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DOE OSTI2021

Atmospheric Carbon and Transport – America (ACT-America) Data Sets: Description, Management, and Delivery

The ACT-America project is a NASA Earth Venture Suborbital-2 mission designed to study the transport and fluxes of greenhouse gases. The open and freely available ACT-America data sets provide airborne in situ measurements of atmospheric carbon dioxide, methane, trace gases, aerosols, clouds, and meteorological properties, airborne remote sensing measurements of aerosol backscatter, atmospheric boundary layer height and columnar content of atmospheric carbon dioxide, tower-based measurements, and modeled atmospheric mole fractions and regional carbon fluxes of greenhouse gases over the Central and Eastern United States. In this study, we conducted 121 research flights during five campaigns in four seasons during 2016–2019 over three regions of the US (Mid-Atlantic, Midwest and South) using two NASA research aircraft (B-200 and C-130). We performed three flight patterns (fair weather, frontal crossings, and OCO-2 underflights) and collected more than 1,140 h of airborne measurements via level-leg flights in the atmospheric boundary layer, lower, and upper free troposphere and vertical profiles spanning these altitudes. We also merged various airborne in situ measurements onto a common standard sampling interval, which brings coherence to the data, creates geolocated data products, and makes it much easier for the users to perform holistic analysis of the ACT-America data products. Here, we report on detailed information of data sets collected, the workflow for data sets including storage and processing of the quality controlled and quality assured harmonized observations, and their archival and formatting for users. Finally, we provide some important information on the dissemination of data products including metadata and highlights of applications of ACT-America data sets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Advancing Cross-Disciplinary Understanding of Land-Atmosphere Interactions

The evolution of disciplinary silos and increasingly narrow disciplinary boundaries have together resulted in one-sided approaches to the study of land-atmosphere interactions—a field that requires a bi-directional approach to understand the complex feedbacks and interactions that occur. The integration of surface flux and atmospheric boundary layer measurements is therefore essential to advancing our understanding. The Land-Atmosphere 2021 workshop (held virtually, June 10-11, 2021) involved almost 300 participants from around the world and promoted cross-discipline collaboration by way of talks from invited speakers, moderated discussions, breakout sessions, and a virtual poster session. The workshop focused on five main theme areas: “big picture” overview, instrumentation and remote sensing, modeling, water, and aerosols and clouds. In talks and breakout groups, there were frequent calls for more AmeriFlux sites to be instrumented for boundary layer height measurements, and for the development of some “super sites” where profiling instruments would be deployed. There was further agreement on the need for the standardization of various datasets. There was also a consensus that funding agencies need to be willing to support the sorts of large projects (including associated instrumentation) which can drive interdisciplinary work. Early-career scientists, in particular, expressed enthusiasm for working across disciplinary boundaries but noted that there need to be more financial support and training opportunities so they would be better prepared for interdisciplinary work. Investment in these career development opportunities would enable today's cohort of early-career scientists to advance the frontiers of interdisciplinary work over the next couple of decades.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Asymptotic coefficients of the attached-eddy model derived from an adiabatic atmosphere

The attached-eddy model (AEM) predicts that the mean streamwise velocity and streamwise velocity variance profiles follow a logarithmic shape, while the vertical velocity variance remains invariant with height in the overlap region of high Reynolds number wall-bounded turbulent flows. Moreover, the AEM coefficients are presumed to attain asymptotically constant values at very high Reynolds numbers. Here, the AEM predictions are examined using sonic anemometer measurements in the near-neutral atmospheric surface layer, with a focus on the logarithmic behaviour of the streamwise velocity variance. Utilizing an extensive 210-day dataset collected from a 62 m meteorological tower located in the Eastern Snake River Plain, Idaho, USA, the inertial sublayer is first identified by analysing the measured momentum flux and mean velocity profiles. The logarithmic behaviour of the streamwise velocity variance and the associated ‘−1’ scaling of the streamwise velocity energy spectra are then investigated. The findings indicate that the Townsend–Perry coefficient (A 1 ) is influenced by mild non-stationarity that manifests itself as a Reynolds number dependence. After excluding non-stationary runs, and requiring the bulk Reynolds number defined using the atmospheric boundary layer height to be larger than 4 × 10 7 , the inferred A 1 converges to values ranging between 1 and 1.25, consistent with laboratory experiments. Furthermore, nine benchmark cases selected through a restrictive quality control reveal a close relation between the ‘−1’ scaling in the streamwise velocity energy spectrum and the logarithmic behaviour of streamwise velocity variance. However, additional data are required to determine whether the plateau value of the pre-multiplied streamwise velocity energy spectrum is identical to A 1 .

atmospheric flows↗

DOE OSTI2025

Using ground-based lidar data to investigate the water–vapor budget in the daytime atmospheric boundary layer

