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DOE OSTI2024

Low-Level Liquid-Bearing Clouds Contribute to Seasonal Lower Atmosphere Stability and Surface Energy Forcing over a High-Mountain Watershed Environment

Measurements of atmospheric structure and surface energy budgets distributed along a high-altitude moun tain watershed environment near Crested Butte, Colorado, from two separate, but coordinated, field campaigns, Surface Atmosphere Integrated field Laboratory (SAIL) and Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH), are analyzed. This study identifies similarities and differences in how clouds influence the radiative budget over one snow-free summer season (2022) and two snow-covered seasons (2021/22; 2022/23) for this alpine location. A relationship between lower-tropospheric stability stratification and longwave radiative flux from the presence or absence of clouds is identified. When low clouds persisted, often with signatures of supercooled liquid in win ter, the lower troposphere experienced weaker stability, while radiatively clear skies that are less likely to be influenced by liquid droplets were associated with appreciably stronger lower-tropospheric stratification. Corresponding surface turbu lent heat fluxes partitioned differently based upon the cloud–stability stratification regime derived from early morning radiosounding profiles. Combined with the differences in the radiative budget largely resulting from dramatic seasonal dif ferences in surface albedo, the lower atmosphere stratification, surface energy budget, and near-surface thermodynamics are shown to be modified by the effective longwave radiative forcing of clouds. The diurnal evolution of thermodynamics and surface energy components varied depending on the early morning stratification state. Thus, the importance of quies cent versus synoptically active large-scale meteorology is hypothesized as a critical forcing for cloud properties and associ ated surface energy budget variations. The physical relationships between clouds, radiation, and stratification can provide a useful suite of metrics for process understanding and to evaluate numerical models in such an undersampled, highly com plex terrain environment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Soil Moisture‐Cloud‐Precipitation Feedback in the Lower Atmosphere From Functional Decomposition of Satellite Observations

Abstract The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Data collected using small uncrewed aircraft systems during the TRacking Aerosol Convection interactions ExpeRiment (TRACER)

Abstract. The main goal of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) project was to further understand the role that regional circulations and aerosol loading play in the convective cloud life cycle across the greater Houston, Texas, area. To accomplish this goal, the United States Department of Energy and research partners collaborated to deploy atmospheric observing systems across the region. Cloud and precipitation radars, radiosondes, and air quality sensors captured atmospheric and cloud characteristics. A dense lower-atmospheric dataset was developed using ground-based remote sensors, a tethersonde, and uncrewed aerial systems (UASs). TRACER-UAS is a subproject that deployed two UAS platforms to gather high-resolution observations in the lower atmosphere between 1 June and 30 September 2022. The University of Oklahoma CopterSonde and the University of Colorado Boulder RAAVEN (Robust Autonomous Aerial Vehicle – Endurant Nimble) were flown at two coastal locations between the Gulf of Mexico and Houston. The University of Colorado Boulder RAAVEN gathered measurements of atmospheric thermodynamic state, winds and turbulence, and aerosol size distribution. Meanwhile, the University of Oklahoma CopterSonde system operated on a regular basis to resolve the vertical structure of the thermodynamic and kinematic state. Together, a complementary dataset of over 200 flight hours across 61 d was generated, and data from each platform proved to be in strong agreement. In this paper, the platforms and respective data collection and processing are described. The dataset described herein provides information on boundary layer evolution, the sea breeze circulation, conditions prior to and nearby deep convection, and the vertical structure and evolution of aerosols. The quality-controlled TRACER-UAS observations from the CopterSonde and RAAVEN can be found at https://doi.org/10.5439/1969004 (Lappin, 2023) and https://doi.org/10.5439/1985470 (de Boer, 2023), respectively.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Windsonde Atmospheric Profile during CoURAGE 2024-2025 at Kent Island.

This is the atmospheric sounding data using Sparv Windsond S1 lower-atmosphere profiling instruments ( https://sparv.io/products/windsond-s1) . These data are taken weekly (twice weekly and twice daily during the two CoURAGE IOPs in February and July 2025). Lower-atmosphere conditions, generally between the surface and 5km altitude, are recorded. The variables include pressure, height, temperature, dew-point temperature, wind speed, and wind direction.

air_temperature↗

DOE OSTI2026

Windsonde Atmospheric Profile during CoURAGE 2024-2025 at Baltimore.

air_temperature↗

DOE OSTI2025

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River watershed during SPLASH, 2021–2023

