M
Milad Asgarimehr
Researcher at University of Potsdam
Publications - 23
Citations - 181
Milad Asgarimehr is an academic researcher from University of Potsdam. The author has contributed to research in topics: GNSS applications & Environmental science. The author has an hindex of 5, co-authored 16 publications receiving 73 citations. Previous affiliations of Milad Asgarimehr include K.N.Toosi University of Technology & Technical University of Berlin.
Papers
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Journal ArticleDOI
Can GNSS Reflectometry Detect Precipitation Over Oceans
Journal ArticleDOI
A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals
TL;DR: The proposed neural network demonstrates an ability to model a variety of effects degrading the retrieval accuracy such as the different levels of the effective isotropic radiated power (EIRP) of GPS satellites.
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TDS-1 GNSS Reflectometry: Development and Validation of Forward Scattering Winds
TL;DR: A decrease in TDS-1-derived bistatic radar cross sections during rain events, at weak winds, is demonstrated, which indicates the promising capability of GNSS forward scattering for wind retrievals during rain.
Journal ArticleDOI
GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet
A. V. Boyarintsev,Milad Asgarimehr,Caroline Arnold,Tobias Weigel,Christopher S. Ruf,Jens Wickert,Jens Wickert +6 more
TL;DR: In this article, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized, and the best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage.
Journal ArticleDOI
Evaluating Impact of Rain Attenuation on Space-borne GNSS Reflectometry Wind Speeds
TL;DR: In this article, the authors evaluated the rain attenuation impact on the bistatic radar cross section and the derived wind speed, considering that an empirical data analysis at extreme wind intensities and rain rates is impossible due to the insufficient number of observations from these severe conditions.