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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Radar-derived precipitation climatology for wind turbine blade leading edge erosion

Wind turbine blade leading edge erosion (LEE) is a potentially significant source of revenue loss for wind farm operators. Thus, it is important to advance understanding of the underlying causes, to generate geospatial estimates of erosion potential to provide guidance in pre-deployment planning, and ultimately to advance methods to mitigate this effect and extend blade lifetimes. This study focuses on the second issue and presents a novel approach to characterizing the erosion potential across the contiguous USA based solely on publicly available data products from the National Weather Service dual-polarization radar. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments. Results from these locations demonstrate the high spatial variability in precipitation-induced erosion potential, illustrate the importance of low-probability high-impact events to cumulative annual total kinetic energy transfer and emphasize the importance of hail as a damage vector.

Letson, Frederick (ORCID:0000000192750359)↗
DOE OSTI2020

CSAPR2 CMAC 2.0 level c1 data.

Raw data from ARM precipitation radars must be corrected for atmospheric phenomena and instrument characteristics (e.g., attenuation, clutter) to retrieve precipitation properties. The Corrected Moments in Antenna Coordinates Version 2 (CMAC2) value-added product (VAP) is a set of algorithms and code that does such corrections, and it also retrieves precipitation quantities from the radar measurements. Similar to the X-SAPR radars at the ARM main site, C-SAPR2 radar data also needs corrections and improvements. CMAC 2.0 has been updated to work with the ARM C-SAPR 2 radar. Data and fields that have been processed to: correct for velocity aliasing, unfold and generate a cross-polarimetric phase difference that is monotonically increasing, removing impulses caused by non-uniform beam filling and phase shift on backscatter, recalculate specific differential phase using a 20-point Sobel filter on the aforementioned phase, correct for liquid path attenuation using the polarimetric signals, estimate rainfall rates at the gate using the specific attenuation. In addition, CMAC writes the data out into a community-standard format netCDF File using the CF/Radial conventions. The data are therefore compatible with new and existing National Center for Atmospheric Research (NCAR) tools such as RadXConvert for converting to a variety of popular file formats.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Cross Validation of the Network of Ground-Based Radar with GPM during the Remote Sensing of Electrification, Lightning, And Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) Field Campaign

The cross-validation of radars in a network is important in making consistent retrievals across the domain and assuring the product quality. During the RELAMPAGO field campaign, two C-band radars, namely the Colorado State University C-band Hydrological Instrument for Volumetric Observations ( CSU-CHIVO) and the C-band Scanning ARM Precipitation Radar (CSAPR-2), were deployed near the Sierras de Cordoba in Argentina, a region known for having some of the most intense severe weather in the world. In addition to these two radars, the operational radar of the Cordoba City, the Radar Meteorologico Argentino 1 (RMA-1), adds another instrument to the RELAMPAGO network. This study presents an intercomparison between the RELAMPAGO C-band radars using the GPM spaceborne radar as a common reference ..A method to bring ground-based radars into better agreement is also proposed. Moreover, the attenuation correction for the C-band radar is studied in the context of intercomparing two radars. The attenuation coefficients are computed for the RELAMPAGO domain using the local disdrometers deployed. during the campaign. After the attenuation correction, CSU-CHIVO, CSAPR-2, and RMA-1 compare well with GPM-DPR with a high correlation and bias less than 1 dB.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Surface Quantitative Precipitation Estimates (SQUIRE) of Snow Water Equivalent from the Surface Atmospheric Integrated Field Laboratory

The upper Colorado River basin is the primary source of water for 40 million people. With declining snowpack in the basin, forecasting hydrological budgets in the Southwest United States is more important than ever. However, due in part, to a lack of reliable observations of precipitation in complex terrain, hydrological models struggle to assess and forecast snowpack snow water equivalent (SWE) in the upper Colorado River basin (UCRB). Therefore, the need for more reliable SWE forecasts in the UCRB motivated the U.S. Department of Energy Atmospheric Radiation Measurement Facility’s Surface Atmospheric Integrated Field Laboratory (SAIL) that occurred from June 2021 to June 2023. During SAIL, the X-band precipitation radar from Colorado State University conducted volume scans sampling the precipitation properties over the UCRB. The ARM facility developed a gridded Surface Quantitative Precipitation Estimates (SQUIRE) product from the radar observations. To do this, various daily SWE estimates from radar using the radar reflectivity factor Z e and specific differential phase K dp were compared against ground-based precipitation gauges. SWE in precipitation calculated from Wolfe and Snider’s S–Z e estimator was in best agreement with the rain gauges for the days when SWE < 12 mm. For days with SWE > 12 mm, the WSR-88D Intermountain West relationship had the best agreement with the precipitation gauges. Airborne snow depth observations show that SQUIRE captures regions of orographic enhancement in the mountains to the west and northwest of the SAIL study area, indicating that the scientific community should focus on understanding and ultimately simulating orographic atmospheric precipitation processes to improve UCRB snowpack SWE assessment and forecasting.

