Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third-party automatic weather stations in Australia

Third-party automatic weather stations (TPAWS) provide a compelling data source for scientists and practitioners to observe and estimate more accurate fine-scale atmospheric conditions, including daily maximum and minimum temperature (denoted as Tmax and Tmin, respectively), than the current primary weather observation network can offer. Several uncertainties and errors arise in data from TPAWS as the quality control applied to these stations may be inadequate or ad hoc. In this study, we develop a statistical approach to evaluate the quality of daily Tmax and Tmin observations collected from TPAWS in Australia. Our approach compares a target observation with multiple types of reliable reference data, including neighbouring primary weather observations from the official Bureau of Meteorology of Australia stations, Australian Gridded Climate Data, and numerical weather prediction data. Guided by the operational requirements in terms of automation, interpretability, and simplicity as well as expandability, a separate test is formed for each type of reference data and then all the individual tests are merged to generate a single result based on a Gaussian mixture model that is used to provide the final overall assessment for each TPAWS observation. The overall assessment is made in the form of a p-value-based confidence score that measures the difference between the target observation and trusted reference data. Our method is validated by synthetic datasets based on high-quality observations and is also applied to daily Tmax and Tmin observations from 184 TPAWS owned by the Department of Primary Industries and Regional Development of Western Australia. The framework can be readily applied to different regions with different reliable or trusted data sources. We present a statistical method assessing daily Tmax and Tmin data from third-party automatic weather stations (TPAWS). Our approach employs p-value-based confidence scores, detecting disparities between TPAWS observations and trusted references. The figure depicts 2019 daily Tmax data from a TPAWS, highlighting eight potentially erroneous readings (labelled in red; Section 5 elaborates). Some anomalies may not be immediately evident in time series trends alone, but comparing them with surrounding reference observations highlights the significance of our method's contribution. image