4-9 September 2022, Bonn, Germany
EMS Annual Meeting Abstracts
Vol. 19, EMS2022-240, 2022
https://doi.org/10.5194/ems2022-240
EMS Annual Meeting 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Exploratory analysis of hourly precipitation data measured by citizen observations over Europe

Emma Baietti1,2 and Cristian Lussana1
Emma Baietti and Cristian Lussana
  • 1the Norwegian Meteorological Institute, Division for Climate Services, Oslo, Norway (cristianl@met.no)
  • 2Universita’ di Bologna, Bologna, Italy

The amount of data observing precipitation near the Earth’s surface today is enormous and is constantly growing. Among the most interesting data sources, which are showing a greater development are the observational networks made up of stations managed by citizens. From the point of view of national meteorological services, this type of data constitutes an opportunity to integrate the networks of traditional weather stations managed by public institutions. For precipitation, especially, the availability of a dense network delivering data at hourly sampling rates, or less, allows for the reconstruction of weather phenomena occurring between the microscale and the mesoscale. Usually, a network of taditional weather stations allow for the reconstruction of atmospheric fields of a variable near the surface with a minimum spatial effective resolution of about 20 km, the use of crowdsourced observations can potentially move this lower limit down to about 2 km.

In this study, we consider the observations of hourly precipitation collected by a network of European citizens from September 2019 to August 2020. All the observations have been measured by Netatmo weather stations. The stations can be purchased in shops and are then installed in private homes for the most varied purposes, including home automation for instance.

We present an exploratory analysis of the data collected aiming at setting up a quality control system for hourly precipitation. In particular, we will show statistics describing the dataset in terms of: data availability over Europe; rain/no-rain probability; (local) variability of precipitation as a function of its intensity; spatial statistics characterizing the length scales of precipitation over time.

How to cite: Baietti, E. and Lussana, C.: Exploratory analysis of hourly precipitation data measured by citizen observations over Europe, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-240, https://doi.org/10.5194/ems2022-240, 2022.

Displays

Display file

Supporters & sponsors