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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 OSTI2024

Targeted Observations of Blowing Snow during SAIL (SAIL-TOBS) Field Campaign Report

Blowing snow research at high-latitude U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility observatories and mobile campaigns demonstrated the need for ground truth during falling and blowing snow events. The purpose of the Targeted Observations of Blowing Snow during SAIL (SAIL-TOBS) field campaign was to affordably deploy additional instruments to observe mixed-phase and frozen hydrometeors, including falling and blowing snow, during the second winter of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. Instruments included two installations of the Open Snowflake Camera for Research and Education (OSCRE), one at the Gothic, Colorado main site (second ARM Mobile Facility [AMF2]) and a second at the Kettle Ponds external field site in support of the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) campaign. Two FlowCapt FC4 acoustic mass flux sensors were also deployed at AMF2 in proximity to OSCRE on top of the instrumented hill (Figure 1).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Ice-nucleating particles (INPs) concentrations from SAIL-Net

This data set contains ice-nucleating particle (INP) concentration spectra collected during the SAIL-Net sampling period, which complemented the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River watershed near Crested Butte, Colorado. SAIL-Net was a distributed aerosol measurement network designed to investigate aerosol variability across complex mountainous terrain. The data set includes samples from multiple SAIL-Net sites, including AOS, Gothic, Snodgrass, Pumphouse, Irwin, and Top. INP concentrations are reported as a function of freezing temperature, together with confidence limits, sampling times, site location, elevation, sampled air volume, and treatment information. These data provide an analysis-ready record of the INPs across the SAIL-Net network.

activation temperature↗
DOE OSTI2025

SAIL Radar b1 Data Processing: Corrections, Calibrations, and Processing Report

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility deployed the second ARM Mobile Facility (AMF2) near Crested Butte, Colorado for the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. The SAIL campaign occurred from September 1, 2021, to June 15 2023. To study the water cycle in the East River Watershed, ARM deployed a vertically pointing Ka-band ARM Zenith radar (KAZR) and a scanning X-band precipitation radar managed by Colorado State University (CSU XPRECIP), as shown in Figure 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Calibration and Validation of the SAIL Radar

Calibration of a weather radar is not a simple process, as is well described and documented in several articles (Chandrasekar, V, L Baldini, N Bharadwaj, and PL Smith. 2015. "Calibration procedures for global precipitation-measurement ground-validation radars." URSI Radio Science Bulletin 2015(355): 45–73, https://10.23919/URSIRSB.2015.7909473.). Direct hardware-based characterization is called calibration whereas indirect check with other sources such as disdrometer, or comparison with other radars or indirect means, is called validation. The validation process has its own measurement error and the comparison should fall within its limit. In the following we describe calibration and validation processes for the SAIL radar. Based on the comparison of calibration and validation, an addition of 2 dB is suggested for the SAIL radar observations, with a nuanced Z dr calibration correction, as presented. If a fixed Z dr calibration is preferred, a bias addition of 0.5 dB is suggested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Back Trajectories during SAIL

This data set contains air mass back trajectories generated during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, Stein et al. 2015, Rolph et al. 2017). For each hour from January 1, 2021 to June 30, 2023, a 96-hour back trajectory was initiated at the SAIL sampling site, starting 100 m above ground level. The calculations used the GDAS meteorological data set with model vertical velocity. The files within this dataset show location and meteorological parameters of the back trajectory analysis. There are 21 columns within each data file. The first 2 columns pertain to back trajectory parameters: the trajectory number (which is always 1) and the meteorological grid number. The next 9 columns pertain to spatiotemporal information: the year, month, day, hour (0-23), and minute of the trajectory, the previous time of the trajectory (in negative hours), and the airmass location (latitude, longitude, and height in m AGL). The rest of the parameters within each file pertains to surface and meteorological characteristics of the airmass: pressure (in hPa), potential temperature (in K), temperature (in K), precipitation rate (in mm/hr), mixing depth (in m), relative humidity (in % relative to liquid water), specific humidity (in g/kg), H2O mixing ratio (in g/kg), underlying terrain height (in meters above sea level), and incoming radiation (in W/m^2).

back trajectory↗
DOE OSTI2025

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado

Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Size and Time-resolved Aerosol Collector↗
DOE OSTI2025

