Quality Control in Weather Monitoring with Dynamic Linear Models

Joel Janek Dabrowski, Ashfaqur Rahman, Ming Li, Quanxi Shao, Shuvo Bakar, Andrea Powell, Brent Henderson

Keywords: Kalman filter, citizen science, crowdsourced data

TL;DR: The Dynamic Linear Model with the Kalman filter is applied for quality control in third-party weather sensors.

Abstract: Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localised weather information. However, with uncertainty in the sensor and installation quality, farmers are at risk of making poor decisions based on incorrect data. We present an automated approach to perform quality control on weather sensors. Our approach uses time-series modelling and data fusion with Bayesian principles to provide predictions with uncertainty quantification. These predictions and uncertainty are used to estimate the validity of a sensor observation. We test on temperature, wind, and humidity data and achieve error hit rates above 80% and false negative rates below 11%.