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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 OSTI2014

First observations of tracking clouds using scanning ARM cloud radars

Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

POPSnet-SGP: A Pilot Aerosol Microphysics Network for Targeting Climate Model Uncertainty Interim Field Campaign Report

Aerosols mediate the radiative fluxes in clear and cloudy skies and dominate the uncertainty in the radiative forcing of climate. Relatively dense networks of aerosol optical depth measurements have been used to effectively constrain simulated aerosol optical properties, but model diversity of aerosol microphysical properties is much larger (Mann et al. 2014, Myhre et al. 2009). A better understanding of aerosol microphysics is essential as they are the fundamental pieces of information required to convert aerosol emissions information to radiative and cloud-nucleating properties that drive radiative effects and forcing of climate. Model representation of aerosol microphysical properties has lagged in part due to their inherently greater spatial variability, but also due to a lack of available observations. This project – the Printed Optical Particle Spectrometer Network (POPSnet)-Southern Great Plains (SGP) Pilot – launched the first spatially dense network of aerosol size distribution measurements over an area the size of a global model grid cell and demonstrated its use in providing valuable information regarding the spatial variability of aerosol microphysical properties and its drivers for improving model constraints.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Observing Profiles of Derived Kinematic Field Quantities Using a Network of Profiling Sites

Abstract Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely, advection, vorticity, divergence, and deformation, can be obtained by applying Green’s theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50–80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A novel network-based approach to determining measurement representation error for model evaluation of aerosol microphysical properties

Atmospheric aerosol size and abundance influence radiative effects and climate change. To date, efforts to constrain global climate models’ radiative forcing with in situ aerosol observations have been hamstrung by uncertainty. One source of error, the regional “representation error,” arises when accurate but sparse single-point measurements of atmospheric aerosol distributions are compared with a model value, assuming that the single-point measurement is representative of the model domain. The Portable Optical Particle Spectrometer network in the Southern Great Plains (POPSnet-SGP) campaign has demonstrated that a network of nearly autonomous aerosol instruments operating at ambient temperature and relative humidity (with low measurement error) may be used to quantify measurement representation error and investigate the factors introducing heterogeneity in aerosol distributions across a rural, continental background region. Measurements were made using Portable Optical Particle Spectrometer (POPS) instruments at several sites for five months across the Department of Energy’s Aerosol Radiation Measurement Southern Great Plains (ARM-SGP) User Facility in the central USA. Measurement representation error decreased with longer averaging periods (20-40 % between 1 sec and 1 day), varied between sites by 10 – 20 % for aerosol concentration 140 – 2500 nm in diameter (N_140), and was higher for aerosols > 400 nm in diameter (N_400). Our measurements also show the influence of local meteorology on aerosol surface area (A_140) and size distributions: A_140 is positively correlated with wind speed and relative humidity, negatively correlated with precipitation, and lower given westerly winds. Based on this study, we conclude that the POPSnet approach provides considerably more insight into the spatial variability in the aerosol population that can be used to constrain climate models than would be available from similar networks of PM 2.5 monitors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E

The US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site includes a heterogeneous distributed scanning Doppler radar network suitable for collecting coordinated Doppler velocity measurements in deep convective clouds. The surrounding National Weather Service (NWS) Next Generation Weather Surveillance Radar 1988 Doppler (NEXRAD WSR-88D) further supplements this network. Radar velocity measurements are assimilated in a three-dimensional variational (3DVAR) algorithm that retrieves horizontal and vertical air motions over a large analysis domain (100 km × 100 km) at storm-scale resolutions (250 m). For the first time, direct evaluation of retrieved vertical air velocities with those from collocated 915 MHz radar wind profilers is performed. Mean absolute and root-mean-square differences between the two sources are of the order of 1 and 2 m s -1 , respectively, and time–height correlations are of the order of 0.5. An empirical sensitivity analysis is done to determine a range of 3DVAR constraint weights that adequately satisfy the velocity observations and anelastic mass continuity. It is shown that the vertical velocity spread over this range is of the order of 1 m s -1 . The 3DVAR retrievals are also compared to those obtained from an iterative upwards integration technique. Lastly, the results suggest that the 3DVAR technique provides a robust, stable solution for cases in which integration techniques have difficulty satisfying velocity observations and mass continuity simultaneously.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