Abstract. From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as atmospheric surface flux stations (ASFSs). The ASFSs were deployed on 28 September 2021 at the Kettle Ponds Annex site and on 12 October 2021 at the Avery Picnic site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes and subsurface conductive flux, each at 1 min resolution; 3-D winds from a sonic anemometer and H2O/CO2 from an open-path gas analyzer, both at 20 Hz from which sensible, latent heat, and CO2 fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 h. The system uptime was 97 % (Kettle Ponds) and 90 % (Avery Picnic), and collectively 1184 d of data was obtained between the stations. The purpose of this article is to document the ASFS deployment at SPLASH, to document the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived at Zenodo (https://doi.org/10.5281/zenodo.10313363, Cox et al., 2023b; https://doi.org/10.5281/zenodo.10327409, Cox et al., 2023c; https://doi.org/10.5281/zenodo.10313894, Cox et al., 2023d; https://doi.org/10.5281/zenodo.10307825, Cox et al., 2023e; https://doi.org/10.5281/zenodo.10310520, Cox et al., 2023f) with the Creative Commons Attribution 4.0 International license.

Cox, Christopher J. (ORCID:0000000322037173)↗

DOE OSTI2024

Atmospheric Boundary Layer-Wind Farm Interactions Field Campaign Report

The American WAKE ExperimeNt (AWAKEN) was a field campaign in northern Oklahoma intended to analyze the potential influence of wind farms and their collective wakes on the atmospheric boundary layer (ABL), wind power production, and turbine structural loads. This report summarizes the deployment of instruments by the University of Oklahoma (OU), National Atmospheric and Atmospheric Administration National Severe Storms Laboratory (NOAA NSSL), and Lawrence Livermore National Laboratory (LLNL) during AWAKEN. Two Collaborative Lower Atmosphere Profiling Systems (CLAMPS) and LLNL ZephIR profiling lidars were co-deployed from October 3, 2022, to December 20, 2022 (winter campaign). At the end of the winter campaign, both ZephIR lidars were moved to different AWAKEN sites to be a part of a targeted wake study at the King Plains wind farm. The two CLAMPS were redeployed for a second observation period from July 1st, 2023, to September 28, 2023 (summer campaign; the ZephIR lidars were not co-located with CLAMPS).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Satellite Reentry Predictions During Sudden Stratospheric Warmings

Prediction of uncontrolled satellite reentry time and trajectory are essential to avoid damages to human being and properties over land. Reentry time and trajectory are largely controlled by aerodynamic drag and mass neutral density below 200 km. In this paper, using a numerical model of the whole atmosphere, WACCM‐X, and a Precision Orbital Determination software, we demonstrate the major sudden stratospheric warming (SSW) in January 2009, during the deep solar minimum of 2009, may cause 0.2–0.3 day of variation in reentry time. The SSW can alter the mass density and composition mixing ratio in the lower thermosphere below 200 km through changed wave forcing and general circulation in the lower thermosphere. This is comparable to the effect of a 2‐SFU (Solar Flux Unit) solar radiance variation on the neutral density. 0.1 day of reentry time uncertainty corresponds to a global shift in impact location, that is, thousands of kilometers distance. For comparison, a major SSW during solar maximum contributes little to reentry time variation, which is predominantly controlled by the solar and geomagnetic forcing. We suggest a few proxies such as O/N 2 in the lower thermosphere or mean meridional wind to account for the lower atmosphere impacts in an operational model. This needs to be investigated in rigorous future work.

54 ENVIRONMENTAL SCIENCES↗

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

Land-Surface Temperature Transitions at the Bankhead National Forest (LASTT-BNF) Field Campaign Report

Land surface types that are common in the Southeast United States (e.g., forests, agriculture, urban) can play key roles in the regulation of surface-atmosphere energy exchange and boundary-layer evolution in the region (Hinkle et al. 2024). Heterogeneity in these land-surface types and associated emissivities can drive strong transitions in land surface temperature (LST), which is an important component of the surface energy balance. Within the forested canopy surrounding the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility’s field deployment at the Bankhead National Forest (BNF) in Alabama main site (M1) (Kuang et al. 2026), the different components of the vegetation and ground surface can create strong gradients in surface temperature that can influence the canopy thermal environment and vegetative function (e.g., stress, transpiration), both of which can have important, yet poorly characterized, controls on lower atmospheric processes (e.g., thermal turbulence, secondary circulations).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Climatology of Cloud‐Land‐Surface Coupling Across Different ARM Sites

