Search for: All records

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data within the tar.gz files are in readable ASCII format. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data format is readable ASCII. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 18° elevation angle. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). The data are readable ASCII. 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The frequency of data collection was based on DOW7 scanning and data are usually sampled every 4-6 minutes. The data are in netCDF format. 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The frequency of data collection was based on DOW6 scanning and data are usually sampled every 4-6 minutes. The data are in netCDF format. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 17° elevation angle. In cases where 17° data were unavailable, the highest available elevation angle was utilized. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). 

3D imaging of porous materials in polymer electrolyte membrane (PEM)-based devices, coupled with in situ diagnostics and advanced multi-scale modelling approaches, is pivotal to deciphering the interplay of mass transport phenomena, performance, and durability. The characterization of porous electrode media in PEM-based cells encompassing gas diffusion layers and catalyst layers often relies on traditional analytical techniques such as 2D scanning electron microscopy, followed by image processing such as Otsu thresholding and manual annotation. These methods lack the 3D context needed to capture the complex physical properties of porous electrode media, while also struggling to accurately and effectively discriminate porous and solid domains. To achieve an enhanced, automated segmentation of porous structures, we present a 3D deep learning-based approach trained on calibrated 3D micro-CT, focused ion beam-scanning electron microscopy datasets, and data from physical porosity measurements. Our approach includes binary segmentation for porous layers and a multiclass segmentation method to distinguish the microporous layers from the gas diffusion layers. The presented analysis framework integrates functions for pore size distribution, porosity, permeability, and tortuosity simulation analyses from the resulting binary masks and enables quantitative correlation assessments. Segmentations achieved can be interactively visualized on-site in a 3D environment. 

The dataset consists of vertical profiles of horizontal winds derived from radar Plan Position Indicator (PPI) scans conducted at a 17° elevation angle. In cases where 17° data were unavailable, the highest available elevation angle was utilized. The wind profiles were obtained using the Vertical Azimuth Display (VAD) technique, which involves measuring the radial velocity of targets at various azimuth angles and applying a least square fitting procedure to extract the horizontal wind speed and direction as a function of altitude. This method assumes that the wind field is horizontally homogeneous over the radar sampling volume. For more details on the VAD technique, refer to Browning and Wexler (1968). 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter. The data format is netCDF. 

Doppler spectra are derived from vertical radar scans (89° elevation) by analyzing the in-phase (I) and quadrature (Q) components of the returned signal. The I/Q time-series data are divided into range gates, and a Fast Fourier Transform (FFT) is applied to convert the data from the time domain to the frequency domain. This reveals Doppler frequency shifts caused by moving scatterers, producing spectra that show the distribution of power across velocities. We corrected the data for the influence of the horizontal wind since the scans were not perfectly vertical and removed returns from ground-clutter.