Improved Observation of Transient Phenomena with Doppler Radars: a Common Framework for Oceanic and Atmospheric Sensing

TL;DR

This paper presents a unified framework using autoregressive modeling for Doppler radars, enhancing the detection of rapid transient phenomena in oceanic and atmospheric sensing with improved resolution and noise handling.

Contribution

It introduces a non-spectral autoregressive approach applied to both oceanic and atmospheric Doppler radar data, enabling better analysis of transient events at short integration times.

Findings

Effective modeling of backscattered signals with autoregressive processes.

Enhanced detection of transient atmospheric phenomena.

Potential for real-time, high-resolution remote sensing.

Abstract

Doppler radars are routinely used for the remote sensing of oceanic surface currents and atmospheric wind profiles. Even though they operate at different frequencies and address different media, they follow very similar processing for the extraction of measured velocities. In particular they both face the challenging issue of capturing geophysical phenomena which vary rapidly with respect to the typical integration time. Recently, the authors applied a non-spectral formalism based on autoregressive processes to model the backscattered time series obtained from High-Frequency oceanic radars. They showed that it allows to calculate Doppler spectra for very short integration times without losing in frequency resolution nor signal-to-noise ratio. We apply this technique to synthetic and experimental data within a common framework and show for the first time the strong potential of the method for the study of transient atmospheric phenomena.