Disciplines:

Atmospheric Science

Computer Science

Mentor:

Jonathan Jiang (329J)

Jonathan.H.Jiang@jpl.nasa.gov

818-354-7135

Mentor URL:

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

Background:

A forward radiative transfer model and retrieval system (FMRS) has been developed by Jiang et al. (2017) and the FMRS system has been applied to simulate simultaneous retrievals of cloud ice particle size, ice water content, water vapor content, and temperature based on simulated radiances from Tropospheric Water and cloud ICE (TWICE) CubeSat instrument. Since then, we have been working on developing a prototype of a combined microwave/radar system, focusing on passive microwave measurements at 118, 183, 240, 310, 380, 670 and 883 GHz GHz channels together with active radar measurements at 13, 35, and 94 GHz.

Description:

This project is to help develop and to test this innovative simulation and retrieval system that can be used for both passive microwave radiometer (100-1000 GHz) and active radar (10-100GHz) satellite instruments to simulate measurements and demonstrate the instrument capabilities, which will effectively help us to prepare future Earth Venture mission proposals.

References:

Jiang, J.H., et al., A Simulation of Ice Cloud Particle Size, Humidity and Temperature Measurements from the TWICE CubeSat, Earth and Space Science, 4, doi:10.1002/ 2017EA000296, 2017.

Student Requirements:

Strong physics and math background and programming skills. Knowledge of radiative transfer and atmospheric science in general.

Location:

UCLA Campus or JPL 183-715

Program:

JIFRESSE Summer Internship Program

Disciplines: 

Carbon Science

Water cycle

Mentor: 

Junjie Liu (329G)

Junjie.Liu@jpl.nasa.gov

818-354-0059

Mentor URL: 

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

Background: 

Liu et al. (2017) have used OCO-2 to understand the impact of climate on carbon cycle, and Fu has worked with JPL scientists to show that carbon cycle can have significant impact on seasonality of the water cycle over Amazonia (Wright et al. 2017).  Liu and Fu are working together to understand how does vegetation (carbon cycle) carry drought memory from one year to the next using satellite measurements of HDO and solar induced fluorescence (SIF).

Description: 

This project aims to test the vegetation influences on atmospheric moisture availability for rainy season onset over both Amazonia and Congo, to support the proposed EVS and explore future satellite mission concepts.

References: 

Liu, J., Bowman, K. W., Schimel, D. S., Parazoo, N. C., Jiang, Z., Lee, M.,…Eldering, A.(2017). Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. Science, 358, eaam5690. https://doi.org/10.1126/science.aam5690

Wright, Jonathon S., et al. "Rainforest-initiated wet season onset over the southern Amazon." Proceedings of the National Academy of Sciences (2017): 201621516.

Student Requirements: 

Strong physics and math background and programming skills. Knowledge and experience in analyzing water isotope and ecosystem  

Location: 

UCLA Campus and/or JPL 183-715

Program: 

JIFRESSE Summer Internship Program

Disciplines: 

Atmospheric physics, atmosphere dynamics, microwave remote sensing

Mentor(s): 

UCLA: David Neelin (PhD advisor)

JPL: F. Joseph Turk (334, x4-0315), Manuel de la Torre Juarez (329, x4-4548), Chi Ao (335, x3-6640

Mentor URL: 

http://people.atmos.ucla.edu/neelin/

Background: 

Dr. Neelin’s research has focused on the complex interaction between the large-scale tropical atmosphere and moist convection.

Description: 

The internship will advance their familiarity and use of GNSS RO observational data to advance the understanding tropical precipitation processes and associated theoretical models.

References: 

Student Requirements: 

The student will be one of Dr. David Neelin’s incoming (Fall 2018) PhD students, who will have a background in atmospheric physics and/or atmosphere dynamics, but not necessarily with remote sensing data analysis. 

Location: 

UCLA Dept of Atmospheric Sciences, and JPL (joint time spent at both locations)

Program: 

JIFRESSE Summer Internship Program

Disciplines: 

Computer Science

Mentor: 

Noppasin Niamsuwan (334H)

Noppasin.Niamsuwan@jpl.nasa.gov

818-354-1093

Mentor URL: 

Background: 

NASA Earth Observing System Simulator Suite (NEOS3) is a software suite for simulating satellite observation systems. The software supports the simulation of measurements collected by various instruments including radars, radiometers, and lidars, while accounting for the contribution from the Earth's atmosphere and surface on the received signal. NEOS3 is highly configurable which facilitates the design of a new observation strategy for a future Earth Science mission.

