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 T_max and T_min, 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 T_max and T_min 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 T_max and T_min 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.

中文翻译：

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开发一种统计方法来评估澳大利亚第三方自动气象站的每日最高和最低温度观测结果

第三方自动气象站 (TPAWS) 为科学家和从业人员提供了令人信服的数据源，以观察和估计比气象站更准确的精细尺度大气条件，包括每日最高和最低温度（分别表示为T _max和T _min）。目前主要的天气观测网络可以提供。由于应用于这些站的质量控制可能不充分或临时性，TPAWS 的数据会出现一些不确定性和错误。在本研究中，我们开发了一种统计方法来评估从澳大利亚 TPAWS 收集的每日T _max和T _min观测值的质量。我们的方法将目标观测与多种类型的可靠参考数据进行比较，包括来自澳大利亚官方气象局站点的邻近主要天气观测、澳大利亚网格气候数据和数值天气预报数据。以自动化、可解释性、简单性和可扩展性方面的操作要求为指导，为每种类型的参考数据形成单独的测试，然后将所有单独的测试合并以基于高斯混合模型生成单个结果，用于为每个 TPAWS 观测提供最终的总体评估。总体评估以基于p值的置信度得分的形式进行，用于衡量目标观察数据与可信参考数据之间的差异。我们的方法通过基于高质量观测的合成数据集进行了验证，并且还应用于西澳大利亚第一产业和区域发展部拥有的 184 个 TPAWS 的每日T _max和T _min观测。该框架可以轻松应用于具有不同可靠或可信数据源的不同区域。