Land-atmosphere interactions play a critical role in the evolution and formation of low-level clouds. The different states of coupling between low-level clouds and the surface are uncertain, primarily over continental regions, where complex thermodynamics complicates their investigation. This study uses observations from the Atmospheric Radiation Measurement User Facility to explore cloud-surface coupling and perform a climatological analysis of this interaction in five countries across three continents. The results reveal consistent coupling thresholds and average percentages across the five sites, with coupled clouds accounting for 66% of the cases and decoupled clouds for 34%. Thermodynamic and dynamic evaluations show distinct differences between coupled and decoupled clouds. Coupled clouds are characterized by humid environments, in which vertical motions connect the surface and lower atmosphere to the cloud base, conditions that favor the formation of boundary layer clouds. Decoupled clouds prefer to occur in a drier and colder environment with vertical motions inside the boundary layer being detached from the cloud base, under which boundary layer clouds are hard to form. Coupled clouds peak during warmer hours and seasons, and vice versa for decoupled clouds. This study underscores the complexity of cloud-land-surface interactions and paves the way for further investigations into cloud formation and evolution under different atmospheric environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Probing the atmospheric boundary layer with integrated remote-sensing platforms during the American WAKE ExperimeNt (AWAKEN) campaign

The American WAKE ExperimeNt (AWAKEN) collaboration is an observational-based field campaign in northern Oklahoma intended to analyze the potential influence of onshore wind farms and their collective wakes on wind power production, turbine structural loads, and on the atmospheric boundary layer (ABL). Focusing on the ABL effects, the University of Oklahoma and the Lawrence Livermore National Laboratory collected continuous high-resolution kinematic and thermodynamic profile measurements during 2022 and Summer 2023. The deployment strategy for these campaigns is detailed first, followed by an initial comparison of data from two sites in the AWAKEN domain: a near-farm site to examine collective wake impacts on the ABL, and a far-field site remaining outside the wind farm-waked region. Here, we summarize the datasets available and demonstrate the benefits of these observations and multiple value-added products (VAPs) for investigation of ABL features observed during AWAKEN. We also highlight examples of preliminary analyses, including ABL height detection and nocturnal low-level jet examination, which are produced using novel VAPs based on optimal estimation to retrieve deeper Doppler lidar wind profiles than previously resolved, along with their uncertainty. By including the near-farm and far-field site in these analyses, we identified a pattern of stronger lower-atmospheric mixing at the near-farm site than the far-field site, motivating deeper investigation into the relationship between wind farms and general ABL characteristics. Future analysis will delve deeper into this relationship by examining other ABL characteristics, such as atmospheric stability and convection.

17 WIND ENERGY↗

DOE OSTI2025

Large‐Scale Statistically Meaningful Patterns (LSMPs) Associated With Precipitation Extremes Over Northern California

Abstract We analyze large‐scale statistically meaningful patterns (LSMPs) that precede extreme precipitation (PEx) events over Northern California (NorCal). We find LSMPs by applying k‐means clustering to the two leading principal components of daily 500 hPa geopotential height anomalies two days before the onset, from October to March during 1948–2015. Statistical significance testing based on Monte Carlo simulations suggests a minimum of four statistically distinguished LSMP clusters. The four LSMP clusters are characterized as Northwest continental negative height anomaly, Eastward positive “Pacific‐North American Pattern (PNA),” Westward negative “PNA,” and Prominent Alaskan ridge. These four clusters, shown in multiple variables, evolve very differently and have differing links to the Arctic and tropical Pacific regions. Using binary forecast skill measures and a new copula‐based framework for predicting PEx events, we find LSMP indices that are useful predictors of NorCal PEx events, with moisture‐based variables being the best predictors of PEx events at least 6 days before the onset, and the lower atmospheric variables being better than their upper atmospheric counterparts any day in advance tested. To ensure statistical rigor, the LSMPs analyzed here (with the modified acronym) include local tests of both significance and consistency, which are not always featured in the literature on large‐scale meteorological patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Pinned Clouds over Industrial Sources of Heat during TRACER

