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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↗

DOE OSTI2024

Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds

The profile of the liquid water content (LWC) in clouds provides fundamental information for understanding the internal structure of clouds, their radiative effects, propensity to precipitate, and degree of entrainment and mixing with the surrounding environment. In principle, differential absorption techniques based on coincident dual-frequency radar reflectivity observations have the potential to provide the LWC profile. Previous differential frequency radar reflectivity (DFR) efforts were challenged by the fact that the measurable differential attenuation for small quantities of LWC is usually comparable to the system measurement error. This typically renders the retrieval impractical, as the uncertainty can become many times greater than the retrieved value itself. Theoretically, this drawback can be mitigated following two interconnected approaches: (1) increasing the frequency separation between the dual-frequency radar system to measure greater differential attenuation and (2) increasing the radar operating frequency to reduce the instrument measurement random error. Our recently developed 239 GHz radar was deployed during the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) along with a variety of collocated remote sensing and in situ instruments. We have combined Ka-band (35 GHz) and G-band (239 GHz) observations to retrieve the LWC from more than 100 vertical profiles of shallow clouds with typical amounts of LWC smaller than 1 g m -3 . We theoretically and experimentally demonstrate that the Ka-band and G-band pair of frequencies offers at least a 65 % relative improvement in the LWC retrieval sensitivity compared to previous works reported in the literature using lower-frequency radars. This new technique provides a missing capability to determine the LWC in the challenging low liquid water path (LWP) range (< 200 g m -2 ) and suggests a way forward to characterize microphysical and dynamical processes more precisely in shallow clouds.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Introducing the Video In Situ Snowfall Sensor (VISSS)

The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A new technique to retrieve aerosol vertical profiles using micropulse lidar and ground-based aerosol measurements

Accurately characterizing the vertical distribution of aerosols and their cloud-forming properties is crucial for understanding aerosol-cloud interactions and their impact on climate. This study presents a novel technique for retrieving vertical profiles of aerosols, cloud condensation nuclei (CCN), and ice nucleating particles (INP) by combining micropulse lidar, radiosonde, and ground-based aerosol measurements. Herein, the technique is applied to data collected by our team at Texas A&M University during the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) campaign. Ground-based aerosol size distribution and CCN counter data are used to estimate the value of the aerosol hygroscopicity parameter, κ. The derived κ, together with Mie scattering theory and the relative humidity profile from the radiosonde, is used to estimate aerosol size growth and the associated increase in backscatter at each altitude. We then correct the lidar backscatter to dry conditions to produce the dry aerosol backscatter coefficient profile. The dry aerosol backscatter coefficient profile is linearly scaled to collocated surface measurements of aerosols, CCN, and INP to produce corresponding vertical profiles. Combining lidar backscatter profiles with aerosol and cloud nucleation measurements leads to a more realistic representation of vertical distributions of aerosol properties. The method could be readily applied to lidar measurements in future field campaigns.

Chen, Bo [Texas A & M Univ., College Station, TX (↗

DOE OSTI2026

Laboratory and field assessment of mid-infrared absorption (MIRA) instrument performance for methane and ethane dry mole fractions

Concurrent measurements of methane (CH 4 ) and ethane (C 2 H 6 ) can be used to identify and separate methane sources, as ethane is present in thermogenic sources (e.g., oil and natural gas) but not in biogenic sources (e.g., agriculture). In this study, we evaluated the performance of multiple Aeris MIRA Ultra instruments (Versions 1 and 2) through controlled laboratory tests and tower-based deployments under field conditions. The systems were modified with an external pump, flow control, a Nafion dryer, and a custom-built auxiliary box to automate the system and transmit near real-time data. We determined the best calibration approach for our application, given practical limitations, to be a full calibration cycle (with ambient and high calibration cylinders) about once per day and an ambient calibration cylinder sampled hourly. Measurement uncertainty was assessed, including the uncertainty due to instrument noise as a function of calibration frequency, uncertainty in the water vapor correction, and cylinder assignment uncertainty. Instrument noise was the dominant source of uncertainty for C 2 H 6 , while the water vapor correction dominated the CH 4 uncertainty. For Version 2 systems with hourly calibrations and a Nafion dryer with counterflow, the mean total uncertainty, including both systematic errors and noise, of hourly averages was 0.8–3.0 ppb CH 4 and 0.35–0.37 ppb C 2 H 6 . Laboratory intercomparisons showed network compatibility within 1.2 ppb CH 4 and 0.23 ppb C 2 H6, and a collocated deployment with a NOAA Picarro system agreed within 1.8 ppb CH 4 . Instrument noise varied substantially amongst the instruments, with errors reaching up to 11 ppb CH 4 and 2 ppb C 2 H 6 for hourly means, with similar variability indicated in a 50 h cylinder test. With appropriate engineering and calibration, the Aeris MIRA Ultra has the potential to distinguish regional methane emission sources in many field settings.