ARM Carbon Cycle Gases Flasks at SGP Site

Data from flasks are sampled at the Atmospheric Radiation Measurement Program ARM, Southern Great Plains Site and analyzed by the National Oceanic and Atmospheric Administration NOAA, Earth System Research Laboratory ESRL. The SGP site is included in the NOAA Cooperative Global Air Sampling Network. The surface samples are collected from a 60 m tower at the ARM SGP Central Facility, usually once per week in the afternoon. The aircraft samples are collected approximately weekly from a chartered aircraft, and the collection flight path is centered over the tower where the surface samples are collected. The samples are collected by the ARM and LBNL Carbon Project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Surface Cloud Grid Version 2 (SFCCLDGRID2) Value-Added Product: Description of Update"

This document describes the algorithm used for the Surface Cloud Grid Value-Added Product (VAP). This VAP uses as input the 15-min. output from the Shortwave (SW) Flux Analysis VAP (see Long 2001; Long and Ackerman 2000; Long et al. 1999) from the Atmospheric Radiation Measurement (ARM) Climate Reseach Facility (ACRF) Southern Great Plains (SGP) Central Facility and extended facilities. This network of 21 sites is unevenly spaced over northern Oklahoma into southern Kansas, covering an area from 95.5° to 99.5° west longitude and 34.5° to 38.5° north latitude. For research applications such as single-column modeling, an estimate of the cloud and cloud effects distribution over this entire domain is desirable. The Surface Cloud Grid VAP applies a multi-pass weighted sum analytic approximation technique (Caracena 1987), which uses Gaussian weighting and an imposed scale length, to interpolate to a 0.25° by 0.25° lat/long grid over the SGP domain. The output, like the input, includes solar elevation angles of 10° or greater.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

GNSS Installation at the ARM Southern Great Plains (SGP) Atmospheric Observatory Field Campaign Report

GRUAN, the GCOS Reference Upper Air Network, is an international reference observing network of sites measuring essential climate variables above Earth's surface, designed to fill an important gap in the current global observing system. GRUAN’s objectives are to (1) provide long-term, high-quality climate records, (2) constrain and calibrate data from more spatially comprehensive global observing systems (including satellites and current radiosonde networks), and (3) fully characterize the properties of the atmospheric column.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗
DOE OSTI2026

SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report

SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Micropulse Differential Absorption Lidar (MPD) Network Demonstration Field Campaign Report

The National Center for Atmospheric Research (NCAR) and Montana State University (MSU) are nearing completion of a National Science Foundation (NSF) major research instrumentation grant to build five micropulse differential absorption lidars (MPDs) for water vapor profiling. The first demonstration of the MPD configured as a five-unit network occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) field sites near Lamont, Oklahoma from 17 April–21 July 2019 for a total of 96 days of operation. The field deployment was funded in part by DOE (which provided support for the MPD installation, power at the field sites, personnel to launch 3-hourly radiosondes from the Central Facility, and the cost of the radiosondes themselves), internal NCAR funds (to support the MPD shipping costs and NCAR staff travel expenses), and NSF/Major Research Instrumentation (MRI) funds (to support the MSU travel expenses to participate in the instrument setup).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Cloud fraction at the ARM SGP site: Reducing uncertainty with self-organizing maps

Instrument downtime leads to uncertainty in the monthly and annual record of cloud fraction (CF), making it difficult to perform time series analyses of cloud properties and perform detailed evaluations of model simulations. As cloud occurrence is partially controlled by the large-scale atmospheric environment, this knowledge is used to reduce uncertainties in the instrument record. Synoptic patterns diagnosed from the North American Regional Reanalysis (NARR) during the period 1997–2010 are classified using a competitive neural network known as the self-organizing map (SOM). The classified synoptic states are then compared to the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) instrument record to determine the expected CF. A number of SOMs are tested to understand how the number of classes and the period of classifications impact the relationship between classified states and CFs. Bootstrapping is utilized to quantify the uncertainty of the instrument record when statistical information from the SOM is included. Although all SOMs significantly reduce the uncertainty of the CF record calculated in Kennedy et al. (Theor Appl Climatol 115:91–105, 2014), SOMs with a large number of classes and separated by month are required to produce the lowest uncertainty and best agreement with the annual cycle of CF. Lastly, this result may be due to a manifestation of seasonally dependent biases in NARR.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Multi-EM27/SUN Total Carbon Column Observing Network (TCCON) Comparison at the Southern Great Plains Site Field Campaign Report

