Radar - Lubbock, TX - Raw Data
Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
17 WIND ENERGY↗
SEARCH · Engineering Papers
Results for “Radars”
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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Radar - Brady, TX - Raw Data
Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
17 WIND ENERGY↗
Radar - Colorado City, TX - Raw Data
Raw data captured from the NOAA ESRL website: https://www.esrl.noaa.gov/psd/data/obs/data/ Hourly Radar Winds (WwWind) and Temperature (WwTemp)
17 WIND ENERGY↗
Radar Wind Profiler at McKinleyville, CA
These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.
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Pacific Coast High-Frequency Radar 2 km
High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 2 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.
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Pacific Coast High Frequency Radar 6 km
High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 6 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.
17 WIND ENERGY↗
Radar Wind Profiler at Bodega Bay
These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.
17 WIND ENERGY↗
Radar Wind Profiler at Point Sur
These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.
17 WIND ENERGY↗
Radar Wind Profiler at Santa Barbara
These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.
17 WIND ENERGY↗
Radar Wind Profiler at San Nicolas Island
These data are collected as part of an observational database developed to support the floating offshore wind energy research under the ORACLE project funded by DOE Wind Energy Technologies Office (WETO). The radar wind profiler network data are collected by NOAA (https://psl.noaa.gov/data/obs/datadisplay), and only data within the state of California are part of the database.
17 WIND ENERGY↗
Cloud Doppler Radar
This dataset contains raw data from the UND 94 GHz W-band Cloud Doppler Radar.
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Radar / Raw Data
This dataset contains raw data from the UND 94 GHz W-band Cloud Doppler Radar.
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Radar / Processed Data
This dataset contains processed raw data from the UND 94 GHz W-band Cloud Doppler Radar.
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Micro Rain Radar / Reviewed Data
This dataset contains the Micro Rain Radar data that have been reviewed by the mentor and calibrated against the Parsivel on the barge.
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Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska
Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.
Dann, Julian↗
Radio Afterglow Detection and AI-driven Response (RADAR): A Federated Framework for Gravitational-wave Event Follow-up
The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.
Gravitational waves↗
Multifrequency radar observations of marine clouds during the EPCAPE campaign
The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was a year-round campaign conducted by the US Department of Energy at the Scripps Institution of Oceanography in La Jolla, CA, USA, with a focus on characterizing atmospheric processes at a coastal location. The ground-based prototype of a new Ka-, W-, and G-band (35.75, 94.88, and 238.8 GHz) profiling atmospheric radar, named CloudCube, which was developed at the Jet Propulsion Laboratory, took part in the experiment during 6 weeks in March and April 2023. This article describes the unique data sets that were obtained during the field campaign from a variety of marine clouds and light precipitation. These are, to the best of the authors' knowledge, the first observations of atmospheric clouds using simultaneous multifrequency measurements including 238.8 GHz. These data sets therefore provide an exceptional opportunity to study and analyze hydrometeors with diameters in the millimeter- and submillimeter size range that can be used to better understand cloud and precipitation structure, formation, and evolution. The data sets referenced in this article are intended to provide a complete, extensive, and high-quality collection of G-band data in the form of Doppler spectra and Doppler moments. In addition, Ka-band and W-band reflectivity and Ka-, W-, and G-band reflectivity ratio profiles are included for several cases of interest on 6 different days.
54 ENVIRONMENTAL SCIENCES↗
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Forward models are a key tool to generate synthetic observations given knowledge of the atmospheric state. In this way, they are an integral part of inversion algorithms that aim to retrieve geophysical variables from observations or in data assimilation. Their application for the exploitation of the full information content of remote sensing observations becomes increasingly important when these are used to evaluate the performance of cloud-resolving models (CRMs). Herein, CRM profiles or fields provide the input to the forward model whose simulation results are subsequently compared to the observations. This paper introduces the freely available comprehensive microwave forward model PAMTRA (Passive and Active Microwave TRAnsfer), demonstrates its capabilities to simulate passive and active measurements across the microwave spectral region for upward- and downward-looking geometries, and illustrates how the forward simulations can be used to evaluate CRMs and to interpret measurements to improve our understanding of cloud processes. PAMTRA is unique as it treats passive and active radiative transfer (RT) in a consistent way with the passive forward model providing upwelling and downwelling polarized brightness temperatures and radiances for arbitrary observation angles. The active part is capable of simulating the full radar Doppler spectrum and its moments. PAMTRA is designed to be flexible with respect to instrument specifications and interfaces to many different formats of input and output, especially CRMs, spanning the range from bin-resolved microphysical output to one- and two-moment schemes, and to in situ measured hydrometeor properties. A specific highlight is the incorporation of the self-similar Rayleigh–Gans approximation (SSRGA) for both active and passive applications, which becomes especially important for the investigation of frozen hydrometeors.
54 ENVIRONMENTAL SCIENCES↗