An analysis of stereo-camera data from the Tracking Aerosol Convection Interactions Experiment (TRACER) campaign in Houston, Texas, reveals the existence of pinned clouds sitting atop industrial heat sources. These are not plumes of vapor and condensate emanating from stacks: The pinned shallow cumuli have cloud bases at about the lifting condensation of near-surface air. On many mornings, the pinned clouds are the only shallow cumuli in the field of view of the stereo cameras, persisting by themselves for at least an hour. On those mornings, the lower atmosphere straddles the boundary between stable and unstable, and the waste heat from industrial facilities is able to pin moist convection overhead. When solar heating of the surface becomes sufficient later in the morning, those mornings transition to having widespread shallow moist convection. Occurring within the field of view of the stereo cameras and other TRACER instruments, these pinned clouds represent steady plume-like moist convection triggered by known heat sources in a well-characterized atmosphere, making them a rich target for further study. Significance Statement This article introduces steady-state cumulus clouds observed during the Tracking Aerosol Convection Interactions Experiment (TRACER) campaign that were pinned to industrial heat sources. These pinned clouds were present in the early morning only when conditions were primed for moist convection, but for which there was not yet enough solar heating to trigger widespread convection. They persist in a statistical steady state and therefore provide a natural setting to explore the mechanisms controlling convective dynamics, which play a central role in Earth’s atmospheric circulation and radiative balance.

Atmosphere↗

DOE OSTI2024

X-Band Radar and Surface-Based Observations of Cold-Season Precipitation in Western Colorado’s Complex Terrain

Abstract Hydrologic processes associated with intermountain cold-season precipitation in the Upper Colorado River basin have important impacts on avalanche forecasting and water resource management. However, traditional weather radar networks struggle with observations in this complex terrain. Data collected during the Study of Precipitation, the Lower Atmosphere, and the Surface for Hydrometeorology (SPLASH) and its sister campaign, Surface Atmosphere Integrated Field Laboratory (SAIL) in the East River watershed of western Colorado, are used to examine a multistorm period from 23 December 2021 to 1 January 2022 that contributed 35% of the total winter precipitation in this watershed. Dual-polarization X-band radar and disdrometer measurements show ∼30-mm differences in precipitation amount at two sites in proximity over four distinct storm events within the period. Wind patterns, synoptic forcings, microphysical characteristics of precipitation, and surface meteorology are analyzed to explain the observed spatial variability of cold-season precipitation in complex mountainous terrain. Analysis shows that differences over time within this event are mainly accounted for by synoptic forcings, such as frontal passages; differences between sites are accounted for by the impact of variations in local wind patterns on precipitation microphysics. Patterns of surface precipitation intensity are compared and found to be correlated with X-band radar signatures; a relationship between a strong dendritic growth stage and intense low-density surface precipitation is reinforced by this study. This relationship demonstrates the importance of particle growth mechanisms on surface snowfall patterns in high-altitude complex terrain, underscoring the importance of realistic microphysical parameterizations. Significance Statement The amount and density of snowpack from western Colorado winter storms have significant impacts on water resources in the Upper Colorado River basin. Snowpack characteristics are affected by small-scale differences in how snow forms in the atmosphere. These differences are hard to study in the complex terrain of the Rockies, but data from the SPLASH and SAIL field campaigns allows us to investigate how snow crystal formation and mountain-driven wind patterns affect snow near the surface. Our study finds that snow crystal growth varies over small space and time scales and is likely controlled by the terrain beneath a given location and resultant local wind patterns. These results imply that predicting snowpack in the Rockies requires properly representing local wind patterns and crystal growth processes in models.

Heflin, Stella↗

DOE OSTI2026

Wind lidar operations and observations from Tundra Pigeon

Lawrence Livermore National Laboratory brought a ZX-300 profiling lidar to the Tundra Pigeon experiment for the purpose of collecting wind measurements in the lower atmospheric boundary layer. The ZX-300 lidar is a portable Doppler lidar which uses light to track naturally occurring aerosols across a measurement cone, thereby deriving wind speed (horizontal and vertical) and wind direction. The ZX-300 is programable between the heights of 10 m and 300 m above ground level and additionally has a 1 m onboard meteorological sensor for collecting measurements of air temperature, relative humidity, air pressure and wind at 1 m height. We programmed the ZX-300 to target altitudes of most interest to the tracer experiment. These heights were 10, 15, 20, 30, 38, 50, 75, 100, 125, 150 and 200 m. Note that the 38 m level is a fixed calibration height and cannot be changed. This measurement strategy prioritized winds close to the surface while ensuring that we’d also collect information about the winds aloft. The wind measurements are collected directly over the lidar instrument and represent a vertical profile of the winds over that location. However, given that there were minimal terrain and vegetation differences in the area, we’d expect these wind conditions to be representative of a larger volume area, discussed in more detail below.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

54 ENVIRONMENTAL SCIENCES↗