Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data

Graf, Maximilian; Wagner, Andreas; Polz, Julius; Lliso, Llorenç; Lahuerta, José Alberto; Kunstmann, Harald; Chwala, Christian

doi:https://doi.org/10.5194/amt-17-2165-2024

Articles | Volume 17, issue 7

https://doi.org/10.5194/amt-17-2165-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-17-2165-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 7

Research article

|

17 Apr 2024

Research article |

| 17 Apr 2024

Improved rain event detection in commercial microwave link time series via combination with MSG SEVIRI data

Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala

Data sets

Index of /climate_environment/CDC/grids_germany/5_minutes/radolan/reproc/2017_002/ Deutscher Wetterdienst https://opendata.dwd.de/climate_environment/CDC/grids_germany/5_minutes/radolan/reproc/2017_002/

PC and PC-Ph products NWC SAF https://www.nwcsaf.org/web/guest/nwc/geo-geostationary-near-real-time-v2021

Short summary

Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.