Diurnal Trends and Meteorological Factors Influencing the Variability of Fluorescent Bioaerosol in Mt. Crested Butte, Colorado During SAIL

Abstract Despite the crucial role of bioaerosol particles (BAP) in our climate system, local ecosystems, and human health, our grasp on their atmospheric interactions is hampered by a lack of high‐resolution and long‐term data, which is essential for understanding their abundance and variability in response to meteorological conditions. To discern these relationships in a high‐altitude mountainous terrain is even less well‐studied. Therefore, we deployed a Wideband Integrated Bioaerosol Sensor (WIBS‐Neo) for three months during the first biologically active season of the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in summer 2022 on Mt. Crested Butte in Colorado (elevation 3,140 m ASL). Here, we report real‐time fluorescent BAP (FBAP) data collected from 15 June to 13 September 2022, sampled from within the ARM Aerosol Observing System (AOS). To our knowledge, these are the first high altitude (>3,000 m ASL) continuous measurements of FBAP made in North America. During this deployment, we observed on average 21% and as many as 48% (hourly maximum) of particles within the detection size range ( to ) of the WIBS as FBAP. Our analysis presents the diurnal cycles for seven distinct types of FBAP, showing unique patterns and, in some cases, correlations with temperature and solar radiation cycles. Abundance and composition of FBAP varied with relative humidity and precipitation. Precipitation events appeared to both cause emission and removal of FBAP, whereas dust and smoke events had no significant effect, highlighting the critical role of meteorology on FBAP at high altitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

SAIL Field Campaign X-Band Precipitation Radar Seasonal Surface Hydrometeor Phase Classification Value-Added Product Report

The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign generated an unprecedented, multisensory record of cold-season precipitation in the Upper Colorado River Basin (UCRB). To complement the Surface Quantitative Precipitation Estimation (SQUIRE) snow water equivalent (SWE) product derived from the Colorado State University (CSU) X-band radar, a hydrometeor phase classification data set was developed to systematically identify phase of the near-surface precipitation. The algorithm uses Corrected Moments in Antenna Coordinates (CMAC) fields with semi-supervised classification (SSC) and fuzzy-logic hydrometeor classification (FHC) methods and maps hydrometeor classes to a compact set of phase classes suitable for Quantitative Precipitation Estimation (QPE) and hydrologic applications. We used SQUIRE grid projections to mitigate beam-blockage issues by selecting the near-surface gates for each beam as well as to make it directly usable for the QPE products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Downscaled GOES-R 500m Land Surface Albedo Over SAIL

This data set contains surface albedo estimates across the SAIL study area derived from the Geostationary Operational Environmental Satellite-R (GOES-R). Unlike the GOES-R surface albedo product, which is reported at 2-km resolution, this data set is produced at 500-m resolution and is produced by downscaling GOES-R data. The downscaling is performed using a "U-Net" and trained with concurrent, collocated, GOES-R surface albedo and 500-m Moderate Resolution Imaging Spectroradiometer (MODIS) blue-sky albedo derived from the MCD43A3 product.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

KAZR-based snowfall rates during SAIL

This data set provides KAZR radar based retrievals of snowfall rates for the two cold periods of the SAIL campaign (NOV 2021- APR 2022 and NOV 2022 - APR 2023).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Merged aerosol size distribution from SMPS and OPC for SAIL

This dataset contains merged aerosol number size distribution data for the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. The merged size distribution data were constructed by combining measurements from a scanning-mobility particle sizer (SMPS) and an optical particle counter (OPC), covering a size range of 0.01–35 µm. The merging methodology follows the approach described by Hand and Kreidenweis (2002) and Marinescu et al. (2019). All aerosol data from the ARM archive were corrected to standard temperature (273.15 K) and pressure (101.3 kPa).

merged size distribution↗
DOE OSTI2025

Source apportionment of aerosols at the White River IMPROVE site near the SAIL site

