Results for “ARM sites” — Page 7

DOE OSTI2021

Accuracy of Vaisala RS41 and RS92 Upper Tropospheric Humidity Compared to Satellite Hyperspectral Infrared Measurements

Radiosondes are important for calibrating satellite sensors and assessing sounding retrievals. Vaisala RS41 radiosondes have mostly replaced RS92 in the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) and the conventional network. This study assesses RS41 and RS92 upper tropospheric humidity (UTH) accuracy by comparing with Infrared Atmospheric Sounding Interferometer (IASI) upper tropospheric water vapor absorption spectrum measurements. Using single RS41 and RS92 soundings at three GRUAN and DOE Atmospheric Radiation Measurement (ARM) sites and dual RS92/RS41 launches at three additional GRUAN sites, collocated with cloud-free IASI radiances (OBS), we compute Line-by-Line Radiative Transfer Model radiances for radiosonde profiles (CAL). We analyze OBS-CAL differences from 2015 to 2020, for daytime, nighttime, and dusk/dawn separately if data is available, for standard (STD) RS92 and RS41 processing, and RS92 GRUAN Data Processing (GDP; RS41 GDP is in development). We find that daytime RS41 (even without GDP) has ~1% smaller UTH errors than GDP RS92. RS41 may still have a dry bias of 1–1.5% for both daytime and nighttime, and a similar error for nighttime RS92 GDP, while standard RS92 may have a dry bias of 3–4%. These sonde humidity biases are probably upper limits since “cloud-free” scenes could still be cloud contaminated. Radiances computed from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses match better than radiosondes with IASI measurements, perhaps because ECMWF assimilates IASI measurements. Relative differences between RS41 STD and RS92 GDP, or between radiosondes and ECMWF humidity profiles obtained from the radiance analysis, are consistent with their differences obtained directly from the RH measurements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Do Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol? Evidence from comparisons between observations and idealized simulations

Abstract. Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol–cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit – Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously – effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener–Bergeron–Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

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 OSTI2024

Aerosol-induced closure of marine cloud cells: enhanced effects in the presence of precipitation

Abstract. The Weather Research Forecasting (WRF) version 4.3 model is configured within a Lagrangian framework to quantify the impact of aerosols on evolving cloud fields. Kilometer-scale simulations utilizing meteorological boundary conditions are based on 10 case study days offering diverse meteorology during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA). Measurements from aircraft, the ground-based Atmosphere Radiation Measurement (ARM) site at Graciosa Island in the Azores, and A-Train and geostationary satellites are utilized for validation, demonstrating good agreement with the WRF-simulated cloud and aerosol properties. Higher aerosol concentration leads to suppressed drizzle and increased cloud water content in all case study days. These changes lead to larger radiative cooling rates at cloud top, enhanced vertical velocity variance, and increased vertical and horizontal wind speed near the base of the lower-tropospheric inversion. As a result, marine cloud cell area expands, narrowing the gap between shallow clouds and increasing cloud optical thickness, liquid water content, and the top-of-atmosphere outgoing shortwave flux. While similar aerosol effects are observed in lightly to non-raining clouds, they tend to be smaller by comparison. These simulations show a relationship between cloud cell area expansion and the radiative adjustments caused by liquid water path and cloud fraction changes. The adjustments are positive and scale as 74 % and 51 %, respectively, relative to the Twomey effect. While higher-resolution large-eddy simulations may provide improved representation of cloud-top mixing processes, these results emphasize the importance of addressing mesoscale cloud-state transitions in the quantification of aerosol radiative forcing that cannot be attained from traditional climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Intercomparison of airborne and surface-based measurements during the CLARIFY, ORACLES and LASIC field experiments