Description: 

This project is to help developing the software for adaptive scheduling of simulation requests and optimizing the number of program executions by utilizing Rules Engine or similar techniques.   The ultimate goal is to improve the overall computation efficiency of instrument simulation in order to meet increasing demand for forward simulation in OSSE. At the end of the project, the student is expected to deliver a software prototype, which will be further developed and integrated into NEOS3 production environment. The same prototype and its products will also serve as a key component for our next AIST proposal.

References: 

https://esto.nasa.gov/forum/estf2014/presentations/A6P3_Niamsuwan.pdf

Simone Tanelli et al., "Integrated instrument simulator suites for Earth science", Proc. SPIE 8529, Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions IV, 85290D (8 November 2012); doi: 10.1117/12.977577

Student Requirements: 

Strong programming skills. Knowledge of radiative transfer and atmospheric science in general are desired. Experience on Rules Engine is a plus.

Location: 

JPL

Program: 

JIFRESSE Summer Internship Program

Disciplines: 

Geography, ecology, biology, computational sciences

Mentor: 

Naiara Pinto

naiara.pinto@jpl.nasa.gov

818-354-9515

Mentor URL: 

https://landscape.jpl.nasa.gov/

https://scholar.google.com/citations?user=w_CVKLAAAAAJ&hl=en&oi=ao

Background: 

In tropical biomes, pasture and agriculture abandonment lead to a patchwork of secondary forest stands. Land use history can have strong implications for forests' recovery to mature state, shaping their role as carbon sinks as well as their ability to support wildlife. This research project aims to understand forest recovery trajectories by quantifying forest structure and composition as a function of forest age.

Description: 

This research project will leverage a rich dataset including airborne radar, unmanned lidar, and ground plots in Costa Rica. The student will work under the supervision of JPL/JIFRESSE researchers under a collaboration with Univ. Florida.

The task described here will focus on chronosequnces, or ground plots with ages ranging from 20 to > 100 years. The student will employ Geographical Information Systems to integrate ground data and remote sensing observations, and use statistical packages to quantify relationships. The questions to be addressed are (1) what are the changes in tree size distribution and composition associated with a tropical forest chronosequence? and (2) how do structural and composition parameters relate to radar and lidar metrics? By integrating ground and remote sensing observations, we seek to uncover large-scale patterns to help us understand the role of land use history on forest succession.

References: 

Chazdon R, Broadbent EN, et al. 2016. Carbon mitigation potential of Neotropical secondary forests. Science Advances

Student Requirements: 

Required skills: GIS programming (R, IDL or Python)

Desired skills: background in ecology, previous experience with remote sensing

Location: 

UCLA campus, with occasional meetings at the JPL campus in Pasadena

Program: 

JIFRESSE Summer Internship Program

Disciplines: 

Remote Sensing, Geophysics, Computer Science

Mentor: 

Zhen Liu (329)

Zhen.Liu@jpl.nasa.gov

818-393-7506

Mentor URL: 

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

Background: 

Space geodetic measurements from Interferometric Synthetic Aperture Radar (InSAR) and Global Position System (GPS) provide capability to image surface deformation at various spatiotemporal scales. Modern satellite SAR sensors such as Sentinel-1A, TerraSAR-X, ALOS-2 etc. now enable imaging time-varying deformation processes with finer spatial resolution at much shorter temporal interval. The growing observations and data volume also call for advances in techniques for rapid and reliable data processing, noise correction, and improved time series analysis.

Description: 

The student will develop or improve state-of-the-art techniques for noise correction and transient detection using InSAR and GPS, and incorporate them in the improved time series analysis for geophysical applications.

References: 

Liu, Z., H. S. Jung, Z. Lu, 2014, Joint correction of ionosphere noise and orbital error in L-band SAR interferometry of interseismic deformation in southern California, IEEE TGRS, 52(6), 3421-3427, doi:10.1109/TGRS.2013.2272791.

Student Requirements: 

Proficiency in python/matlab/C/C++/linux. Backgrounds in math, computer and signal processing. Geophysics/Satellite InSAR background is preferred.

Location: 

UCLA campus and/or JPL

Program: 

JIFRESSE Summer Internship Program