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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 OSTI2025

Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data

The Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite developed by the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) launched in May 2024 carries a novel 94 GHz cloud profiling radar (CPR) with Doppler capability. This work describes the open-source instrument simulator Orbital-Radar, which transforms high-resolution radar data from field observations or forward simulations of numerical models to CPR primary measurements and uncertainties. The transformation accounts for sampling geometry and surface effects. We demonstrate Orbital-Radar's ability to provide realistic CPR views of typical cloud and precipitation scenes. The presented case studies show small-scale convection, marine stratus clouds, and Arctic mixed-phase cloud cases. These results provide valuable insights into the capabilities and challenges of the EarthCARE CPR mission and its advantages over the CloudSat CPR. Finally, Orbital-Radar allows for evaluating kilometre-scale numerical weather prediction models with EarthCARE CPR observations. So, Orbital-Radar can generate calibration and validation (Cal/Val) data sets already pre-launch. Nevertheless, an evaluation of synthetic CPR output data to accurate EarthCARE CPR data is missing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

vdisquants: Video Distrometer derived radar equivalent quantities. Retrievals from the VDIS instrument providing radar equivalent quantities, including dual polarization radar quantities (e.g., Z, Differential Reflectivity ZDR).

1. Provide cleaned and filtered data stream from multiple ARM disdrometers; 2. Estimate commonly-used physical parameters of the Drop Size Distribution (DSD). 3. Calculate radar equivalent quantities such as the Radar Reflectivity Factor Z.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

An extended radar relative calibration adjustment (eRCA) technique for higher-frequency radars and range–height indicator (RHI) scans

Abstract. This study extends the relative calibration adjustment technique for calibration of weather radars to higher-frequency radars as well as range–height indicator (RHI) scans. The calibration of weather radars represents one of the most dominant sources of error for their use in a variety of fields including quantitative precipitation estimation and model comparisons. While most weather radars are routinely calibrated, the frequency of calibration is often less than required, resulting in miscalibrated time periods. While full absolute calibration techniques often require the radar to be taken offline for a period of time, there have been online calibration techniques discussed in the literature. The relative calibration adjustment (RCA) technique uses the statistics of the ground clutter surrounding the radar as a monitoring source for the stability of calibration but has only been demonstrated to work at S- and C-band for plan-position indicator (PPI) scans at a constant elevation. In this work the RCA technique is modified to work with higher-frequency radars, including Ka-band cloud radars. At higher frequencies the properties of clutter can be much more variable. This work introduces an extended clutter selection procedure that incorporates the temporal stability of clutter and helps to improve the operational stability of RCA for relatively higher-frequency radars. The technique is also extended to utilize range–height scans from radars where the elevation is varied rather than the azimuth. These types of scans are often utilized with research radars to examine the vertical structure of clouds. The newly extended technique (eRCA) is applied to four Department of Energy Atmospheric Radiation Measurement (DOE ARM) weather radars ranging in frequency from C- to Ka-band. Cross comparisons of three co-located radars with frequencies C, X, and Ka at the ARM Cloud, Aerosol, and Complex Terrain Interactions (CACTI) site show that the technique can determine changes in calibration. Using an X-band radar at the ARM Eastern North Atlantic (ENA) site, we show how the technique can be modified to be more resilient to clutter fields that show increased variability, in this case due to sea clutter. The results show that this technique is promising for a posteriori data calibration and monitoring.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Estimating Ice Water Content for Winter Storms from Millimeter-Wavelength Radar Measurements Using a Synthesis of Polarimetric and Dual-Frequency Radar Observations

The potential of millimeter-wavelength radar-based ice water content (IWC) estimation is demonstrated using a Ka-band Scanning Polarimetric Radar (KASPR) for the U.S. northeast coast winter storms. Two IWC relations for Ka-band polarimetric radar measurements are proposed: one that uses a combination of the radar reflectivity Z and the estimated total number concentration of snow particles N t and the other based on the joint use of Z, specific differential phase K DP , and the degree of riming f rim . A key element of the algorithms is to obtain the “Rayleigh-equivalent” value of Z measured at the Ka band, i.e., the corresponding Z at a longer radar wavelength for which Rayleigh scattering takes place. This is achieved via polarimetric retrieval of the mean volume diameter D m and incorporating the relationship between the dual-wavelength ratio DWR S/Ka and D m . Those techniques allow for retrievals from single millimeter-wavelength radar measurements and do not necessarily require the dual-wavelength ratio (DWR) measurements, if the DWR–D m relation and Rayleigh assumption for Ka-band K DP are valid. Comparison between the quasivertical profile product obtained from KASPR and the columnar vertical profile product generated from the nearby WSR-88D S-band radar measurements demonstrates that the DWR S/Ka can be estimated from the two close radars without the need for collocated radar beams and synchronized antenna scanning and can be used for determining the Rayleigh-equivalent value of Z. As a result, the performance of the suggested techniques is evaluated for seven winter storms using surface disdrometer and snow accumulation measurements.