This data set contains source apportionment results at the White River IMPROVE site (39.1536, -106.8209), which is about 30 km north of the Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign site. The IMPROVE network (Malm et al. 1994) collected 24-hour aerosol filter samples every three days over several decades at this site. Chemical concentrations in the PM2.5 fraction of 19 elements (Al, As, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Se, Si, Ti, V, and Zn), along with nitrate, sulfate, elemental carbon (EC), organic carbon (OC), and calculated coarse mass concentrations (PM10−PM2.5 mass concentrations), from 2014 to 2023, were used as input for the PMF analysis. PMF was performed using EPA PMF 5.0 (Norris et al. 2014). A five-factor solution was chosen as the optimal solution. These factors were identified as coarse dust, fine dust, biomass burning, sulfate-dominated, and nitrate-dominated sources. This data set is useful for understanding aerosol sources and their long-term variability near this region.

biomass burning↗
DOE OSTI2026

Fluorescent Particle Vertical Profiles (Counts and Fractions) at SAIL

This dataset contains vertically resolved fluorescent aerosol number concentrations and fluorescent fractions of total aerosol number concentrations measured with the ARM Tethered Balloon System during the Surface Atmosphere Integrated Field Laboratory campaign near Gothic, Colorado. Measurements were collected during April and June 2023 as part of the ARM–EMSL FICUS project “Characterization of the Aerobiome during the ARM Surface-Atmosphere Integrated Laboratory Campaign,” which was designed to characterize the vertical distribution of bioaerosol-relevant particles in the Colorado River Basin. The dataset includes profiles of fluorescent particle concentrations measured with a Fluorescent Portable Optical Particle Sizer (FPOPS) in the approximately 0.5–3 µm diameter range. For fluorescent fractions of total aerosol number concentrations, we compare FPOPS measurements to POPS on the same TBS flight.

Fluorescent particle concentration↗
DOE OSTI2025

Investigating Spatial Variability of Aerosol, Cloud Condensation Nuclei, and Ice Nucleating Particles in Mountainous Terrain Field Campaign Report

The U.S. Department of Energy Atmospheric System Research (ASR)-supported Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the ERW as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the second ARM Mobile Facility (AMF2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small portable optical particle spectrometer (POPS; Gao et al. 2016), cloud condensation nuclei (CNN) using a miniature CCN counter (CloudPuck), and ice nucleating particles (INP) using the time-resolved aerosol filter sampler (TRAPS; Creamean et al. 2018). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al. 2019, Kelly et al. 2021, Asher et al. 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al. 2017, Popoola et al. 2018, Caubel et al. 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al. 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al. 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in the ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies. We met this goal by answering the following science questions: 1. What is the aerosol temporal variability, and how does aerosol inhomogeneity vary seasonally? Is there significant seasonal variability in sources, or are short-term meteorological conditions the most important determining factor in sources for cloud nuclei? 2. What is the aerosol spatial variability? What are the aerosol characteristics at cloud base, presumably the particles most representative of those acting as cloud nuclei? 3. How should measurement networks be designed to capture aerosol-cloud interactions, and what do they need to measure? Can a single measurement site accurately represent aerosol properties in regions of complex terrain? SAIL-Net consisted of six measurement nodes spread across the ERW near Crested Butte, Colorado. The primary objective in site placement was to select locations that captured the vertical variation in aerosol properties while also spanning the domain of the SAIL campaign. The elevation of the sites ranged from roughly 2750 m along the valley floor of the ERW to approximately 3500 m near the top of Crested Butte Mountain, which is one of the taller peaks in the ERW. The farthest distance between sites was 14 km, while the closest two sites were approximately 1 km apart. Two of the sites were collocated with the ARM SAIL sites; our instruments sat on top of one of the trailers at AOS and another one of our sites was located in a meadow just above AMF2.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Investigating spatial variability of aerosol, cloud condensation nuclei, and ice nucleating particles in mountainous terrain

The ASR-supported Surface Atmosphere Integrated field Laboratory (SAIL) in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the East River Watershed as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the ARM Mobile Facility (AMF-2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small particle counter (POPS, (Gao et al., 2016)), CNN using a miniature CCN counter (CloudPuck), and INP using the Time-Resolved Aerosol Filter Sampler (TRAPS, Creamean et al. (2018)). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al., 2019; Kelly et al., 2021; Asher et al., 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al., 2017; Popoola et al., 2018; Caubel et al., 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al., 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al., 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies.

54 ENVIRONMENTAL SCIENCES↗