4.3 Using Python to QA and QC Data from the ZiaMet Weather Station Network

Monday, 8 January 2018: 2:30 PM
Room 8 ABC (ACC) (Austin, Texas)
Stan Engle, New Mexico State Univ., Las Cruces, NM; and D. DuBois

The need for high-quality surface weather data is an everlasting necessity in the fields of meteorology and climatology. The New Mexico Climate Center (NMCC) works to provide such data with the ZiaMet weather station network, a National Mesonet Program participant. Annual maintenance on the ZiaMet weather stations prevent most data quality issues, however, sensor failures may still occur, requiring the NMCC staff to constantly monitor the quality of the data. To assist in such efforts, several Python-powered products have been written that automatically analyze the weather station data and report on potential issues on an hourly and daily basis. NMCC programmer and field technician, Stanley Engle, will discuss how these Python scripts analyze the ZiaMet data by comparing the data to sources including the High Resolution Rapid Refresh model output, the National Digital Forecast Database, and other independent weather station networks, producing time-series and GIS visualizations using technologies such as NumPy, Pandas, MatPlotLib and Cartopy.
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