Conditional mixture models for precipitation data quality control


Tadesse Zemicheal (Oregon State University)
Thomas Dietterich (Oregon State University)


Session: 4.2. Environment

Abstract: Rainfall is a very important weather variable, especially for agriculture. Unfortunately, rain gauges fail frequently. This paper describes a conditional mixture model for predicting the presence and amount of rain at a weather station based on measurements at nearby stations. The model is evaluated on simulated faults (blocked rain gauges) inserted into observations from the Oklahoma Mesonet. Using the negative log-likelihood as an anomaly score, we evaluate the area under the ROC and precision-recall curves for detecting these faults. The results show a very good performance.