Speaker
Description
The project MERGOSAT (Merging of rain rate estimates from opportunistic sensors and geostationary satellites) has the overarching goal to develop novel methods for the generation of improved near-real-time rainfall maps for data-scarce regions via a combination of data from geostationary satellites (GEOsat) and opportunistic ground-based sensors, namely commercial microwave links (CMLs) and satellite microwave links (SMLs). In this contribution we focus on the first results of the work on improving the rainfall retrieval from CMLs and SMLs.
We present a new deep learning method for wet-dry classification that uses a combination of CML attenuation time series and the CLAAS3 cloud type product from the Meteosat Second Generation (MSG) and Meteosat Third Generation (MTG) geostationary satellites. To our knowledge this is the first time that these two data sources are combined for wet-dry classification via deep learning. Our results show that this approach outperforms existing methods.
To investigate the effect of the melting layer on SML path attenuation, we have analysed synthetic Ku-Band attenuation based on ICON model output (2-moment microphysics; ~2km resolution) applying its native radar forward operator EMVORADO. Our results show that the attenuation within the melting layer can reach the same level as the attenuation due to liquid hydrometeors below the melting layer, even though the liquid path length is typically 4-6 times larger. In particular for lighter rain we see a significant overestimation of rain rates from SML attenuation if standard treatment of the melting layer is used.
A long-standing question in CML rainfall estimation is how the wet antenna attenuation (WAA) effect is dependent on frequency. We use a combination of full-wave electromagnetic simulations, simplified simulations setups and WAA measurements in a lab environment to investigate this in detail. For a given antenna and frequency, our results show that the WAA effect can be estimated quantitatively with sufficient accuracy from only rudimentary knowledge about the wetness condition of the radome. With the support from thorough electromagnetic full-wave simulations, the development of a more general, physics-based and frequency-dependent WAA model is currently pursued.