Polar science enabled by JAWS Automatic Weather Station data
Presenting Author: Charlie Zender
Organization: University of California, Irvine
Co-Author(s): Wenshan Wang, Matthew Laffin, Ajay Saini
Automated Weather Station networks are the primary source of surface-level meteorological data in remote polar regions. Researchers receive these data in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. The "Justified AWS" (JAWS) project harmonizes L2 data from six (and counting) polar AWS networks totaling 378 stations and 3600 station-years of data from Greenland and Antarctica into a common, extensible, CF-compliant netCDF format with value-added data (e.g., solar zenith angle) and tilt-adjusted fluxes. These JAWS data have resolved questions about Cloud Radiative Effects, uncovered physical biases in GCM cloud and albedo treatments, trained a Machine Learning-based algorithm to use MERRA2 analyses to detect wind-driven surface melt and assess ice shelf vulnerability to hydrofracture, and enabled discovery of foehn-induced surface melt during polar night on the Larsen C Antarctic ice shelf. We will present these and other science applications of JAWS, the first and only global polar AWS dataset.