The moisture advection term in the water–vapor budget equation is investigated with a combination of a vertically-staring water–vapor lidar and Doppler lidar systems. These instruments make it possible to get the mean profile of moisture tendency and the latent heat flux (LHF) divergence. We use data of the Land–Atmosphere Feedback Experiment (LAFE) at the Atmospheric Radiation Measurement (ARM) Program’s Southern Great Plains (SGP) site, Oklahoma, USA, collected on 30 August 2017 between 15 and 24 UTC, which corresponds to 09 to 18 LT. The lidars provide turbulence resolving profiles of moisture and vertical wind fluctuations. The LHF profile is derived from the covariance of these moisture and vertical wind fluctuations. The mean boundary layer height z i is determined from the peak of the moisture variance. The results demonstrate that the combination of two remote sensing instruments can be applied for determining the dominant water–vapor budget terms, namely moisture tendency, latent heat flux divergence and moisture advection.

Advection↗

DOE OSTI2021

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors

Cloud drop number concentrations (N d ) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosoloptical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak inspring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH),low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol–N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d .

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A two-fluid single-column model of turbulent shallow convection. Part II: Single-column model formulation and numerics

The two-fluid single-column model of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) is extended to include moisture and horizontal wind shear. Turbulent kinetic energy is introduced as a prognostic variable, dependence on a diagnosed boundary-layer height is removed, and subfilter fluxes are approximated using a two-fluid version of a Mellor–Yamada scheme. Three mechanisms for entrainment and detrainment processes are introduced, which represent entrainment of unstable air at the surface, forced detrainment of air at the top of the boundary/cloud layers, and turbulent mixing that relaxes the convective fluid to a reference profile. A semi-implicit Eulerian discretization replaces the semi-implicit semi-Lagrangian implementation of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) to improve numerical stability and conservation. The equations for the implicit time step are solved using a quasi-Newton method, which is shown to perform well in numerical tests for conservation and convergence. The two-fluid single-column model presented in this article will be applied to simulations of shallow cumulus convection in Part III.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Effects of Low-Carbon Energy Adoption on Airborne Particulate Matter Concentrations With Feedbacks to Future Climate Over California

California plans to reduce emissions of long-lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short-lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low-carbon energy (GHG-Step) scenario on radiative forcing in California. Simulations were carried out using the source-oriented WRF/Chem (SOWC) model with 12 km spatial resolution for the year 2054. Furthermore, the avoided aerosol emissions due to technology advances in the GHG-Step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business-as-Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top-of-atmospheric forcing predicted by the SOWC model increased by 0.15 W m -2 , surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG-Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG-Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order-of-magnitude comparison, the long-term effects of global reductions in GHG emissions (RCP8.5–RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long-lived climate pollutants such as CO 2 are much stronger than the effects of short-lived climate pollutants such as PM soot over California in the year 2054.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Boundary Layer Structures Over the Northwest Atlantic Derived From Airborne High Spectral Resolution Lidar and Dropsonde Measurements During the ACTIVATE Campaign

The Planetary Boundary Layer height (PBLH) is essential for studying PBL and ocean-atmosphere interactions. Marine PBL is usually defined to include a mixed layer (ML) and a capping inversion layer. The ML height (MLH) estimated from the measurements of aerosol backscatter by a lidar was usually compared with PBLH determined from radiosondes/dropsondes in the past, as the PBLH is usually similar to MLH in nature. However, PBLH can be much greater than MLH for decoupled PBL. Here, in this study, we evaluate the retrieved MLH from an airborne lidar (HSRL-2) by utilizing 506 co-located dropsondes during the ACTIVATE field campaign over the Northwest Atlantic from 2020 to 2022. First, we define and determine the MLH and PBLH from the temperature and humidity profiles of each dropsonde, and find that the MLH values from HSRL-2 and dropsondes agree well with each other, with a coefficient of determination of 0.66 and median difference of 18 m. In contrast, the HSRL-2 MLH data do not correspond to dropsonde-derived PBLH, with a median difference of -47 m. Therefore, we modify the current operational and automated HSRL-2 wavelet-based algorithm for PBLH retrieval, decreasing the median difference significantly to -8 m. Further data analysis indicates that these conclusions remain the same for cases with higher or lower cloud fractions, and for decoupled PBLs. These results demonstrate the potential of using HSRL-2 aerosol backscatter data to estimate both marine MLH and PBLH and suggest that lidar-derived MLH should be compared with radiosonde/dropsonde-determined MLH (not PBLH) in general.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Environmental Controls on Water Vapor Deuterium Excess in the Coastal Boundary Layer: An Information Theory Perspective

