SOC #2 30th of January 2024, 9:00 am CET

Join the conference via Zoom: https://tuwien.zoom.us/j/64842384214

Open data from microwave links, radar and gauges published to boost rainfall research and applications (OpenMRG)

Jafet C. M. Andersson1, Jonas Olsson1, Remco (C. Z.) van de Beek1, and Jonas Hansryd2

1 Swedish Meteorological and Hydrological Institute (SMHI), Sweden, 2 Ericsson Research, Ericsson AB, Sweden

Commercial microwave links (CMLs) are viable opportunistic sensors for rainfall (www.smhi.se/memo). However, access to CML data for research and implementation is often very limited. To help in gaining better access and research into CML-derived rainfall we present a dataset at 10 second resolution with true coordinates for 364 bi-directional CMLs gathered during a pilot study in Gothenburg, Sweden. These data are complemented by additional data from 11 high-resolution rain gauges and radar data. The data are openly shared at https://doi.org/10.5281/zenodo.7107689, to boost rainfall research and applications using CMLs.

The role of quality control in CML-based transboundary rainfall estimation

Nico Blettner1,2, Martin Fencl3, Vojtěch Bareš3, Harald Kunstmann1,2, and Christian Chwala1,2

1 Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany, 2 Institute of Geography, University of Augsburg, Augsburg, Germany, 3 Department of Hydraulics and Hydrology, Czech Technical University in Prague, Prague, Czech Republic

Raw CML observations require elaborate processing routines to enable meaningful rainfall estimation. The algorithms applied for this purpose are commonly adjusted to individual datasets. This limits the applicability to larger spatial scales involving more than one dataset. We analyzed whether two independent and heterogeneous CML datasets (from Germany and the Czech Republic) can be combined and processed jointly to enable consistent transboundary rainfall estimation. While rain rate retrieval algorithms were found to be largely transferable, extensions of quality control algorithms were required to enable consistent rainfall maps.

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SOC #1 28th of November 2023, 9:00 am CET

CML data assimilation for precipitation forecasts over Austria Alexander Kann1, Christoph Wittmann1, Phillip Scheffknecht1, Benedikt Bica1, Oliver Eigner2, Fabian Kovac2 1 GeoSphere, Austria, 2 St.

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