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This dataset contains data from a NOAA Radar Wind Profiler located on Block island. Data can be found at https://psl.noaa.gov/data/obs/datadisplay/ with the following filters: Active Sites: bid Instrument: 915 MHz Wind Profiler Product: Radar 915MHz Sub-Hourly Weber Wuertz Wind
Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.
Following methods outlined in Remillard et al. (2012), we classify seven cloud types using radar reflectivity, best-estimated cloud base, and cloud-layer product from the Active Remotely Sensed Cloud Locations (ARSCL) product (Kollias et al. 2007). A cloud mask is created based on the detectable radar reflectivity (>-40 dBZ) combined with the best-estimated cloud base height. Each cloud object is analyzed individually as contiguous cloudy pixels, and its type is determined based on the cloud’s boundaries and duration. Focusing on marine boundary-layer clouds, low clouds are further classified into four types: shallow cumulus, broken stratocumulus (Sc) or cumulus clouds, single-layer Sc, and multi-layer Sc or Sc coupled with cumulus. The remaining three categories are middle clouds, high clouds, and deep convective clouds. For detailed definition of each cloud type, please refer to Zheng et al. (2024).
The X-band Scanning ARM Precipitation Radar (X-SAPR) is an X-band dual-polarization Doppler weather radar. The X-SAPR operates in a simultaneous transmit and receive (STAR) mode, meaning that the transmit signal is split so that power is transmitted on both horizontal and vertical polarizations at the same time. The X-SAPR transmitter is a 200 kW (peak power) magnetron. The receiver is based upon the Vaisala RVP-900 and runs Vaisala’s IRIS software. In addition to the first three Doppler moments (reflectivity, radial velocity, and spectra width), the X-SAPR also provides differential reflectivity, correlation coefficient, and specific differential phases. The dual-polarization variables enable estimates of rainfall rates and identification of precipitation types. At the Southern Great Plains site, three X-SAPRs surround the Central Facility, allowing the use of multi-Doppler velocity retrievals to estimate wind fields.
X-Band Scanning ARM Cloud Radar (XSACR) Along-Wind RHI Scan Corrected
X-Band Scanning ARM Cloud Radar (XSACR) Cross-Wind RHI Scan Corrected
X-Band Scanning ARM Cloud Radar (XSACR) Vertically Pointing Scan Corrected
X-Band Scanning ARM Cloud Radar (XSACR) Hemispherical Sky RHI Scans (6 horizon-to-horizon scans at 30-degree azimuth intervals) Corrected
X-Band Scanning ARM Cloud Radar (XSACR) Boundary Layer RHI Scan Corrected
As the utility of synthetic aperture radar (SAR) systems increases in autonomous vehicles, satellites, and other power- and space-constrained edge applications, there is a growing need for processors that can form SAR images at low power. In recent years, analog in-memory compute (AIMC) has shown immense promise for accelerating neural networks and other matrix-vector multiplication (MVM) heavy workloads at the edge. Here, in this work, we examine how the polar format algorithm (PFA), a popular SAR image formation algorithm, can be mapped to these AIMC systems. The PFA maps readily onto analog MVMs because it primarily consists of two linear operations: interpolation of frequency-domain data to a Cartesian grid, followed by a 2-D Fourier transform. This work presents two approaches to map the interpolation operation onto MVMs in analog hardware: a chirp transform and a modified form of sinc interpolation. These mappings introduce algorithmic errors, and their effect on the quality of SAR image formation is examined, both quantitatively and qualitatively. In addition, the impact of errors introduced by the analog hardware is explored to determine which approach is optimal under varying assumptions about the underlying analog memory devices and circuits.
This research project was successful in delivering on four main objectives. First, software was developed to accurately calculate 915-MHz radar wind profiler (RWP) spectrum moments from the recorded Doppler velocity power spectra. Second, software was developed to calibrate the RWP reflectivity factor using collocated surface disdrometer observations. Third, the Python processing code was documented and given to the ARM Infrastructure to produce ARM ‘b level’ calibrated RWP products. Fourth, calibrated RWP products were uploaded to the ARM Archive as PI Products for 10 years of SGP RWP observations and for GoAmazon and TRACER field campaign RWP observations. In addition to working with RWP observations, this research project also worked with KAZR observations to distinguish insects from boundary layer clouds to help improve the ARSCL cloud mask product. The PI worked with senior and early career ARM funded scientists at BNL exploring how to include calibrated RWP moments into future versions of the ARSCL product.