We use information theory to quantify the environmental controls on water vapor deuterium excess (D-excess) in coastal Southern California from June 2023 through February 2024. Using Shannon entropy, mutual information (MI), and joint mutual information, metrics that capture both linear and nonlinear relationships, we identify the most informative variables and variable combinations governing D-excess across contrasting marine and continental regimes. Relative humidity with respect to sea surface temperature (RHS) is consistently the strongest individual predictor, explaining up to 27% of D-excess variability during marine conditions but only 10% in continental air masses. The Relative humidity(RHS) + sea surface temperature (SST) combination demonstrates synergistic effects, where their joint influence (explaining up to 36% of D-excess variability) exceeds what either variable achieves individually, confirming their coupled influence on deuterium excess. Wind direction complements RHS most effectively during continental conditions. The best three-variable combination (RHS + SST + Planetary Boundary Layer height) explains 38% of D-excess variability in marine air, while no combination exceeds 20% explanatory power during continental periods. Information theory shows that heteroscedasticity in D-excess relationships indicates regime shifts in controlling processes and quantifies fundamental constraints on predictor variables: some environmental factors like surface pressure or water vapor flux contain insufficient information content to explain D-excess variability regardless of their physical relevance. These results highlight the different predictability limits between marine and continental regimes, challenging the adequacy of linear models and providing a rigorous framework for quantifying the information content of isotope-climate relationships with implications for both modern and paleoclimate applications.

information theory↗

DOE OSTI2025

Short-Term Energy and Meteorological Impacts on Thanksgiving CO2 in Salt Lake City

Abstract Long-term, high-frequency atmospheric CO2 measurements at multiple sites in the Salt Lake City (SLC), Utah, reveal that annual and monthly CO2 variability aligns with a priori estimates of emissions from anthropogenic and biological sources. In this study, we investigate whether short-term fluctuations in anthropogenic emissions, as captured in the Vulcan3 dataset for the United States, can be detected in atmospheric CO2 observations. Specifically, we focus on Thanksgiving holidays, when traffic and energy usage patterns differ from the rest of November. Onroad CO2 emissions exhibit a double peak during weekday morning and evening rush hours but remain relatively low on weekends and Thanksgiving. Interestingly, CO2 mole fractions during Thanksgiving were higher than the rest of November at all SLC monitoring sites, particularly from 2008 to 2013. This increase is partially attributed to elevated energy-related emissions — especially residential sources — and meteorological factors such as weak wind speeds, cold temperature, and a low planetary boundary layer height (PBLH). While CO₂ emissions and mole fraction patterns align over time, notable spatial differences exist. For instance, the near-highway site in Murray shows the highest CO₂ mole fractions despite low local emissions, suggesting pollution transport via highways and wind advection. Random Forest model-based SHapley Additive exPlanations (SHAP) analysis reveals that onroad emissions dominate CO2 contributions on weekdays and weekends, while energy-related emissions play a larger role during Thanksgiving, alongside meteorological drivers such as wind speed and PBLH. Across six urban cities, CO2 emissions display a consistent pattern: residential and commercial (onroad) emissions peak during Thanksgiving (weekday) with substantial (minimal) year-to-year variability. These findings highlight that urban CO₂ variability is driven by the combined influence of emissions and meteorology, underscoring the need for integrated mitigation strategies. Additionally, multi-site measurements are essential for accurate source attribution and the development of effective policy interventions. 

Ryoo, Ju-Mee (ORCID:0000000234256296)↗

DOE OSTI2023

Near-Cloud Atmospheric Ingredients for Deep Convection Initiation

A lack of routine environmental observations located near deepening cumulus congestus clouds limits verification of important theorized and simulated updraft–environment interaction processes occurring during deep convection initiation (CI). We analyze radiosonde profiles collected during several hundred CI events near a mountain range in central Argentina during the CACTI field campaign. Statistical analyses illustrate environmental conditions supporting radar-observed CI outcomes that span a spectrum of convective cell depths, widths, and durations, as well as events lacking precipitating convection. Tested environmental factors include a large variety of sounding-derived measurements of CAPE, CIN, moisture, terrain-relative winds, vertical shear, and lifted parcel properties, with supplemental model reanalysis of background larger-scale vertical motion. CAPE and CIN metrics do not consistently differentiate CI success from failure. Only a few environmental factors contain consistent monotonic relationships among the spectrum of cloud depths achieved during CI: (i) the depth and strength of background ascent, and (ii) the component of low-level flow oriented parallel to the ridgeline. These metrics suggest that the ability of the surrounding flow to lift parcels to their LFC and terrain-modified flow are consistently relevant processes for CI. Low- to midlevel relative humidity strongly discriminated between CI and non-CI events, likely reflecting entrainment-driven dilution processes. However, we could not confidently conclude that relative humidity similarly discriminated robust from marginal CI events. Circumstantial evidence was found linking cell width, an important cloud property governing the probability of CI, to LCL height, boundary layer depth, depth and magnitude of the CIN layer, and ambient wind shear.

