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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Weather radar utility in hazard detection and response

Publicly accessible weather radar data have significant capabilities for meteorological measurements and predictions and, further, have the potential to measure nonmeteorological events that include smoke, ash, and debris plumes as well as explosions. The ability to identify and track nonmeteorological events can be of assistance in emergency response, hazard mitigation, and related activities in locations where radar coverage both exists and is recorded and accessible to the user. Here, in this study, events from multiple locations in the United States that are reported in news outlets are assessed using a manual inspection process of Level 2 weather radar data to identify anthropogenic and nonbiological returns. Explosive events are also identified, and a large high-altitude debris cloud from the intentional destruction of the SpaceX Starship is tracked across a wide area. Finally, future efforts using a machine learning model are discussed as a means of automating the process and potentially enabling near-real-time nonmeteorological event identification in the same areas where the data are accessible. Using weather radar data can be a valuable new tool for Department of Defense systems to aid in military awareness, and for interagency emergency response and forensic mission experts to consider national weather service data in their mission profiles. Radar data can be effective in detecting several common types of emergencies and inform and aid response personnel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Next Generation Weather Radar (NEXRAD) Setback (4-km)

The Next Generation Weather Radar (NEXRAD) system is a network of doppler radar operated jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force. This dataset represents 4-kilometer setback requirements typically applied to wind energy development. A setback requirement is a minimum distance away that an energy project may be developed. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Airspace/Defense↗
DOE OSTI2024

Next Generation Weather Radar (NEXRAD) Radar Line-of-Sight

The Next Generation Weather Radar (NEXRAD) system is a network of doppler radar operated jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force. This dataset represents a line-of-sight for each radar station. Radar line-of-sight can become saturated with wind energy due to interference. For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Airspace/Defense↗
DOE OSTI2021

Weather radar in Nepal: opportunities and challenges in a mountainous region

Extreme rainfall is one of the major causes of natural hazards in the central Himalayan region, including Nepal. The performance of strategies to manage hazards and related risks relies on the accuracy of quantitative hydrometeorological prediction. Rain gauges have traditionally been used to measure the rainfall amount. However, point measurements with limited gauge coverage cannot accurately represent spatial precipitation variability in complex topography. Weather radar have shown potential for useful information on accurate areal rainfall estimates. The Department of Hydrology and Meteorology (DHM) in Nepal installed their first weather radar in 2019 in the western region of the country. Two more radars will be added to the planned radar network in the near future, in the country's central and eastern regions, respectively. We highlight both the opportunities and challenges with radar installation and observation in the mountainous regions. Radar-rainfall estimates across the Himalayas can be useful to inform decision-making in a broad range of infrastructure sectors, including water, energy, construction, transportation, and agriculture.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Adapt: A Weather Radar Data Analysis and Nowcasting Platform for Informed Adaptive Scanning

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.

Raut, Bhupendra Ashokrao [Argonne National Laborat↗
DOE OSTI2023

Performance Limits for Airborne Weather Detection Radar

An aircraft commander needs to be aware of weather phenomena that might be hazardous to his aircraft and mission. An important tool for this is airborne weather (WX) detection radar. The airborne WX radar needs to map weather for the aircraft commander that might be relevant to the safety of the aircraft, which involves both detecting a weather phenomenon, and to some extent seeing through it to detect weather phenomena behind it. Many factors influence the performance of an airborne WX radar

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Calibration and Validation of the SAIL Radar

Calibration of a weather radar is not a simple process, as is well described and documented in several articles (Chandrasekar, V, L Baldini, N Bharadwaj, and PL Smith. 2015. "Calibration procedures for global precipitation-measurement ground-validation radars." URSI Radio Science Bulletin 2015(355): 45–73, https://10.23919/URSIRSB.2015.7909473.). Direct hardware-based characterization is called calibration whereas indirect check with other sources such as disdrometer, or comparison with other radars or indirect means, is called validation. The validation process has its own measurement error and the comparison should fall within its limit. In the following we describe calibration and validation processes for the SAIL radar. Based on the comparison of calibration and validation, an addition of 2 dB is suggested for the SAIL radar observations, with a nuanced Z dr calibration correction, as presented. If a fixed Z dr calibration is preferred, a bias addition of 0.5 dB is suggested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Study of Microphysical Signatures Based on Spectral Polarimetry during the RELAMPAGO Field Experiment in Argentina