Cloud retrieval↗
DOE OSTI2020

What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Investigation of Hydrometeor Distributions in Continental and Maritime Storm Systems: Application of Cloud-Resolving Simulations, a Polarimetric Scanning Radar Simulator and Polarimetric Radar Observations

Detailed observations from DOE ARM field campaigns were used to evaluate and improve microphysical and dynamic fields simulated by 3D Cloud Resolving Models (CRMs), employing both bulk and size-resolved bin microphysics. One of the main challenges in bridging the gap between observations and models is placing the analysis in a common framework. To address this, a polarimetric radar simulator was built, consisting of a forward operator to calculate model-consistent polarimetric radar fields, and an analysis package to calculate microphysical and dynamic fields from radar data in a self-consistent manner. The assumptions required for the transformation from model output to radar parameters were carefully considered and tested. It was found that although rain is fairly well captured by the forward model, ice characteristics are difficult to constrain. This polarimetric radar simulator, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), was then applied to WRF simulations and polarimetric C-band radar observations from Darwin, Australia and the SGP site in Oklahoma. These two locations contrast maritime and mid-latitude continental convection respectively. POLARRIS was used with NASA Unified WRF simulations of the same storms in order to compare the observed land and ocean contrasts in microphysics and dynamics. An improved model framework for dynamic downscaling of large-scale aerosol products was also developed and applied to the NASA Unified WRF (NU-WRF) cloud resolving model. This improved framework was used to explore the effects of thermodynamic and aerosol invigoration in deep land and ocean convection through a series of sensitivity studies by perturbating aerosol (CCN) and convective available potential energy (CAPE). It was found that increases in aerosol concentrations yielded increases in low-density graupel in the maritime case and high-density hail in the continental case through riming by increased supercooled water. This result implies that native differences in aerosol concentrations may enhance the inherent contrast in riming effects between deep convective systems in the maritime and continental conditions. Formal publications were generated describing this research.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements

Abstract. A relative calibration technique has been developed for the US Department of Energy's (DOE's) Atmospheric Radiation Measurement (ARM) user facility Ka-band ARM Zenith Radars (KAZRs). This method uses the signal attenuation caused by water on the radome to estimate reflectivity factor (Ze) offsets. The wet-radome attenuation (WRA) is assumed to follow a log-linear relationship with rainfall rate during light and moderate rain, as measured by a collocated surface disdrometer. The technique has an uncertainty of approximately 3 dB, due to factors such as disdrometer measurement error, rain variability between radar and disdrometer sample volumes, and the fitting function's uncertainty for the WRA behavior. A practical advantage of this WRA-based approach to shorter-wavelength radar monitoring is that, while it requires a reference disdrometer, it proves feasible for a wider range of collocated disdrometer measurements compared to traditional direct disdrometer comparison at the onset of light rain. This technique thus offers a cost-effective monitoring tool for remote or long-term radar deployments. This calibration technique was applied during the ARM Tracking Aerosol Convection Interactions Experiment (TRACER) from October 2021 through September 2022. The estimated Ze offsets were compared against traditional radar calibration and monitoring methods using available datasets from this campaign. Results show that the WRA-based offsets align closely with mean offsets found between cloud radars and from direct disdrometer comparison near the onset of rain, while also reflecting similar offset and campaign-long trends when compared to collocated, independently calibrated radar wind profilers. Nevertheless, overall, the KAZR Ze offsets estimated during TRACER remained stable at approximately 2 dB lower than the disdrometer estimates from the campaign start until the end of June 2022; afterward, the offsets increased to around 7 dB by the campaign's end. This increase is linked to a drop of about 1 dB in transmitter power toward the end of the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Convolutional Neural Networks for Hydrometeor Classification using Dual Polarization Doppler Radars

Traditional fuzzy logic hydrometeor classification algorithm is a common way to classify precipitation type from dual polarization doppler radar. We propose a deep learning-based method to estimate hydrometeors efficiently using observed radar variables such as horizontal reflectivity (Z H ), differential reflectivity (Z DR ), correlation coefficient (ρ HV ) and specific differential phase (K DP ) from National Weather Service NEXRAD collected at Vance AFB facility at the first elevation angle from January 1st, 2015 to July 31th, 2019. We stack matrices of values from these four polarimetric variables as one 3D array. Samples are preprocessed and divided into training, validation and test set with four target hydrometeor categories (Ice Crystals (IC), Dry Snow (DS), Light and/or Moderate Rain (RA) and Big Drops (rain) (BD)). We developed and optimized five Convolutional Neural Networks (CNNs) architectures and achieved an accuracy of 87.23% and 93.736% respectively using modified ResNet with two different input data selection strategies for hydrometeor classification. Training data selection strategies were important to ensure use of available samples in training for robust performance evaluated by applying the models to novel time period beyond what was use to train the model. Seasonal variation in atmospheric conditions lead to seasonal patterns of liquid vs solid forms of precipitation, that poses challenge for classifier and offer insights into domain specific approaches required for problem of hydrometeor identification. Computationally efficient and scalable approach for classification of hydrometeors offer opportunties to effectively use the large volumes of rich time series of radar observations that are becoming increasingly available.

