EMS Annual Meeting Abstracts
Vol. 18, EMS2021-385, 2021
https://doi.org/10.5194/ems2021-385
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting

Zoi Paschalidi1, Walter Acevedo1, Meike Hellweg3,4, Thomas Kratzsch2, Roland Potthast1,5, and Jens Nachtigall4
Zoi Paschalidi et al.
  • 1German Weather Service (DWD), Research and Development Department, Data Assimilation Unit, Germany
  • 2German Weather Service (DWD), Basic Forecast Department, Germany
  • 3Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Germany
  • 4AUDI AG, Germany
  • 5University of Reading, Department of Mathematics, United Kingdom

The growing availability of high resolved meteorological measurements coming from automobiles puts forward the possibility of developing real time weather forecast systems, which appears to be an essential key of autonomous driving enhancement. In this frame, the Fleet Weather Maps (Flotten-Wetter-Karte - FloWKar) project, a joint work of the German Meteorological Service (DWD) and the German car manufacturer AUDI AG, aims to explore how environmental data from sensors of vehicles on Germany’s roads, respecting data protection regulations, can be used in real time to improve weather forecast, nowcasting and warnings within DWD’s products. Regarding weather forecasting, an exceptionally fast data assimilation cycle with an update rate of the order of minutes is necessary. However, this cannot be achieved using standard assimilation systems. Hence, an ultra-rapid data assimilation (URDA) method has been developed. The URDA updates only a reduced version of the state variables in an existing model forecast, using different kind of observation data available, only after a standard assimilation cycle and a full model forecast. Moreover, the quality of the meteorological data collected by moving vehicles is vital and therefore a series of quality control and bias correction algorithms has been built for the correction of the raw observations, employing among others artificial intelligence techniques. The first preliminary results of both project partners are promising: the corrected measured variables of the mass-produced vehicle-based sensors match well with the ‘ground truth’ and real time maps are produced after the assimilation of the high resolved project data. The improved and detailed model outputs for road weather forecasting are a first necessary step towards the safety on roads especially in the winter conditions and the future autonomous driving.

How to cite: Paschalidi, Z., Acevedo, W., Hellweg, M., Kratzsch, T., Potthast, R., and Nachtigall, J.: Real time weather for autonomous driving and precise road weather forecasts based on floating car data and seamless integration of Ultra-Rapid Data Assimilation and Nowcasting, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-385, https://doi.org/10.5194/ems2021-385, 2021.

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