Automated Bayesian quality control of streaming rain gauge data

► There is a significant need for automated quality control in real-time rain gauge data streams. ► This study develops a real-time method for identifying measurement errors in rain gauge data streams. ► The error detector uses online machine learning to address non-stationarity in the model parameters. ► The false alarm rate and false negative rate of the error detector was calculated to be 0.90% and 1.5%, respectively.