Along with researchers at New Mexico Tech and the University of Maryland, I will be researching how climate controls surface melt on the Greenland Ice Sheet. This work, funded by the National Science Foundation, will involve statistical analysis of climate datasets via Self-Organizing Maps (an artificial neural network-based spatial classification scheme), analysis of Passive Microwave satellite imagery, and mesoscale climate modeling using Polar WRF, developed at the Byrd Polar Research Center of the Ohio State University. UT Martin students will have the opportunity to work as summer research assistants and be involved in this important work assessing a complex, dynamic, and important region with implications for sea level rise.
I have been investigating the climate forcing of surface melting of ice shelves in West Antarctica using satellite remote sensing, artificial neural networking and energy balance modeling. I have compiled passive microwave (SSM/I) imagery from 1987-2008 and determined melt occurrence for all of West Antarctica via XPGR for this time period. This database is being used along with Self-Organizing maps (SOMs) to elucidate particular components of the energy budget that are contributing to surface melting on the ice shelves that surround the margins of West Antarctica. Specifically I have been working to figured out what mechanisms are responsible for surface melt events in West Antarctica, which includes in-stu melting, warm air advection, and adiabatic warming. Forthcoming SOM and passive microwave work will be submitted to Journal of Climate. This work was recently presented at the 2013 American Meteorological Society meeting and 2011 AGU Fall Meeting in San Francisco and some preliminary work can be found here.
Along with my Ph.D. advisor, Dr. Derrick Lampkin, I have developed a novel retrieval of surface melt magnitude on Ross Ice Shelf using coupled optical and thermal MODIS imagery. Surface melt magnitude retrieval work has been submitted to Antarctic Science for publication. Preliminary results from the surface melt magnitude retrieval study were published as part of the Proceedings of the 65th Eastern Snow Conference and can be found here.
Abstract: Satellite based assessments of melt from passive microwave systems are typically used to detect the presence of surface melt on Antarctic Ice Shelves, although they are limited in that they only provide an indication of melt occurrence and have coarse spatial resolution. We developed an algorithm to retrieve surface melt magnitude using coupled near-IR/thermal surface measurements from MODIS, calibrated by estimates of liquid water fraction (LWF) in the upper 1cm of the firn derived from a one-dimensional thermal snowmelt model (SNTHERM). SNTHERM was forced by hourly meteorological data from automatic weather station data at four reference sites spanning a range of melt conditions across the Ross Ice Shelf during a relatively intense melt season. Effective melt magnitude or LWF was derived for satellite composite periods covering the Antarctic summer months at a 4km resolution over the entire Ross Ice Shelf, and ranges from 0-0.5% LWF in early December to as much as 1% LWF during the time of peak surface melt areas in along the coast. This work represents a step forward in characterizing not only melt occurrence and duration but also melt magnitude and at a higher spatial resolution than current passive microwave melt detection methods.
Additionally I have collaborated with fellow PSU Graduate Students Seth Baum and Jacob Haqq-Misra to write a response to an editorial, entitled "Climate Change: Evidence of Human Causes and Arguments for Emissions Reduction" that is to be published in a forthcoming issue of Science and Engineering Ethics. The preprint of the article can be found here.
Fieldwork at Niwot Ridge, Colorado
I was supported for the Summer of 2008 on a NASA grant to collect surface radiometric spectral measurements on a melting snowpack in the Colorado Front Range.
Synoptic Climatology of Snowfall in the Northeastern United States
Abstract: In this study, a quantitative estimate of the amount of snowfall resulting from several different snowstorm types or tracks is presented. A modified version of Kalkstein and Corrigan’s (1986) Temporal Synoptic Index (TSI) is employed to classify synoptic weather patterns across the northeastern US from 1971-2000. First, six-hourly winter weather data from Syracuse, NY is subjected to principal components analysis. A cluster analysis is subsequently performed on the loading scores, generating a calendar of 19 synoptic weather types. These types are grouped into three primary synoptic weather patterns: Coastal Storms, Lake-Effect Patterns, and Overrunning Storms. The calendar of synoptic types is compared to a gridded snowfall dataset for the northeastern United States to determine the amount and spatial distribution of snowfall from each of the primary synoptic weather patterns. Overrunning storms are found to cause most of the snowfall in most areas; however, lake-effect snowfall predominates in areas closer to the Great Lakes and the higher elevations of the Appalachians, where as much as 50% of all snowfall is attributable to lake-effect snowfall. While coastal storms are significant snowfall producers along the eastern seaboard, they do not provide the majority of the snowfall in any portion of the study area. Two and three-day sequences of synoptic weather type are analyzed and classified according to snowstorm type. These sequences correspond to storm tracks and weather patterns described in the literature, including persistent lake-effect events, Colorado Low Pressure systems, Alberta Clippers, and events that transition from coastal storms to lake-effect patterns.
Thesis Presentation, University of Delaware, October, 2007
Poster Presentation, American Geophysical Union Joint Assembly, May, 2006
Surface Geologic Mapping, Olympic Peninsula of Washington (Research Assistantship)
Circumpolar Active Layer Monitoring, North Slope of Alaska (Research Assistantship)