Lu, Yuping↗
DOE OSTI2021

Using radar observations to evaluate 3-D radar echo structure simulated by the Energy Exascale Earth System Model (E3SM) version 1

Abstract. The Energy Exascale Earth System Model (E3SM) developed by the Department of Energy has a goal of addressing challenges in understanding the global water cycle. Success depends on correct simulation of cloud and precipitation elements. However, lack of appropriate evaluation metrics has hindered the accurate representation of these elements in general circulation models. We derive metrics from the three-dimensional data of the ground-based Next-Generation Radar (NEXRAD) network over the US to evaluate both horizontal and vertical structures of precipitation elements. We coarsened the resolution of the radar observations to be consistent with the model resolution and improved the coupling of the Cloud Feedback Model Intercomparison Project Observation Simulator Package (COSP) and E3SM Atmospheric Model Version 1 (EAMv1) to obtain the best possible model output for comparison with the observations. Three warm seasons (2014–2016) of EAMv1 simulations of 3-D radar reflectivity features at an hourly scale are evaluated. A general agreement in domain-mean radar reflectivity intensity is found between EAMv1 and NEXRAD below 4 km altitude; however, the model underestimates reflectivity over the central US, which suggests that the model does not capture the mesoscale convective systems that produce much of the precipitation in that region. The shape of the model-estimated histogram of subgrid-scale reflectivity is improved by correcting the microphysical assumptions in COSP. Different from previous studies that evaluated modeled cloud top height, we find the model severely underestimates radar reflectivity at upper levels – the simulated echo top height is about 5 km lower than in observations – and this result is not changed by tuning any single physics parameter. For more accurate model evaluation, a higher-order consistency between the COSP and the host model is warranted in future studies.

58 GEOSCIENCES↗
DOE OSTI2020

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar datasets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective up- and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size-intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ~ 0.5), and increases in draft intensity with altitude. A model-observational intercomparison for draft properties (core width, intensity, mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (?x) that range from 4 km to 250 m. It is shown that the simulations performed at ?x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.

radar, arm, wind profiler, goamazon↗
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 OSTI2026

BNF C-band Scanning ARM Precipitation Radar 2nd Generation (CSAPR-2) Extracted Radar Columns and In-Situ Sensors (RadCLss)

Corrected Moments in Antenna Coordinates (CMAC) calculates quantitative precipitation estimates (QPE) from empirical relationships based on equivalent radar reflectivity factor, specific differ- ential phase, and specific attenuation. To evaluate these empirical relationships, the Extracted Radar Columns and In-Situ Sensors (RADclss) product was developed. Utilizing Py-ART, RAD- clss extracts CMAC radar columns above various ARM and partner locations. These columns are then spatiotemporally synced with in-situ observations at the surface utilizing the Atmospheric data Community Toolkit (ACT; Theisen et al. 2025), allowing direct comparison of radar parameters with rain gauges and laser disdrometers for further investigation.

bankhead↗
DOE OSTI2026

Multi-Doppler radar analysis from CSAPR, CHIVO, COW, and RMA-1 radars during the CACTI/RELAMPAGO experiments in Argentina in 2018

This data set contains multi-Doppler radar analysis from CSAPR-2, CSU-CHIVO, COW, and RMA-1 radars. These radars were collecting dual-polarization data during the CACTI/RELAMPAGO experiments in Argentina in 2018. Doppler analysis is systematically conducted for 31 days with convection. Three-dimensional wind fields are retrieved using the PyDDA algorithm. Dual-polarization information is also included in the data set.

3D cartesian gridded corrected mean Doppler veloci↗
DOE OSTI2020

Synergetic Use of the WSR-88D Radars, GOES-R Satellites, and Lightning Networks to Study Microphysical Characteristics of Hurricanes

