Climate and Space Sciences and Engineering 2018 SURE and SROP Research Projects

CLASP Project #1: Sensors for Space Weather Observations
Faculty Mentor: Mark Moldwin,
Project Details: Help develop, build, test and deploy a low-cost research quality space weather observatory sensor suite for ground-based observations of aurora signatures and geomagnetic storms. The sensor suite will be all-weather (ability to operate in polar to equatorial climates), robust (able to operate autonomously for at least one-year) and provide high-quality magnetometer and GPS data. In addition to the engineering work, the project can include data analysis and programming of a web-based analysis tool to monitoring the deployed stations.

CLASP Project #2: Statistically Comparing How Well Model Predict the Aurora
Faculty Mentor:  Aaron Ridley,
Project Details: The northern (and southern) lights (i.e., the aurora) add a lot of energy to our atmosphere and cause it to swell, increasing drag on low-Earth orbiting satellites. There are a lot of models that predict the strength and location of the aurora, as well as electric and magnetic fields that are associated with the aurora. This project is aimed at determining how well these models perform.  We will conduct a statistical comparison between satellite data and the model results using thousands of simulations hosted on NASA’s websites. The results will then be explored to determine if there are times when the models do better or worse, since there are things such as seasonal dependence of the aurora. This is a big data project applied to the aurora and models.

CLASP Project #3: Effect of soil moisture and soil freezing on Lake Erie harmful algal blooms
Faculty Mentor: Allison Steiner,
Project Details: The long-term sustainability of coastal ecosystems is influenced by anthropogenic activity, and in particular, climate change. This research project is a component of a broader NSF project designed to answer the question: How does climate influence the biophysical dynamics of freshwater ecosystems and ecosystem services, and how can scientist and stakeholder co-production of information enhance coastal decision-making and sustainability? Specifically, this summer research project will focus on using satellite-derived soil moisture and freeze/thaw products to understand winter soil moisture conditions that may influence runoff and the simulations of phosphorous loading. Work on the project will include the analysis of data from the Soil Moisture Active/Passive (SMAP) satellite in conjunction with simulations from a watershed-based model that calculates annual phosphorous loading.

CLASP Project #4: Analysis of Recent MAVEN Spacecraft Solar Occultation Measurements
Faculty Mentor: Stephen Bougher,
Perquisites:  Matlab proficiency
Project Details: The summer 2018 project will be focused analyzing new and unanticipated solar occultation measurements of the Mars thermosphere made by the Extreme Ultraviolet (EUV) Monitor (EUVM) onboard the NASA Mars Atmosphere and Volatile Evolution (MAVEN) orbiter. Recently, it has been shown that the EUVM 17-22 nm channel can be used to accurately measure CO2 density from approximately 120 km to 200 km. These measurements are made during each orbit that includes an eclipse segment, which is approximately 50% of all orbits between October 2014 and September 2017. Because the retrieved density measurements are inherently constrained to either the dawn or dusk terminators, they are ideally suited for tracking variability due to external forcing (i.e. seasonal and solar cycle), as well as identifying latitudinal variability. Specifically, the following four drivers of atmospheric variability will be considered in detail: global circulation, dust storms, solar EUV forcing and atmospheric waves. Each of these four drivers can be characterized by comparing EUVM measurements of thermospheric temperature and CO2 density with predictions made by the state-of-the-art (physics-based) Michigan Mars Global Ionosphere-Thermosphere Model (M-GITM). The overarching goal of this project is to improve predictions of atmospheric drag in the Mars upper atmosphere.

This is a data analysis project that will make heavy use of Matlab and IDL sorting and plotting routines. The student should be proficient in Matlab and willing to learn and apply IDL routines to this EUVM database and M-GITM outputs. The complex physics of the Mars upper atmosphere will be learned along the way.