Hydrology↗
DOE OSTI2025

SAIL Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the second ARM Mobile Facility (AMF2) near Crested Butte, Colorado for the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. The SAIL campaign occurred from September 1, 2021, to June 15 2023. To study the water cycle in the East River Watershed, ARM deployed a vertically pointing Ka-band ARM Zenith radar (KAZR) and a scanning X-band precipitation radar managed by Colorado State University (CSU XPRECIP), as shown in Figure 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Seasonal Precipitation Classification during Surface Atmosphere Integrated Field Laboratory Campaign

The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, conducted from September 2021 to June 2023 in Crested Butte, Colorado, aimed to characterize precipitation processes in the Upper Colorado River Basin (UCRB). This increased observations of snowfall accumulation in this hydrologically significant watershed would be useful for quantitative precipitation estimates (QPE). Therefore, the Surface Quantitative Precipitation Estimate (SQUIRE) product was developed using the ARM-supported Colorado State University (CSU) X-band Precipitation Radar. Although SQUIRE will be only released for snowfall, by categorizing precipitation types, users can effectively utilize relevant datasets under diverse meteorological conditions. Moreover, the dataset facilitates validation of the QPE product and the analysis of seasonal variations in precipitation types at the surface. Hydrometeors classes are organized based on their phase and physical characteristics mapping the CSU (both winter Summer) and Py-ART classifications into four groups. 1. Liquid Precipitation: includs drizzle, rain, and large raindrops. 2. Frozen Snow and Ice : Pure Snow, combining ice crystals, aggregates, and vertically oriented ice structures. 3. Dense and Large frozen hydrometeors: including low- and high-density graupel and dry hail. 4.Melting: Wet Snow and Melting Hail, hydrometeors exhibiting both liquid and frozen characteristics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Rapid SACR Observations of Convection at Bankhead National Forest (RAPID) Field Campaign Report

Improving our representation of convective cell processes requires better quantification of convective clouds throughout their entire life cycle. This includes gaining a clearer understanding of the controls on key convective cloud properties, such as updraft intensity, particle size distributions, rainfall rates, and hydrometeor species. Our inability to improve convective cloud process modeling stems, in part, from a limited understanding of convective cell properties, particularly given how rapidly these storms evolve. This lack of detailed observations in the most intense and organized convective storms is especially significant, as large errors remain in representing these clouds, which are critical for severe weather prediction and Earth system model performance. Cloud and precipitation radars are essential tools for studying cloud microphysics and dynamics, particularly in deeper convective clouds. The recent U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s third Mobile Facility (AMF3) Bankhead National Forest (BNF) deployment provides a unique opportunity to investigate important land–atmosphere interactions, as well as the environmental controls on deep convective cloud processes, in a location favorable for frequent convection. We operate the X/Ka-band Scanning ARM Cloud Radar (X/Ka SACR) using a scan strategy optimized to capture these rapidly evolving clouds and their properties, thereby improving studies of deep convective cloud processes. This effort is strengthened by a complementary and coordinated partnership with ongoing university and multi-agency radar activities collocated in north Alabama—a unique opportunity to examine clouds and precipitation from a lifetime-centric perspective.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review

This article reviews how precipitation microphysics processes are observed in dual-polarization radar observations. These so-called “fingerprints” of precipitation processes are observed as vertical gradients in radar observables. Fingerprints of rain processes are first reviewed, followed by processes involving snow and ice. Then, emerging research is introduced, which includes more quantitative analysis of these dual-polarization radar fingerprints to obtain microphysics model parameters and microphysical process rates. New results based on a detailed rain shaft bin microphysical model are presented, and we conclude with an outlook of potentially fruitful future research directions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft. A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Cloud-Tracking Data Set for the CSAPR2 Adaptive Scanning during TRACER

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility (Mather and Voyles 2013) deployed the first ARM Mobile Facility (AMF1; Miller et al. 2016) near LaPorte, Texas to support the Tracking Aerosol Convection Interactions Experiment (TRACER) (Jensen et al. 2025) near Houston, Texas. From October 2021 to September 2022, AMF1 was deployed to 29.67° N, 95.06° W near LaPorte, Texas and the 2nd Generation C-band Scanning ARM Precipitation Radar (CSAPR2) was deployed to a supplementary site at 29.53° N, 95.28° W (Figure 1). During an intensive operational period (IOP) from 1 June to 30 September 2022, the CSAPR2 sampled precipitation echoes in an adaptive scanning mode following the Multisensor Agile Adaptive Scanning (MAAS) framework (Kollias et al. 2020). MAAS helped optimize the CSAPR2 scan strategy to perform frequent plan position indicator (PPI) and range height indicator (RHI) scans (Lamer et al. 2023). Details of the CSAPR2 scanning, data processing, and calibration procedures used by the principal investigator (PI), and the PI data files are described by Oue et al. (2023). Details of the CSAPR2 operational performance, ARM data processing and correction procedures, and data quality masks are described by Feng et al. (2024a).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Urban Effect on Precipitation and Deep Convective Systems Over Dallas‐Fort Worth

A range of multi-year observational data sets are used to characterize the hydroclimate of the Dallas Fort-Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non-linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight-times the current extent, is much less marked for deep convection with stronger synoptic forcing.

58 GEOSCIENCES↗