Weather radars with dual-polarization capabilities enable the study of various characteristics of hydrometeors, including their size, shape, and orientation. Radar polarimetric measurements, coupled with Doppler information, allow for analysis in the spectral domain. This analysis can be leveraged to reveal valuable insight into the microphysics and kinematics of hydrometeors in precipitation systems. This paper uses spectral polarimetry to investigate precipitation microphysics and kinematics in storm environments observed during the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field experiment in Argentina. This study uses range–height indicator scan measurements from a C-band polarimetric Doppler weather radar deployed during the field campaign. Here, in this work, the impact of storm dynamics on hydrometeors is studied, including the size sorting of hydrometeors due to vertical wind shear. In addition, particle microphysical processes because of aggregation and growth of ice crystals in anvil clouds, as well as graupel formation resulting from the riming of ice crystals and dendrites, are also analyzed here. The presence of different particle size distributions because of the mixing of hydrometeors in a sheared environment and resulting size sorting has been reported using spectral differential reflectivity (sZ dr ) slope. Spectral reflectivity sZ h and sZ dr have also been used to understand the signature of ice crystal aggregation in an anvil cloud. The regions of pristine ice crystals are identified from vertical profiles of spectral polarimetric variables in anvil cloud because of sZ h < 0 dB and sZ dr values around 2 dB. It is also found that the growth process of these ice crystals causes a skewed bimodal sZ h spectrum due to the presence of both pristine ice crystals and dry snow. Next, graupel formation due to riming has been studied, and it is found that the riming process produces sZ h values of about 10 dB and corresponding sZ dr values of 1 dB. This positive sZ dr indicates the presence of needle/columnar secondary ice particles formed by ice multiplication processes in the riming zones. Last, the temporal evolution of a storm is investigated by analyzing changes in hydrometeor types with time and their influence on the spectral polarimetric variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

xsacrcfrspccopol (a1)

ARM's scanning cloud radars are fully coherent dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (WSACR; 1.7 kW peak power) or X-band (XSACR; 20 kW peak power). At ARM's tropical sites, X-band radars are paired with the Ka-band because they are better suited for the atmospheric attenuation in this region. Beamwidth for the X-band is approximately 1 degree, and the Ka-band beamwidth is roughly 0.3 degrees. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the XSACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. For the second ARM Mobile Facility (AMF2), the XSACR can be dismounted from the pedestal it shares with the KASACR and mounted on a separate pedestal. This allows the XSACR to operate more like a weather (precipitation) radar in deployments where such local coverage is lacking. Measurements collected with the XSACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, differential Reflectivity (Zdr), correlation coefficient (rho-hv), and specific differential phase (phi-dp). XSACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

xsacrcfrspcxpol (a1)

ARM's scanning cloud radars are fully coherent dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (WSACR; 1.7 kW peak power) or X-band (XSACR; 20 kW peak power). At ARM's tropical sites, X-band radars are paired with the Ka-band because they are better suited for the atmospheric attenuation in this region. Beamwidth for the X-band is approximately 1 degree, and the Ka-band beamwidth is roughly 0.3 degrees. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the XSACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. For the second ARM Mobile Facility (AMF2), the XSACR can be dismounted from the pedestal it shares with the KASACR and mounted on a separate pedestal. This allows the XSACR to operate more like a weather (precipitation) radar in deployments where such local coverage is lacking. Measurements collected with the XSACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, differential Reflectivity (Zdr), correlation coefficient (rho-hv), and specific differential phase (phi-dp). XSACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

xsacrcfrspccross (a1)

ARM's scanning cloud radars are fully coherent dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (WSACR; 1.7 kW peak power) or X-band (XSACR; 20 kW peak power). At ARM's tropical sites, X-band radars are paired with the Ka-band because they are better suited for the atmospheric attenuation in this region. Beamwidth for the X-band is approximately 1 degree, and the Ka-band beamwidth is roughly 0.3 degrees. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the XSACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. For the second ARM Mobile Facility (AMF2), the XSACR can be dismounted from the pedestal it shares with the KASACR and mounted on a separate pedestal. This allows the XSACR to operate more like a weather (precipitation) radar in deployments where such local coverage is lacking. Measurements collected with the XSACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, differential Reflectivity (Zdr), correlation coefficient (rho-hv), and specific differential phase (phi-dp). XSACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Ka-Band Scanning ARM Cloud Radar (a1)

The Ka-Band Scanning ARM Cloud Radar (KASACR) records cloud properties. ARM's scanning cloud radars are dual-frequency, dual-polarization Doppler radars mounted on a common scanning pedestal. Each pedestal includes a Ka-band radar (2kW peak power) and the deployment location determines whether the second radar is a W-band (1.7 kW peak power) or an X-band (20 kW peak power). Beamwidths for Ka-bands paired with W-bands are roughly matched at 0.3 degrees. The X-band beamwidth is approximately 1 degree. Due to the narrow antenna beamwidth, ARM’s scanning cloud radars use scanning strategies unlike typical weather radars. Rather than focusing on plan position indicator, or PPI, scans, the KASACR uses range height indicator, or RHI, scans at numerous azimuths to obtain cloud volume data. Measurements collected with the KASACR are copolar and cross-polar radar reflectivity, Doppler velocity, spectra width and spectra when not scanning, and linear depolarization ratio. KASACR data from the 2018–2019 Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign in Argentina are now available as b1-level products. Building on the original CACTI operational data, the b1-level products feature improved data quality resulting from extensive analyses and corrections. The data are cross-calibrated to a common point, datastreams are corrected for operational issues that occurred during the campaign, and several data quality masks and basic derived products are incorporated. For more information, read the CACTI radar b1-level processing report.

54 ENVIRONMENTAL SCIENCES↗