INL recently summarized how Ukraine conducted an unprecedented drone attack on Russian air bases on Sunday, June 1, 2025 using Russian cellular networks to operate the drones. It also showed how a drone with a cellular connection can be detected using network signatures. This report adds an additional section, section 2, on detecting drones with radar if they are not connected to a cellular network and hence network signatures are not available.
This is a final technical research report for the grant DE-SC0021247 "Polarimetric radar & lightning analysis and high resolution simulations to support TRACER science goals" summarizing project goals, accomplishments, and publications resulting from supported work.
The Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) was an aircraft-based campaign (15 January–26 February 2018) that deployed in situ probes and remote sensors to investigate low-level clouds over the Southern Ocean (SO). A novel methodology was developed to identify cloud boundaries and classify cloud phases in single-layer, low-level marine boundary layer (MBL) clouds below 3 km using the HIAPER Cloud Radar (HCR) and in situ measurements. The cloud base and top heights derived from HCR reflectivity, Doppler velocity, and spectrum width measurements agreed well with corresponding lidar-based and in situ estimates of cloud boundaries, with mean differences below 100 m. A liquid water content–reflectivity (LWC-Z) relationship, LWC = 0.70Z0.29, was derived to retrieve the LWC and liquid water path (LWP) from HCR profiles. The cloud phase was classified using HCR measurements, temperature, and LWP, yielding 40.6% liquid, 18.3% mixed-phase, and 5.1% ice samples, along with drizzle (29.1%), rain (3.2%), and snow (3.7%) for drizzling cloud cases. The classification algorithm demonstrates good consistency with established methods. This study provides a framework for the boundary and phase detection of MBL clouds, offering insights into SO cloud microphysics and supporting future efforts in satellite retrievals and climate model evaluation.
Fully-polarimetric synthetic aperture radar (PolSAR) data contain a rich body of elementary scattering physics information that is critically valuable for a broad range of applications and scientific purposes. However, there is a lack of available high-resolution (< 0.3048-m) data available for PolSAR phenomenology research. This article introduces a high-resolution PolSAR data set collected and provided by Sandia National Laboratories (SNL). The data sets were collected to support studying high-resolution scattering physics from different types of clutter and applications such as polarimetric-based terrain classification.
Peat soils are a critical component of the global carbon cycle as natural producers of biogenic greenhouse gases (e.g., methane and carbon dioxide) that accumulate within the soil and are released to the atmosphere. Previous studies have showed the ability of ground-based minimally-invasive geophysical methods such as ground-penetrating radar (GPR) to characterize carbon dynamics in peat soils. However, ground-based GPR is limited by scale of measurement and soil disturbance potentially altering gas releases during deployment. Here, we explore the potential of drone-based GPR for identification of hot spots and hot moments of gas accumulation and release in subtropical soils. Here, we collected drone-based GPR data sets across two grids (∼17,500 m 2 ) in the Everglades during January (dry season), September, and November (wet season) of 2023 to characterize peat thickness and seasonal variability of gas content. Results show that drone-based GPR is effective and efficient for: (a) capturing the temporal variation of in situ biogenic gas content in peat soils with changes between 1% and 25 % volumetric gas content over repeatable grids; (b) inferring a total peat thickness between 0.8 and 1.2 m; and (c) estimating flux releases of 63 and 135 mg CH 4 m −2 day −1 for specific locations and periods that are strikingly consistent with our coincident gas trap measurements. This work also indicates that (a) spatial distribution of gas content in the Everglades is strongly controlled by landscape morphology such as ridges and sloughs and (b) the temporal variation of gas content is seasonal with increased gas production during the wet season.
When collected over a curved path, Synthetic Aperture Radar (SAR) contains information about the three-dimensional content of a scene that can aid interpretation of this difficult imaging modality. In a point scatterer simulation with amplitudes and heights derived from a real SAR image pair, we supplement confocal processing with a monopulse-based algorithm to compute heights of certain bright points, which we find automatically. In SAR, bright points often reflect man-made infrastructure central to scene interpretation.
SF-26-021 Adapt is a data processing platform for real-time data analysis, short term prediction of targets convective cells and tracking for archived data. It provides tools for downloading, processing, segmenting, projecting, analyzing, and visualizing storm cell data from weather radar. The pipeline includes cell detection, motion estimation using optical flow, cell property extraction, and persistence to NetCDF and SQLite/Parquet for guiding adaptive scanning.