Data are presented from intercomparisons between two research aircraft, the FAAM BAe-146 and the NASA Lockheed P3, and between the BAe-146 and the surface-based DOE (Department of Energy) ARM (Atmospheric Radiation Monitoring) Mobile Facility at Ascension Island (8 S, 14.5W, a remote island in the mid-Atlantic). These took place from 17 August to 5 September 2017, during the African biomass burning season. The primary motivation was to give confidence in the use of data from multiple platforms with which to evaluate numerical climate models. The three platforms were involved in the CLouds-Aerosol-Radiation Interaction and Forcing for Year 2017 (CLARIFY-2017), ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES), and Layered Atlantic Smoke and Interactions with Clouds (LASIC) field experiments. Comparisons from flight segments on six days where the BAe-146 flew alongside the ARM facility on Ascension Island are presented, along with comparisons from wing-tip to wing-tip flight of the P3 and BAe-146 on 18 th August 2017. The intercomparison flight sampled a relatively clean atmosphere overlying a moderately polluted boundary layer, while the 6 fly-bys of the ARM site sampled both clean and polluted conditions 2–4 km upwind. We compare and validate characterisations of aerosol physical, chemical, and optical properties, atmospheric radiation, and cloud microphysics between platforms. We assess the performance of measurement instrumentation in the field, under conditions where sampling conditions are not tightly controlled as in laboratory measurements where calibrations are performed. Solar radiation measurements compared well between airborne platforms. Optical absorption coefficient measurements compared well across all three platforms, even though absolute magnitudes were often low (< 10 Mm -1 ) and close to the sensitivity limits of measurement instrumentation thereby confounding assessments of the comparability of absorption Ångström exponent characterisations. Aerosol absorption measurements from airborne platforms were more comparable than aircraft-to-ground observations. Scattering coefficient observations compared well between airborne platforms, but agreement with ground-based measurements was worse, potentially caused by small differences in sampling conditions or actual aerosol population differences. Chemical composition measurements followed a similar pattern, with better comparisons between the airborne platforms. Thermodynamics, aerosol, and cloud microphysical properties generally compared well.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

microbasekaplus.c1

This microbasekaplus datastream is produced by re-writed micorbaseka vap (microbasekaplus) in python under ADI framewrok, and added uncertainties for the microphysics. The original Microbase VAP was developed in 2002. The primary purpose of producing this VAP was to provide the required microphysical values for input to the Broad Band Heating Rate Profiles (BBHRP) VAP in order to generate estimations of radiative heating rates. It served the Radiatively Important Parameters Best Estimate (RIBPE) VAP in the capacity of providing baseline cloud microphysical values as input. Microbase produces cloud microphysical properties based on cloud radar measurements taken at all fixed ARM sites.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

BIRDIE beryllium-10 and -7 measurements from 2022 summer deep convection rain at ARM SGP

Water samples were manually collected from 11 SGP ARM sites in the early summer of 2022 to measure cosmogenic isotopes of beryllium. The water samples were brought back to the Center for Accelerator Mass Spectrometry (CAMS) at LLNL and analysed for beryllium-10 and beryllium-7 as well as for chlorine-37, chlorine-36, and tritium. A total of six storm events were measured over three weeks. At least five of those storms included overshooting cloud events. Available data include precipitation amount, beryllium-10 concentration, beryllium-7 concentration, and 10/7Be ratio. Spatial and temporal variability in the cosmogenic isotopes was relatively high but could not directly be linked to precipitation amount. Analysis is on-going to explain the measured variability and to see if it can be linked to cloud top height and overshooting depth.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Synoptic Weather Regime Classifications for June, July, August, and September, 2022

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A). This dataset includes the data in June, July, August, and September; the last year of the data is 2022.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Synoptic Weather Regime Classifications for the whole year, from 2014 to 2015

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Snow ALbedo eVOlution (SALVO) Campaign Oblique Photos from April - June, 2024 in Utqiagivk, AK

While albedo line measurements were being made, oblique photos were taken from eye level (~1.8 m) of the position of each albedo measurement (i.e., at 5-meter intervals along the line). The photo numbers were recorded in a notebook and in the ASD notes documents (see the spectral albedo archive). The photos were subsequently renamed for archiving according to the following file naming convention: ‘salvo_[site]_line_oblique_[YYYYMMDD]_[pos]_[rep].JPG’, where [site] is the overall measurement site (‘arm’, ‘beo’, or ‘ice’); YYYYMMDD is the date of the measurement in AKDT (local time); [pos] is the position along the line in meters; and [rep] is the repetition number if repeat photographs were made. Oblique photos are available for almost every albedo measurement.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Synoptic Weather Regime Classifications for June, July, August, from 2000 to 2024

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

node_som_ml↗

DOE OSTI2025

Synoptic Weather Regime Classifications for March, April and May, from 2000 to 2025

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

node_som_ml↗

DOE OSTI2025

Synoptic Weather Regime Classifications for June, July and August, from 2000 to 2025