During the summer of 2015, a field campaign took place to help characterize off-the-shelf portable solar-viewing Fourier Transform Spectrometer (FTS) instruments (EM27/SUN). These instruments retrieve greenhouse gas (GHG) abundances from direct solar spectra. A focus of this campaign was to test possible dependence on different atmospheric conditions. Along with the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site in Oklahoma, experiments were conducted in Pasadena, California; Park Falls, Wisconsin; and the Armstrong Flight Research Center (AFRC), California. These locations are home to instruments in the Total Column Carbon Observing Network (TCCON). TCCON measurements were used as standards for the portable (EM27/SUN) measurements. Comparisons between the two types of instruments are crucial in the attempt to use the portable instruments to broaden the capabilities of GHG measurements for monitoring, reporting, and verification of carbon in the atmosphere. This campaign was aimed at testing the response of the portable FTS to different atmospheric conditions both local and regional. Measurements made at ARM SGP provided data in an agricultural environment with a relatively clean atmosphere with respect to pollution. Due to the homogeneity of the region surrounding Lamont, Oklahoma, portable FTS measurements were less effected by large changes in column GHG abundances from air mass movement between regions. These conditions aided in characterizing potential artificial solar zenith angle dependence of the retrievals. Data collected under atmospheric conditions at ARM SGP also provide for the analysis of cloud interference on solar spectra. In situ measurements were also made using a Picarro isotopic methane analyzer to determine surface-level in situ GHG concentrations and possible influences due to local agriculture and nearby towns. Data collected in this campaign have been presented via a poster at the American Geophysical Union Fall Meeting in 2015 and is included in a paper that is in preparation to be submitted to Atmospheric Measurement Techniques in 2016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Data Management Facility Operations Plan

The Data Management Facility (DMF) is the data center that houses several critical Atmospheric Radiation Measurement (ARM) Climate Research Facility services, including first-level data processing for the ARM Mobile Facilities (AMFs), Eastern North Atlantic (ENA), North Slope of Alaska (NSA), Southern Great Plains (SGP), and Tropical Western Pacific (TWP) sites, as well as Value-Added Product (VAP) processing, development systems, and other network services.

Keck, Nicole N↗
DOE OSTI2019

Precipitation Meteorological Instruments (PRECIPMET) Handbook

The PRECIPMET surface meteorology instrument system provides temperature, relative humidity, pressure, wind speed and direction, and precipitation data relevant to the precipitation network instruments: laser disdrometer (PARS2) and radar wind profiler (RWP). The PRECIPMET is installed at the intermediate facilities (IF) of the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory. The instrument system consists of a Vaisala WXT520 weather transmitter and a 12”-diameter Novalynx tipping bucket rain gauge (TBRG). The WXT520 is mounted on top of the control building at a height of approximately 4 meters. The tipping bucket is mounted on a pole level with the surrounding fencing (approximately 2 meters) to prevent physical interference with the gauge.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Aerosol Direct Radiative Effects at the ARM SGP and TWP Sites: Clear Skies

The clear-sky aerosol direct radiative effect (DRE) was estimated at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites. The NASA Langley Fu-Liou radiation model was used with observed inputs including aerosol vertical extinction profile from the Raman lidar; spectral aerosol optical depth (AOD), single-scattering albedo and asymmetry factor from Aerosol Robotic Network; temperature and water vapor profiles from radiosondes; and surface shortwave (SW) spectral albedo from radiometers. A radiative closure experiment was conducted for clear-sky conditions. The mean differences of modeled and observed surface downwelling SW total fluxes were 1 W m-2 at SGP and 2 W m -2 at TWP, which are within observational uncertainty. At SGP, the estimated annual mean clear-sky aerosol DRE is -3.00 W m -2 at the top of atmosphere (TOA) and -6.85 W m -2 at the surface. The strongest aerosol DRE of -4.81 (-10.77) W m -2 at the TOA (surface) are in the summer when AODs are largest. The weakest aerosol DRE of -.28 (-2.77) W m -2 at the TOA (surface) are in November–January when AODs and single-scattering albedos are lowest. At TWP, the annual mean clear-sky DRE is -2.82 W m-2 at the TOA and -10.34 W m -2 at the surface. The strongest aerosol DRE of -5.95 (-22.20) W m -2 at the TOA (surface) are in November (October) due to the biomass burning season’s peak. The weakest aerosol DRE of -0.96 (-4.16) W m -2 at the TOA (surface) are in March (April) when AODs are smallest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