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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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18 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Detection of multi-modal Doppler spectra – Part 1: Establishing characteristic signals in radar moment data

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.

Wugofski, Sarah [Pennsylvania State Univ., Univers↗
NASA NTRS2015

High Ice Water Content at Low Radar Reflectivity near Deep Convection: Consistency of In Situ and Remote-Sensing Observations with Stratiform Rain Column Simulations - Part I

Occurrences of jet engine power loss and damage have been associated with flight through fully glaciated deep convection at -10 to -50 degrees Centigrade. Power loss events commonly occur during flight through radar reflectivity (Zeta (sub e)) less than 20-30 decibels relative to Zeta (dBZ - radar returns) and no more than moderate turbulence, often overlying moderate to heavy rain near the surface. During 2010-2012, Airbus carried out flight tests seeking to characterize the highest ice water content (IWC) in such low-radar-reflectivity regions of large, cold-topped storm systems in the vicinity of Cayenne, Darwin, and Santiago. Within the highest IWC regions encountered, at typical sampling elevations (circa 11 kilometers), the measured ice size distributions exhibit a notably narrow concentration of mass over area-equivalent diameters of 100-500 micrometers. Given substantial and poorly quantified measurement uncertainties, here we evaluate the consistency of the Airbus in situ measurements with ground-based profiling radar observations obtained under quasi-steady, heavy stratiform rain conditions in one of the Airbus-sampled locations. We find that profiler-observed radar reflectivities and mean Doppler velocities at Airbus sampling temperatures are generally consistent with those calculated from in situ size-distribution measurements. We also find that column simulations using the in situ size distributions as an upper boundary condition are generally consistent with observed profiles of radar reflectivity (Ze), mean Doppler velocity (MDV), and retrieved rain rate. The results of these consistency checks motivate an examination of the microphysical pathways that could be responsible for the observed size-distribution features in Ackerman et al. (2015).

Rain↗
NASA NTRS2022

Mind-the-Gap - Part 3: Doppler Velocity Measurements From Space

Convective motions and hydrometeors microphysical properties are highly sought-after parameters for evaluating atmospheric numerical models. With most of the Earth’s surface covered by water, space-borne Doppler radars are ideal for acquiring such measurements at a global scale. While these systems have proven to be useful tools for retrieving cloud microphysical and dynamical properties from the ground, their adequacy, and specific requirements for spaceborne operation still need to be evaluated. Comprehensive forward simulations enable us to assess the advantages and drawbacks of six different Doppler radar architectures currently planned or under consideration by space agencies for the study of cloud dynamics. Radar performance is examined against state-of-the-art numerical model simulations of well-characterized shallow and deep, continental, and oceanic convective cases. Mean Doppler velocity (MDV) measurements collected at multiple frequencies (13, 35 and 94 GHz) provide complementary information in deep convective cloud systems. The high penetration capability of the 13-GHz radar enables to obtain a complete, albeit horizontally under-sampled, view of deep convective storms. The smaller instantaneous field of view (IFOV) of the 35-GHz radar captures more precise information about the location and size of convective updrafts above 5-8 km height of most systems which was determined is the portion of storms where the mass flux peak is typically located. Finally, the lower mean Doppler velocity uncertainty of displaced phase center antenna (DPCA) radars makes them an ideal system for studying microphysics in shallow convection and frontal systems, as well as ice and mixed-phase clouds. It is demonstrated that a 94-GHz DCPA system can achieve retrieval errors as low as 0.05-0.15 mm for raindrop volume-weighted mean diameter and 25% for rime fraction (for a -10 dBZ echo).

