luciana Alves

lualves@ucla.edu

626-524-3408

Mentor URL: 

https://science.jpl.nasa.gov/people/LXu/

https://www.ioes.ucla.edu/person/luciana-alves/

Background: 

Terrestrial ecosystems play important roles in the global carbon cycle as a significant sink of atmospheric CO2. Accurate quantifications of biomass/carbon stored in global live vegetation and its horizontal and vertical structure are thus crucial for future projections of atmospheric CO2 content, and the effectiveness of climate mitigation efforts. In recent years, the Light Detection and Ranging (Lidar) sensors from airborne platforms and spaceborne observations such as NASA’s ICESAT-1, 2 and GEDI have collected (or are collecting) samples widely across global vegetation and provided precise forest structure and biomass with the aid of existing inventories, but the samples are not spatially continuous and lack of repeatability. On the other hand, Synthetic Aperture Radar (SAR) measurements provide imagery sensitive to vegetation structure and biomass/carbon depending on the frequency and geometry. SAR polarimetry (PolSAR), interferometry (InSAR) and Tomography (TomoSAR) measurements at very low frequencies (C-band to P-band or 6 GHz to 0.4 GHz respectively) have demonstrated the ability to map vegetation structure and biomass when trained with inventory and Lidar, but with medium spatial resolution (> 100 m) and variable precision. Lidar and SAR data together, along with the spatially fine optical imagery such as from Landsat and Sentinel, can provide the synergistic observations necessary to map structure and biomass across vegetation types and environmental conditions.

Description: 

The project will focus on data fusion of current available satellite products to estimate and map forest structure and biomass across selected research sites in different ecoregions. The selected long-term research sites should have contemporary measurements of ground inventory, airborne or spaceborne (ICESAT or GEDI) Lidar and SAR imagery from UAVSAR or ALOS PALSAR-1/2. We aim to develop a synergistic algorithm on vegetation structure and biomass retrieval that works across different ecoregions with optimized parameters learned from multi-temporal sequence and multi-sensor data fusion.

References: 

Yang, Y., Saatchi, S.S., Xu, L., Yu, Y., Choi, S., Phillips, N., Kennedy, R., Keller, M., Knyazikhin, Y., Myneni, R.B., 2018. Post-drought decline of the Amazon carbon sink. Nature Communications 9, 3172.

Xu, L., Saatchi, S.S., Meyer, V., Ferraz, A., Yang, Y., Bastin, J-F., Banks, N., Boeckx, P., Verbeeck, H., Lewis, S., Shapiro, A., 2017. Spatial Distribution of Carbon Stored in Forests of Democratic Republic of Congo. Scientific Reports 7(1), 15030.

Student Requirements: 

Basic requirements

• Must be currently enrolled in a college or university pursuing a Bachelor, Master, or PhD majoring in geography, atmospheric, environmental science or related disciplines.
• Must have a minimum cumulative 3.0 out of 4.0 GPA.

Desired requirements

• Strong analytical and coding skills
• Strong oral and written communication skills
• Related internship experience

Location: 

JPL 329G