WOW: Quantifying Bias and Uncertainty in Citizen Weather Data.

The sheer volume of citizen weather data collected and uploaded to online data hubs, such as WOW-NL, is immense. However as with any citizen data it is difficult to assess the accuracy of the measurements.

Speaker: Dr. Simon Bell, University of Birmingham.

This talk begins by discussing what citizen data is available, as well the numerous sources of error and uncertainty in the data. This is supported by the results of a thorough intercomparison field study, in which popular models of automatic citizen weather stations were put to the test.

It explains how key sources of bias can be parameterised, and introduces a quality control system through which citizen air temperature observations can be passed. This system adopts a Bayesian approach, capable of learning and updating its estimates of the calibration and radiation-induced biases inherent to each station. By attaching an uncertainty estimate to each observation, the system also provides a measure for real world applications to base their confidence in the data. Although further work is required to bring it up to an operational setting, such a system will hopefully allow applications to leverage the additional value citizen weather data can bring to longstanding professional observing networks.

Bio:

During the year that preceded his PhD Simon studied Applied Meteorology and Climatology at the University of Birmingham where he was taught the art of making accurate and reliable meteorological observations - only to begin his PhD at Aston University and find nearly every rule in the book broken in the world of citizen observing. As such it took him the next 3 years to develop an approach to help quality control this vast source of data.
Simon now indulges his passion for web development using an 'Internet of Things' approach to sensing air and road surface temperatures, having returned to the University of Birmingham.