Project:	Simulating the TIR Radiance Spectra of Volcanic SO2 Plumes (JPL AO No. 16246)
Disciplines:	Computer Science, Artificial Intelligence
Mentor:	Vincent Realmuto, (JPL), Vincent.J.Realmuto@jpl.nasa.gov, Phone: (818) 354-1824
Background:	The Thermal Infrared (TIR) component of the Surface Biology and Geology (SBG) Mission is scheduled for launch in 2029. SBG-TIR will collected radiance measurements in eight spectral channels across a ground swath of 1060 km, at a spatial resolution of 60 m (over the land surface). The SBG-TIR orbit will provide 3-day repeat coverage at the equator, with more frequent observations at higher latitudes. Principal objectives of the SBG-TIR Mission include monitoring volcanic sulfur dioxide (SO2) emissions at time scales of days to weeks and documenting active eruptions at time scales of hours to days. These objectives are challenging given the high volume of data anticipated from SBG-TIR, and we are investigating machine learning (ML) approaches to map concentrations of SO2. Training the ML will require the simulation of plume spectra for a wide variety of SO2 concentrations, profiles of atmospheric temperature and humidity, and surface elevations, temperatures, and emissivities.
Description:	The candidate's primary role will be to design, implement, and execute batch procedures to simulate SBG-TIR radiance spectra based on global databases of atmospheric and surface properties. These properties, together with SO2 concentrations, plume altitudes, and satellite view angle, will be input to a radiative transfer (RT) model to generate the radiance spectra. The candidate will not be responsible for developing the (legacy) RT model. The atmospheric profile data will be acquired from the NASA Global Modeling and Assimilation Office (GMAO), and some collation/sorting of these data may be required. The emissivity and topography data will be derived from existing global data bases. If time allows, the candidate will also participate in the training of the ML procedure.
References:	For background on the use of TIR radiance to estimate SO2 concentration: Realmuto, V.J., and A. Berk (2016), Plume Tracker: interactive mapping of volcanic sulfur dioxide emissions with high-performance radiative transfer modeling, J. Volcanol. Geotherm. Res., 327, 55-69, doi:10.1016/j.jvolgeores.2016.07.001.
Student Requirements:	Candidate should be proficient in Python or similar high-level scripting language. Python is the preferred programming language for the SBG Project. Familiarity with machine learning concepts and toolkits (e.g., RStudio) would be beneficial.
Location / Safety:	Project building and/or room locations: . Student will need special safety training: .
Programs:	This AO can be done under the following programs:   Program    Available To        SURF@JPL    Caltech students only  Click on a program name for program info and application requirements.