Title: The JAWS Workflow to harmonize polar Automatic Weather Station data
Presenting Author: Charles S. Zender
Organization: University of California, Irvine
Co-Author(s): Ajay Saini, Wenshan Wang

Abstract:
Automated Weather Station and AWS-like networks are the primary source of surface-level meteorological data in remote polar regions. These networks have developed organically and independently, and deliver data to researchers in idiosyncratic ASCII formats that hinder automated processing and intercomparison among networks. Moreover, station tilt causes significant biases in polar AWS measurements of radiation and wind direction. Researchers, network operators, and data centers will benefit from AWS-like data in a common format, amenable to automated analysis, and adjusted for known biases. Our project addresses these needs by developing a scientific software workflow called "Justified AWS" (JAWS) to ingest Level 2 (L2) data in the multiple formats now distributed, harmonize it into a common format, and deliver value-added Level 3 (L3) output suitable for distribution by the network operator, analysis by the researcher, and curation by the data center.