Abstract This study analyzes the microphysics and precipitation pattern of Hurricanes Harvey (2017) and Florence (2018) in both the eyewall and outer rainband regions. From the retrievals by a satellite red–green–blue scheme, the outer rainbands show a strong convective structure while the inner eyewall has less convective vigor (i.e., weaker upper-level reflectivities and electrification), which may be related to stronger vertical wind shear that hinders fast vertical motions. The WSR-88D column-vertical profiles further confirm that the outer rainband clouds have strong vertical motion and large ice-phase hydrometeor formation aloft, which correlates well with 3D Lightning Mapping Array source counts in height and time. From the results from this study, it is determined that the inner eyewall region is dominated by warm rain, whereas the external rainband region contains intense mixed-phase precipitation. External rainbands are defined here as those that reside outside of the main hurricane circulation, associated with surface tropical storm wind speeds. The synergy of satellite and radar dual-polarization parameters is instrumental in distinguishing between the key microphysical features of intense convective rainbands and the warm-rain-dominated eyewall regions within the hurricanes. Substantial amounts of ice aloft and intense updrafts in the external rainbands are indicative of heavy surface precipitation, which can have important implications for severe weather warnings and quantitative precipitation forecasts. The novel part of this study is to combine ground-based radar measurement with satellite observations to study hurricane microphysical structure from surface to cloud top so as to fill in the gaps between the two observational techniques.

Meteorology & Atmospheric Sciences↗
DOE OSTI2026

GPM IMERG V07B and V06B: Evaluation Using Ground-Based Radar Observations and Application in Global Mesoscale Convective System Tracking

This study evaluates the latest Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG V07B) against its predecessor V06B, for studying mesoscale convective systems (MCSs). Both versions are compared using ground-based radar and rain gauge data from five meteorologically diverse regions: the contiguous United States (including eastern coastlines), Amazon rainforest, central Argentina mountains, equatorial Indian Ocean, and northern Australia across multiple temporal (0.5–6 hours) and spatial scales (0.1°–0.25°). An updated global MCS tracking dataset is developed by integrating satellite-observed infrared brightness temperature with IMERG V07B. Comparation of IMERG against radar observations reveals that IMERG demonstrates better performance in capturing the probability distribution and quantitative contributions of rainfall (from no-rain to intense-rain conditions) over tropical oceans than over land, with marked improvements in IMERG V07B for heavy-to-intense rain (> 10 mm h-1). Over land, systematic biases persist: IMERG tends to overestimate light-to-moderate rain (1–10 mm h-1) while underestimating heavy-to-intense rain. Additionally, aggregating IMERG to coarser resolutions (3-hourly or 0.25°) improves consistency with radar observations, outperforming the 1-hourly/0.1° resolution. The new IMERG V07B-based global MCS dataset exhibits consistent statistical characteristics with the V06B-based dataset, despite lower mean rain rates and reduced heavy precipitation contributions. These findings offer valuable insights for utilizing IMERG V07B in global precipitation studies, MCS characterization, and model evaluation.

Zhang, Sihan↗
DOE OSTI2020

Radar Motion Measurements and Synthetic Aperture Radar Image Geolocation Accuracy

Once a Synthetic Aperture Radar (SAR) image is formed, the natural question then is, "Where is this image?" and/or "Where exactly is this feature displayed in the image?" Thus, geolocation is an important exploitation of the SAR image. Since SAR measures relative location to its own position, it is crucial to understand how the radars position and motion imp acts the ability to geolocate a feature in the SAR image. Furthermore, accuracy and precision of navigation aids like GPS directly impact the goodness of the geolocation solution. These relationships are developed and discussed.

58 GEOSCIENCES↗
DOE OSTI2023

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar: SUMO Default vs. Radar-Measured CF model Parameters

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Schrader, Max (ORCID:0000000336737672)↗
DOE OSTI2021

Mixed‐Phase Clouds Over the Southern Ocean as Observed From Satellite and Surface Based Lidar and Radar

Abstract This study investigates the occurrence of mixed‐phase clouds (MPC, i.e., cloud layers containing both liquid and ice water at sub‐freezing temperatures) over the Southern Ocean (SO) using space‐ and surface‐based lidar and radar observations. The occurrence of supercooled clouds is dominated by geometrically thin (<1 km) layers that rarely contain ice. We diagnose layers that are geometrically thicker than 1 km to contain ice ~65% and ~4% of the time from below by surface remote sensors and from above by orbiting remote sensors, respectively. We examine the discrepancy in MPC occurrence statistics as diagnosed from below and above the cloud layer. From above, we find that MPC occurrence has a gradient associated with the Antarctic Polar Front near 55°S with a rare occurrence of satellite‐derived MPC south of that latitude. In contrast, surface sensors find ice in 33% of supercooled liquid water layers. We infer using observing system simulation experiments and data analysis that space‐based lidar cannot identify the occurrence of MPC except when secondary ice‐forming processes operate in convection that is, sufficiently strong to loft ice crystals to cloud tops. We conclude that the CALIPSO phase statistics of MPC have a severe low bias in MPC occurrence. Based on surface‐based statistics in the SO, we present a parameterization of the frequency of MPC as a function of cloud top temperature that differs substantially from that used in recent climate model simulations.

54 ENVIRONMENTAL SCIENCES↗