Pavlos Kollias↗
DOE OSTI2023

MICRE W-band radar reflectivity and Doppler velocity

Daily files containing radar reflectivity and mean Doppler velocity collected from vertically pointing W-band radar during the Macquarie Island Cloud & Radiation Experiment (MICRE): April 2016 to March 2017.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Micro Rain Radar Pro Data at the Argonne Testbed for Multiscale Observational Science obtained during the CROCUS Urban Integrated Field Laboratory

The Micro Rain Radar Pro (MRR-PRO) is a vertically pointing Ka-band Doppler radar designed to capture the fine-scale structure and evolution of precipitation. By recording the full Doppler spectrum at high temporal and spatial resolution, the MRR-PRO provides insight into both hydrometeor fall velocities and precipitation microphysics. From these spectra, key moments—reflectivity, mean Doppler velocity, spectral width, and rainfall rate—are derived and stored alongside the raw spectral data in CF/Radial 1.4-compliant files. Deployed at the Argonne Testbed for Multiscale Observational Studies (ATMOS) since November 2024, the MRR-PRO delivers vertical profiles at 70 m range resolution extending up to 4.5 km above ground level. These observations enable detailed analyses of precipitation type, intensity, and vertical structure, supporting process-level studies of cloud and precipitation dynamics in diverse weather regimes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A thermal-driven graupel generation process to explain dry-season convective vigor over the Amazon

Large-eddy simulations (LESs) are conducted for each day of the intensive observation periods (IOPs) of the Green Ocean Amazon (GoAmazon) field campaign to characterize the updrafts and microphysics within deep convective cores while contrasting those properties between Amazon wet and dry seasons. Mean Doppler velocity (V dop ) values simulated using LESs are compared with 2-year measurements from a radar wind profiler (RWP) as viewed by statistical composites separated according to wet- and dry-season conditions. In the observed RWP and simulated LES V dop composites, we find more intense low-level updraft velocity, vigorous graupel generation, and intense surface rain during the dry periods compared with the wet periods. To investigate coupled updraft–microphysical processes further, single-day golden cases are selected from the wet and dry periods to conduct detailed cumulus thermal tracking analysis. Tracking analysis reveals that simulated dry-season environments generate more droplet-loaded low-level thermals than wet-season environments. This tendency correlates with seasonal contrasts in buoyancy and vertical moisture advection profiles in large-scale forcing. Employing a normalized time series of mean thermal microphysics, the simulated cumulus thermals appear to be the primary generator of cloud droplets. When subsequent thermals penetrate the ice crystal layer, droplets within the thermals interact with entrained ice crystals, which enhances riming in the thermals. This appears to be a production pathway of graupel/hail particles within simulated deep convective cores. In addition, less-diluted dry-case thermals tend to be elevated higher, and graupel grows further during sedimentation after spilling out from thermals. Therefore, greater concentrations of low-level moist thermals likely result in more graupel/hail production and associated dry-season convective vigor.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A radar view of ice microphysics and turbulence in Arctic cloud systems

Ice microphysical processes are inherently complex because of their sensitivity to temperature and humidity, the diversity of ice crystal habits, and their interaction with supercooled liquid water (SCL) and turbulence. Long-term surface-based radar observations have been systematically used to unravel the different processes that affect ice particle growth. In this study, we present a statistical analysis of 6.5 years of Ka-band radar observations in Arctic cloud systems, combined with thermodynamic profiles derived from radiosonde measurements. For the first time, ice particle growth and sublimation – diagnosed from vertical gradients of radar reflectivity and mean Doppler velocity – are systematically mapped across a broad range of temperature and moisture conditions. These vertical gradients correspond closely to saturation levels relative to ice and exhibit a strong temperature dependence in supersaturated regions. Notably, distinct signatures near −15 °C are indicative of dendritic growth. Turbulence, quantified via the eddy dissipation rate (EDR), is most frequently observed in regions containing SCL. The co-occurrence of SCL and elevated turbulence results in significantly enhanced ice particle growth compared to conditions in which either is present alone. This work provides new observational constraints that are critical for improving the representation of ice microphysics in atmospheric models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2023

Leipzig University W-Band Cloud Radar, Gothic (Colorado), SAIL Campaign Second Winter (15.11.2022 - 05.06.2023)