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

node_som_ml↗

DOE OSTI2025

Synoptic Weather Regime Classifications for December, January and February, from 2000 to 2025

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

{node_som_ml,synop_wea_reg}↗

DOE OSTI2025

Synoptic Weather Regime Classifications for September, October and November, from 2000 to 2025

The synoptic weather regime classification has become a highly demanded product for the ARM site in recent years. This type of regime classification has shown applications in various studies and topics, including aerosol-cloud interactions, land-atmosphere interactions, and cloud radiative effects. The VAP employs an unsupervised machine learning method, Self-organizing map (SOM), to classify weather regimes for each day of the AMF campaigns and fixed sites, using ERA5 data. This idea is mainly based on our published study for TRACER in Wang et al. (2022, JGR-A).

node_som_ml↗

DOE OSTI2026

E3SMv3 ‘Nephele’ Perturbed Parameter Ensemble Value-Added Product for ENA, SGP, NSA, and Global Means

Processed output from the E3SMv3 Nephele perturbed parameter ensemble (PPE). This product contains global means of present-day (using 2010 aerosol forcings) and preindustrial (using 1850 aerosol forcings) monthly and daily global-mean quantities, as well as hourly mean quantities at the E3SMv3 model grid cell closest to the ENA, SGP, and NSA ARM sites. In this PPE, the E3SMv3 model was run for 27 months in an atmosphere-only configuration with free tropospheric winds nudged to reanalysis. Each ensemble member represents a unique combination of 25 model parameter values (in the microphysics, convective microphysics, and aerosol schemes) that were perturbed randomly and simultaneously. The E3SMv3 default configuration is included as member 0. See https://doi.org/10.22541/essoar.174907165.57104591/v1 (in review) for more information on the Nephele PPE and the perturbed parameters.

Model atmospheric temperature↗

NASA NTRS1997

Absorption of Solar Radiation by the Cloudy Atmosphere Interpretations of Collocated Aircraft Measurements

As part of the Atmospheric Radiation Measurement (ARM) Enhanced Shortwave Experiment (ARESE), we have obtained and analyzed measurements made from collocated aircraft of the absorption of solar radiation within the atmospheric column between the two aircraft. The measurements were taken during October 1995 at the ARM site in Oklahoma. Relative to a theoretical radiative transfer model, we find no evidence for excess solar absorption in the clear atmosphere and significant evidence for its existence in the cloudy atmosphere. This excess cloud solar absorption appears to occur in both visible (0.224-0.68 microns) and near-infrared (0.68-3.30 microns) spectral regions, although not at 0.5 microns for the visible contribution, and it is shown to be true absorption rather than an artifact of sampling errors caused by measuring three-dimensional clouds.

Valero, Francisco P. J.↗

NASA NTRS1999

Studies of Radiation and Microphysics in Cirrus and Marine Stratocumulus Clouds

Two tasks were completed during this period. In the first, we examined the polarization of millimeter-wavelength radar beams scattered by ice crystals. Because of their non-spherical shape and size, ice crystals depolarize the incident polarized radar beam. In principle, this depolarization can be used to identify ice from liquid water, as well as provide some information on size. However, the amount of de-polarization is small, producing only a weak signal at the receiver. Our task was to determine the magnitude of such a signal and decide if our radar would be capable of measuring it under typical cirrus conditions. The theoretical study was carried out by Henrietta Lemke, a visiting graduate student from Germany. She had prior experience using a discrete dipole code to compute scattering depolarization. Dr. Kultegin Aydin of the Penn State Electrical Engineering Department, who is also expert in this area, consulted with us on this project at no cost to the project. Our conclusion was that the depolarization signal is too weak to be usefully measured by our system. Therefore we proceeded no further in this study. The second task involved the study of the effect of stratus microphysics on surface cloud forcing. Manajit Sengupta, a graduate student, and the project PI jointly carried out this task. The study used data culled from over a year of continuous radar and radiometer observations at the Atmospheric Radiation Measurement (ARM) site in Oklahoma. The study compared solar radiation calculations made using constant microphysics with calculations made using a retrieved mean particle size. The results showed that on average the constant microphysics produced the correct forcing when compared with the observed forcing. We conclude, therefore, that there is little impetus on radiation grounds alone to include explicit microphysics in climate models. The question of pollutant particle emission impacts on microphysics remains to be resolved. A manuscript is in preparation and will be submitted this year.

Source record↗