Bradley Isom

The Atmospheric Imaging Radar: Simultaneous Volumetric Observations Using a Phased Array Weather Radar

AbstractMobile weather radars often utilize rapid-scan strategies when collecting observations of severe weather. Various techniques have been used to improve volume update times, including the use of agile and multibeam radars. Imaging radars, similar in some respects to phased arrays, steer the radar beam in software, thus requiring no physical motion. In contrast to phased arrays, imaging radars gather data for an entire volume simultaneously within the field of view (FOV) of the radar, which is defined by a broad transmit beam. As a result, imaging radars provide update rates significantly exceeding those of existing mobile radars, including phased arrays. The Advanced Radar Research Center (ARRC) at the University of Oklahoma (OU) is engaged in the design, construction, and testing of a mobile imaging weather radar system called the atmospheric imaging radar (AIR). Initial tests performed with the AIR demonstrate the benefits and versatility of utilizing beamforming techniques to achieve high spatial...

Mitigation of Wind Turbine Clutter for Weather Radar by Signal Separation

This paper addresses the mitigation of wind turbine clutter (WTC) in weather radar data in order to increase the performance of existing weather radar systems and to improve weather analyses and forecasts. We propose a novel approach for this problem based on signal separation algorithms. We model the weather signal as group sparse in the time-frequency domain; in parallel, we model the WTC signal as having a sparse time derivative. In order to separate WTC and the desired weather returns, we formulate the signal separation problem as an optimization problem. The objective function to be minimized combines total variation regularization and time-frequency group sparsity. We also propose a three-window short-time Fourier transform for the time-frequency representation of the weather signal. To show the effectiveness of the proposed algorithm on weather radar systems, the method is applied to simulated and real data from the next-generation weather radar network. Significant improvements are observed in reflectivity, spectral width, and angular velocity estimates.

A Weather Radar Simulator for the Evaluation of Polarimetric Phased Array Performance

A radar simulator capable of generating time series data for a polarimetric phased array weather radar has been designed and implemented. The received signals are composed from a high-resolution numerical prediction weather model. Thousands of scattering centers (SCs), each with an independent randomly generated Doppler spectrum, populate the field of view of the radar. The moments of the SC spectra are derived from the numerical weather model, and the SC positions are updated based on the 3-D wind field. In order to accurately emulate the effects of the system-induced cross-polar contamination, the array is modeled using a complete set of dual-polarization radiation patterns. The simulator offers reconfigurable element patterns and positions and access to independent time series data for each element, resulting in easy implementation of any beamforming method. It also allows for arbitrary waveform designs and is able to model the effects of quantization on waveform performance. Simultaneous, alternating, quasi-simultaneous, and pulse-to-pulse phase-coded modes of polarimetric signal transmission have been implemented. This framework allows for realistic emulation of the effects of cross-polar fields on weather observations, as well as the evaluation of possible techniques for the mitigation of those effects.

The ARM Cloud Radar Simulator for Global Climate Models: Bridging Field Data and Climate Models

C louds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real-world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors that are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators,” which convert model variables into pseudoinstrument observations, has evolved with the goal to facilitate and improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the GCM community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to the radar measurements made by CloudSat [a satellite carrying the first spaceborne 94-GHz (3.2-mm wavelength) cloud radar], which provides near-global sampling of profiles of cloud condensate and precipitation with a vertical resolution of 500 m (Stephens et al. 2002), ARM radar measurements occur with higher temporal resolution (10 s) and finer vertical resolution (45 m). This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to both surface contamination (Mace et al. 2007; Marchand et al. 2008) and a radar sensitivity of approximately −28 dBZ near the surface. Therefore, the ARM ground-based cloud observations complement measurements from space.

ARM: X-Band Scanning ARM Cloud Radar (XSACR) RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) RHI Scans, which can vary in elevation range and azimuth

ARM: X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scan

X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)

ARM: Ka-Band Scanning ARM Cloud Radar (KASACR) RHI scans, which can vary in elevation range and azimuth

Ka-Band Scanning ARM Cloud Radar (KASACR) RHI scans, which can vary in elevation range and azimuth

ARM: Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan

Ka-Band Scanning ARM Cloud Radar (KASACR) Hemispherical Sky RHI Scan (6 horizon-to-horizon scans at 30-degree azimuth intervals)

ARM: Ka ARM Zenith Radar (KAZR): filtered spectral data, moderate sensitivity mode, cross-polarized mode

Ka ARM Zenith Radar (KAZR): filtered spectral data, moderate sensitivity mode, cross-polarized mode

ARM: W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scan

W-Band Scanning ARM Cloud Radar (W-SACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals)