Milad Asgarimehr

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.

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.

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.

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.