Just Accepted
This article has been peer reviewed and accepted for publication. It is in production and has not been edited, so may differ from the final published form.
Comparison of temperature and humidity measurements from two separate weather stations at Camden Airport, Australia
Abstract
In Australia, meteorological measurements from air quality monitoring networks are an over-looked source of data for urban climatology and meteorology research and operations. The reasons for this are not clear but may include uncertainty in the quality of the observations. Here I compare over one million one-minute near surface air temperature (n=516,334) and relative humidity (n=516,717) measurements from two distinct observational stations at Camden Airport in New South Wales (NSW), Australia – the Bureau of Meteorology automatic weather station (AWS) and the NSW Department of Climate Change, Energy, the Environment and Water’s air quality monitoring station (AQMS). Annual mean bias in the AQMS temperature measurements was found to be –0.14 °C. There are seasonal and diurnal variations in temperature bias with monthly mean bias varying from –0.27 to +0.06 °C and mean hourly bias varying between –0.39 to +0.11 °C. Annual mean bias in AQMS humidity measurements was–0.37%, monthly mean bias varies from –2.21 to +1.44% and bias in mean hourly measurements varies between –2.64 to +2.66%. Temperature and humidity mean biases are both within the range of the combined instrument uncertainties. The seasonal and diurnal signal in the bias is likely due to differences in instrument siting and the different radiation shields (Stevenson and multi-plate). This analysis suggests that temperature and humidity measurements from the NSW AQMS are of high quality. The performance of the AQMS measurements matches the AWS measurements and for most circumstances the temperature and humidity measurements are comparable. Urban climatologists and me-teorologists should consider data from air quality networks in their re-search and can use this data with confidence
ES24013 Accepted 22 November 2024
© CSIRO 2024