The instrument is a polarimetric W-Band simultaneous transmission simultaneous reception (STSR) Doppler Cloud Radar manufactured by Radiometer Physics GmbH (RPG, instrument type RPG-FMCW-94-DP). It was deployed about 500m away from the ARM M1 facility in Gothic (CO) near the Ore House between Nov 15, 2022 and Jun 5, 2023 at an altitude of 2913 m. The Doppler cloud radar was mounted on a prototype of a cold temperature scanner manufactured by RPG. The dataset contains common radar variables like equivalent radar reflectivity factor, mean Doppler velocity, Doppler spectrum width as well as common polarimetric radar variables like differential phase shift (PhiDP), specific differential phase shift (KDP), correlation coefficient (RHV) and differential reflectivity (ZDR). Between Nov 15, 2022, and Feb 7, 2023 the radar was operated at 40° constant elevation and 151° azimuth (towards the KAZR at the AMF1). Between Feb 7 and Mar 15, 2023 the following scan cycle was repeated in 15min intervals: zenith pointing measurements, RHI scans from 0° to 90° and 90° to 0° elevation at 151° azimuth, constant elevation measurements at 40° elevation and 151° azimuth and PPI scans at 85° elevation for wind retrievals (one PPI per hour). Between Mar 15 18:18 UTC and Apr 8, 2023 11:04 UTC, the scanner was stuck at 72° elevation, which resulted in corrupted files for this period. For that period, the files containing data are uploaded but should be treated with care. Between Apr 8 and Apr 14, 2023 the scanning pattern described above was performed again. Afterwards, except for May 17 and May 18, 2023 where scanning patterns as described above were performed, measurements continued at 40° elevation and 151° azimuth until May 22, 2023 09:00 UTC. The scanning pattern was then started again, but without hourly PPI measurements until May 31, 2023. Between May 31 and Jun 5, 2023 solely zenith-pointing observations were performed. The data provided here are the RPG Level 1 (L1) files, Doppler spectra Level 0 (L0) files are available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2005

Retrieval of Raindrop Size Distribution, Vertical Air Velocity and Water Vapor Attenuation Using Dual-Wavelength Doppler Radar Observations

Two techniques for retrieving the slope and intercept parameters of an assumed exponential raindrop size distribution (RSD), vertical air velocity, and attenuation by precipitation and water vapor in light stratiform rain using observations by airborne, nadir looking dual-wavelength (X-band, 3.2 cm and W-band, 3.2 mm) radars are presented. In both techniques, the slope parameter of the RSD and the vertical air velocity are retrieved using only the mean Doppler velocities at the two wavelengths. In the first method, the intercept of the RSD is estimated from the observed reflectivity at the longer wavelength assuming no attenuation at that wavelength. The attenuation of the shorter wavelength radiation by precipitation and water vapor are retrieved using the observed reflectivity at the shorter wavelength. In the second technique, it is assumed that the longer wavelength suffers attenuation only in the melting band. Then, assuming a distribution of water vapor, the melting band attenuation at both wavelengths and the rain attenuation at the shorter wavelength are retrieved. Results of the retrievals are discussed and several physically meaningful results are presented.

Heymsfield, Gerald M.↗
NASA NTRS2008

Retrieval of Snow and Rain From Combined X- and W-B and Airborne Radar Measurements

Two independent airborne dual-wavelength techniques, based on nadir measurements of radar reflectivity factors and Doppler velocities, respectively, are investigated with respect to their capability of estimating microphysical properties of hydrometeors. The data used to investigate the methods are taken from the ER-2 Doppler radar (X-band) and Cloud Radar System (W-band) airborne Doppler radars during the Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment campaign in 2002. Validity is assessed by the degree to which the methods produce consistent retrievals of the microphysics. For deriving snow parameters, the reflectivity-based technique has a clear advantage over the Doppler-velocity-based approach because of the large dynamic range in the dual-frequency ratio (DFR) with respect to the median diameter Do and the fact that the difference in mean Doppler velocity at the two frequencies, i.e., the differential Doppler velocity (DDV), in snow is small relative to the measurement errors and is often not uniquely related to Do. The DFR and DDV can also be used to independently derive Do in rain. At W-band, the DFR-based algorithms are highly sensitive to attenuation from rain, cloud water, and water vapor. Thus, the retrieval algorithms depend on various assumptions regarding these components, whereas the DDV-based approach is unaffected by attenuation. In view of the difficulties and ambiguities associated with the attenuation correction at W-band, the DDV approach in rain is more straightforward and potentially more accurate than the DFR method.