Storm environments↗

DOE OSTI2023

Analysis of an Arctic cold air outbreak during autumn and related air mass transformations forced by surface changes and advection in higher altitudes

During marine cold air outbreaks (MCAOs), cold and dry Arctic air masses are transported from the central Arctic southward across the closed sea ice and much warmer open oceans. They experience significant transformations including a rapid heating and moistening, often leading to cloud formation. While intense wintertime MCAOs have been analyzed widely, the air mass transformations during other seasons have been studied sparsely. We address this gap by investigating an MCAO case observed in September 2020. To study the transformation processes, we combine the fifth generation of atmospheric reanalyses of the global climate (ERA5), trajectory calculations, as well as shipborne and airborne measurements. In the central Arctic, observations acquired from aboard the research vessel (RV) Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition characterized the initial state of the air mass over closed sea ice. Trajectories indicated the pathway the air mass took from RV Polarstern southward to the Fram Strait. For the first 24 h of the southbound drift, the air masses remained quasi-stationary. Then, still 15 h ahead of the marginal sea ice zone, differential advection across the boundary layer flow introduced humidity and clouds at higher altitudes between 1.5 and 2.5 km. ERA5-derived temperature and humidity tendencies indicated complex vertical interactions. Radiative cloud-top cooling, entrainment, and turbulence were significantly reduced in the lower and enhanced in the upper advected cloud layer. Eventually, the lower cloud deck dissipated. After this confluence of 2 different air masses, observations gathered by Polar 5 in Fram Strait as part of the MOSAiC Airborne observations in the Central Arctic campaign revealed cloudy, moist layers throughout the lowest 3.5 km and an increasing boundary layer height. Comparing the initial with the final state 48 h later, the largest net heating of +8 K was found close to the surface, yet the largest net moistening of +2.5 g kg -1 at an altitude of 1 km, as the initial profile was exceptionally dry here. We conclude that the observed air mass transformations were driven by the surface changes from sea ice to open ocean but additionally strongly impacted by the differential advection of clouds and moisture across the near-surface MCAO flow.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product Report

The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Tethered Balloon System Merged Data (TBSMERGED) Value-Added Product (VAP) integrates data from instruments flown on ARM’s tethered balloon system missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary-layer height from a surface-based ceilometer to improve the ease of use of tethered balloon system (TBS) data sets. The TBSMERGEDINCLOUD VAP, a variation of TBSMERGED, includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Tethered Balloon System merged (TBSmerged) Value-Added Product Report

TBSmerged integrates data from instruments flown on ARM’s Tethered Balloon System missions that collect in situ measurements of temperature, humidity, wind speed, wind direction, and aerosol properties with estimates of cloud base and boundary layer height from a surface-based ceilometer to improve the ease of use of TBS datasets. TBSmergedincloud includes supercooled liquid water content (tbsslwc) measurements collected within the cloud.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multiscale ACI Satellite Database

The SATELLITE_EAGLES_PNNL NetCDF dataset contains a suite of satellite- and reanalysis-derived atmospheric and surface parameters on a regular latitude–longitude grid. The dataset includes core geophysical fields such as land fraction, aerosol optical depth at multiple wavelengths (465, 550, 667, and 865 nm), sea surface temperature, estimated inversion strength, and various thermodynamic and dynamic quantities (e.g., relative humidity, vertical velocity, boundary-layer height, and surface fluxes) from both MERRA and ERA reanalysis products, provided as daily-mean and instantaneous values. A major component of the dataset consists of MODIS-retrieved cloud microphysical properties, including cloud droplet number concentration, cloud effective radius, optical thickness, and liquid water path, provided for three compositing regimes (“All,” “Q06,” and “G18”). Corresponding cloud-top parameters—temperature, height, and pressure—along with total and domain-mean cloud fraction fields are also included. The file further integrates additional satellite data from AMSR-E (for cloud water, rain water, and surface precipitation retrievals) and CERES (for top-of-atmosphere radiative fluxes, cloud fractions, and albedo). This dataset is designed to evaluate aerosol–cloud interactions in warm clouds, emphasizing the use of MODIS for deriving cloud droplet number concentration and liquid water path statistics. The complementary satellite and reanalysis fields are co-located and time-matched to the same instantaneous MODIS observations, enabling consistent comparisons between cloud properties, aerosol loading, and large-scale meteorological conditions. The dataset is recently featured in Christensen et al. (2025), Machine Learning Reveals Strong Grid-Scale Dependence in the Satellite Nd–LWP Relationship, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3850, 2025.

Christensen, Matthew [Pacific Northwest National L↗

DOE OSTI2025

Multiscale ACI Satellite Database

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.

47 OTHER INSTRUMENTATION↗