RadarLab

Albert Oude Nijhuis

Radar remote sensing of wind vector and turbulence intensity fields from raindrop backscattering

Scanning radars are promising sensors for atmospheric remote sensing, giving potential to retrieve parameters that characterize the local air dynamics during rain. For the observation of air motion, radars are relying on the backscatter of particles, which can, for example, be raindrops or insects. To measure wind vectors and turbulence intensities remotely during rain the radar is a common choice. This is mainly because the radar signals are not attenuated too much by the rain itself, which is the case for instruments operating at other frequencies, such as lidars. There is, however, a problem with measuring air dynamics from raindrops. Raindrops are not perfect tracers of the air motion. It may thus be necessary to make some corrections when air-dynamics parameters are estimated with a radar during the rain, and account for that raindrops are imperfect tracers of the air motion. This dissertation focuses on this problem. In addition, existing radar-based wind vector and turbulence intensity retrieval techniques are assessed for when they are applied during the rain, and they have been further developed.

Jeroen van Gemert

Efficient computational methods in Magnetic Resonance Imaging

How to design dielectric pads that can be used to increase image quality inMagnetic Resonance Imaging, and how to accelerate image reconstruction times using a preconditioner.