Liao, Liang↗
NASA NTRS2015

High Ice Water Content at Low Radar Reflectivity near Deep Convection: Part II. Evaluation of Microphysical Pathways in Updraft Parcel Simulations

The aeronautics industry has established that a threat to aircraft is posed by atmospheric conditions of substantial ice water content (IWC) where equivalent radar reflectivity (Ze) does not exceed 20-30 dBZ and supercooled water is not present; these conditions are encountered almost exclusively in the vicinity of deep convection. Part 1 (Fridlind et al., 2015) of this two-part study presents in situ measurements of such conditions sampled by Airbus in three tropical regions, commonly near 11 km and -43 C, and concludes that the measured ice particle size distributions are broadly consistent with past literature with profiling radar measurements of Z(sub e) and mean Doppler velocity obtained within monsoonal deep convection in one of the regions sampled. In all three regions, the Airbus measurements generally indicate variable IWC that often exceeds 2 gm (exp -3) with relatively uniform mass median area-equivalent diameter (MMD(sub eq) of 200-300 micrometers. Here we use a parcel model with size-resolved microphysics to investigate microphysical pathways that could lead to such conditions. Our simulations indicate that homogeneous freezing of water drops produces a much smaller ice MMD(sub eq) than observed, and occurs only in the absence of hydrometeor gravitational collection for the conditions considered. Development of a mass mode of ice aloft that overlaps with the measurements requires a substantial source of small ice particles at temperatures of about -10 C or warmer, which subsequently grow from water vapor. One conceivable source in our simulation framework is Hallett-Mossop ice production; another is abundant concentrations of heterogeneous ice freezing nuclei acting together with copious shattering of water drops upon freezing. Regardless of the production mechanism, the dominant mass modal diameter of vapor-grown ice is reduced as the ice-multiplication source strength increases and as competition for water vapor increases. Both mass and modal diameter are reduced by entrainment and by increasing aerosol concentrations. Weaker updrafts lead to greater mass and larger modal diameters of vapor-grown ice, the opposite of expectations regarding lofting of larger ice particles in stronger updrafts. While stronger updrafts do loft more dense ice particles produced primarily by raindrop freezing, we find that weaker updrafts allow the warm rain process to reduce competition for diffusional growth of the less dense ice expected to persist in convective outflow.

Reflectivity↗
DOE OSTI2023

Doppler spectra from ARM Doppler Lidar during ARMing the Edge

There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. In this dataset, we provide the Doppler spectra from the ARM Doppler lidar at C1 recorded in April 2022 during ARMing the Edge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

CSAPR2 cell-tracking data collected during TRACER

One of the challenges of analyzing convective cell properties is quick evolution of the individual convective cells. While the operational radar data provide great a data set to analyze the evolution of radar observables of convective precipitation clouds statistically, previous studies also suggested that, because of the quick evolution of cell life cycle, conventional radar volume scan strategies taking ~5-7 minutes might not capture the detailed evolution. The TRACER campaign deployed CSAPR2, which performed frequent update of RHI and sector PPI scans to track convective cells every < 2 minutes guided by a new cell-tracking framework, Multisensor Agile Adaptive Sampling (MAAS; Kollias et al. 2020). This allows for capturing fast-evolving radar observables. The submitted data files are CSAPR2 data in CfRadial format collected during the TRACER field campaign from June to September 2020. The data files include processed radar variables including: noise-masked reflectivity and differential reflectivity corrected for rain attenuation and systematic biases, noise-masked dealiased radial velocity, specific differential phase, locations of target cells (latitude, longitude, radar range), and radar-echo classification.

54 ENVIRONMENTAL SCIENCES↗
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↗