EMS Webinar: Citizen science – blending NMS observations with crowd-sourced observations

The recording of this webinar is now available on the EMS YouTube Channel

Abstract

High-resolution weather maps are fundamental components of early warning systems, since they enable the (near) real-time tracking of extreme weather events. In the Community Climatology approach, we use multi-fidelity regression kriging to blend official NMS observations with crowd-sourced observations and high-resolution covariates. Crowd-sourced weather networks producing low-fidelity observations are often the only type of data available at local (e.g. neighborhood) scales. In this work, we demonstrate that we can provide such maps by combining high-fidelity official weather data with low-fidelity crowd-sourced weather data and high-resolution covariate information.

In the Community Climatology approach, we use multi-fidelity regression kriging to blend official NMS observations with crowd-sourced observations and high-resolution covariates. We think this can evolve into a new partnership between society and science, where volunteers find a meaningful way to directly contribute to improved monitoring capabilities of climate change and the quantification of extreme events.

The contribution from volunteer observations is shown to be a meaningful addition to gridded data sets, for five different user-requested climate variables in the Netherlands. We provide this data set at a spatial resolution of 1 x 1 km and at a 10-minute  time resolution. Initial results show a significant increase of accuracy of these data sets due to the blending approach.

The significant bias and noise in the volunteer observations and how these affect the temperature maps based on the multi-fidelity regression kriging are discussed.

Bios

Gerard van der Schrier, works as Senior Scientist at the Royal Netherlands Meteorological Institute’s Research & Development Observations and Data Technology dept. In that capacity, he is responsible for the European Climate Assessment & Dataset and the E-OBS datasets. With his team, work is ongoing to include 2nd and 3rd party data into the Climate Services products of the Dutch Meteorological Service and turn its focus more toward the impact of climate and weather.