| Summary: | This dataset summarizes work done to predict the presence of perennial firn aquifers (PFA) and buried lakes in and on the Greenland ice sheet using satellite-based remote sensing observations (specifically, differences in two wavelengths of passive microwave data observed by Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E)). This work was done by Ms. Margeaux Carter in partial fulfillment of a Master’s degree in Hydrology (2017) at the New Mexico Institute of Mining and Technology, under the research supervision of Dr. David B. Reusch. A copy of this thesis has been archived for personal or classroom use only. While every reasonable effort has been made to provide “production” quality files, certain aspects of files in this project tend to be more “research” (or “development”) grade. For example, while metadata on Network Common Data Form (netCDF) variables will usually be sufficient, there tends to be a lack of file history metadata. The main components of this project are (1) modeling microwave brightness temperature (Tb) of ice and snow using Microwave Emission Model of Layered Snowpacks (MEMLS) and (2) predicting occurrence of subsurface liquid melt using an algorithm known as “Polarization Difference” and AMSR-E passive microwave observations. 1. Modeling microwave emissions of ice and snow using MEMLS MEMLS (Weismann and Matzler, 1999) was used to test the feasibility of using passive low frequency microwave (LFM) satellite observations to identify subsurface water (surface lakes, aquifers) in the Greenland Ice Sheet (GIS). A model snowpack was used to test whether the characteristics (frequency, polarization) of the available LFM data could be used to identify subsurface water layers. The representative snowpack is 25 m deep and has twelve layers with thickness, temperature, and density chosen to reflect density profiles of ice cores obtained from snowpacks with perennial firn aquifers (Koenig et. al., 2014), and average winter snow temperature profiles from the Greenland Climate Network observation station at Swiss Camp (Steffen et. al., 1996). The tested range of liquid water content in the snowpack was chosen to reflect those observed by Koenig et. al. (2014). The official archive for MEMLS is at github.com/akasurak/memls_TVC. Custom files (primarily MATLAB scripts) are archived in the file memls_TVC-custom.tgz. 2. AMSR-E-based prediction Prediction of subsurface liquid water was done using AMSR-E passive microwave data at two frequencies, 6.9 and 10.7 Gigahertz (GHz), and vertical (V) and horizontal (H) polarizations. A netCDF version of the original binary files has been archived. These files were used with an algorithm called “Polarization Difference” (PD) that looks at differences between V and H polarizations at each wavelength to predict the presence of subsurface liquid water. A 10-year (2002-2011) daily resolution dataset from the PD algorithm has been archived. These data were further analyzed to classify the ice sheet into four categories: probable firn aquifer, probable buried lake, “overlap” where subsurface liquid water is likely present but type cannot be classified, and “not in range”. Details on development of the prediction algorithm may be found in the master of science (MSc) thesis. PD-based predictions were tested against a number of independent datasets. Direct verification included observations of PFAs (Forster et al., 2014) and buried surface lakes (Koenig et al., 2015). These files are not archived here, please contact those authors. Additional testing against aspects of modeled monthly meteorology was done with a subset of the Arctic System Reanalysis (ASR; Bromwich et. al., 2012). These data have been archived here after customizations for our analysis. Observations of surface melt occurrence from the “MEaSUREs Greenland Surface Melt Daily 25 kilometers (km) Equal Area Scalable Earth (EASE)-Grids 2.0, Version 1” (Mote 2014; https://nsidc.org/data/NSIDC-0533/versions/1), also a part of testing the PD algorithm, were also archived in modified format (spatially subset and spatially summed). Other In addition to observational and model datasets, we have also archived a set of NCL (National Center for Atmospheric Research (NCAR) Command Language) scripts related to file processing and model development and verification. References Bromwich, D., L. Bai, K. Hines, S. Wang, Z. Liu, H. Lin, Y. Kuo, and M. Barlage (2012), Arctic System Reanalysis (ASR) Project. https://doi.org/10.5065/D6K072B5, Nat. Cent. Atmos. Res., Comp. Inf. Sys. Lab. Boulder, Colo., Accessed 01 Mar 2016. Forster, R., J. Box, M. van den Broeke, C. Miège, E. Burgess, J. van Angelen, J. Lenaerts, L. Koenig, J. Paden, C. Lewis, S. Gogineni, C. Leuschen, and J. McConnel (2014), Extensive liquid meltwater storage in firn within the Greenland ice sheet, Nat. Geosci., 7, 95-98, doi: 10.1038/NGEO2043. Koenig, L., D. Lampkin, L. Montgomery, S. Hamilton, J. Turrin, C. Joseph, S. Moustafa, B. Panzer, K. Casey, J. Paden, C. Leuschen, and P. Gogineni (2015), Wintertime storage of water in buried supraglacial lakes across the Greenland ice sheet, The Cryosphere, 9, 1333-1342, doi: 10.5194/tc-9-1333-2015. Mote, T. L. 2014. MEaSUREs Greenland Surface Melt Daily 25km EASE-Grid 2.0, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/MEASURES/CRYOSPHERE/nsidc-0533.001..
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