03 NATURAL GAS↗

DOE OSTI2015

A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

14 SOLAR ENERGY↗

DOE OSTI2016

Evaluation of three lidar scanning strategies for turbulence measurements

Abstract. Several errors occur when a traditional Doppler beam swinging (DBS) or velocity–azimuth display (VAD) strategy is used to measure turbulence with a lidar. To mitigate some of these errors, a scanning strategy was recently developed which employs six beam positions to independently estimate the u, v, and w velocity variances and covariances. In order to assess the ability of these different scanning techniques to measure turbulence, a Halo scanning lidar, WindCube v2 pulsed lidar, and ZephIR continuous wave lidar were deployed at field sites in Oklahoma and Colorado with collocated sonic anemometers.Results indicate that the six-beam strategy mitigates some of the errors caused by VAD and DBS scans, but the strategy is strongly affected by errors in the variance measured at the different beam positions. The ZephIR and WindCube lidars overestimated horizontal variance values by over 60 % under unstable conditions as a result of variance contamination, where additional variance components contaminate the true value of the variance. A correction method was developed for the WindCube lidar that uses variance calculated from the vertical beam position to reduce variance contamination in the u and v variance components. The correction method reduced WindCube variance estimates by over 20 % at both the Oklahoma and Colorado sites under unstable conditions, when variance contamination is largest. This correction method can be easily applied to other lidars that contain a vertical beam position and is a promising method for accurately estimating turbulence with commercially available lidars.

Newman, Jennifer F.↗

DOE OSTI2015

Consistent satellite XCO 2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm

Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO 2 ) are required for carbon cycle and climate related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO 2 , the column-averaged dry-air mole fraction of CO 2 . BESD has been initially developed for SCIAMACHY XCO 2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO 2 product. GOSAT BESD XCO 2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO 2 from GOSAT and present detailed comparisons with ground-based observations of XCO 2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO 2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO 2 . For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT-SCIAMACHY (linear correlation coefficient r = 0.82), −0.34 ± 1.37 ppm ( r = 0.86) for GOSAT-TCCON and 0.10 ± 1.79 ppm ( r = 0.75) for SCIAMACHY-TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (±2 h, 10° × 10° around TCCON sites), i.e., the observed air masses are not exactly identical, but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO 2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multi-seasonal measurements of the ground-level atmospheric ice-nucleating particle abundance on the North Slope of Alaska

Atmospheric ice-nucleating particles (INPs) are an important subset of aerosol particles that are responsible for the heterogeneous formation of ice crystals. INPs modulate the arctic cloud phase (liquid vs. ice), resulting in implications for radiative feedbacks. The number of arctic INP studies investigating specific INP episodes or sources increased recently. However, existing studies are based on short-duration field data, and long-term datasets are lacking. Continuous, long-term measurements are key to determining the abundance and variability of ambient arctic INPs and constraining aerosol–cloud interactions, e.g., to verify and/or improve simulations of mixed-phase clouds. Here, we present a new long-duration INP dataset from the Arctic: 2 years of predominantly immersion-mode INP concentrations (n INP ) measured continuously at the National Oceanic and Atmospheric Administration's Barrow Atmospheric Baseline Observatory (BRW) on the North Slope of Alaska. A portable ice nucleation experiment chamber (PINE-03), which simulates adiabatic expansion cooling, was used to directly measure the ground-level INP abundance with an approximately 12 min time resolution from October 2021 to December 2023. We document PINE-03 n INP measurements as well as estimated ice nucleation active surface site density (n s ) over a wide range of heterogeneous freezing temperatures from −16 to −31 °C from which we introduce new season-specific parameterizations suitable for modeling mixed-phase clouds. Collocated aerosol and meteorological data were analyzed to assess the correlation between ambient n INP , air mass origin region, and meteorological variability. Our findings suggest (1) very high freezing efficiency of INPs across the measured temperatures (n s ≈ 2×10 8 –10 10 m −2 for −16 to −31 °C), which is a factor of 10–1000 times greater efficiency as compared to that found in the previous mid-latitude INP measurements in fall using the same instrument; (2) surprisingly high n INP (≥ 1 L −1 at −25 °C) for the examined temperatures throughout the year that were not measured by PINE-03 at other sites; and (3) high n INP in spring, possibly related to arctic haze episodes. Relatively low concentrations of aerosol surface area and contrasting high-INP concentrations at BRW relative to mid-latitude sites are the possible reasons for the observed high freezing efficiency.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux ( H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events – such as the 2015 El Niño – on surface turbulent fluxes and GPP.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data

Determining the spatial distribution and temporal development of evaporation at regional and global scales is required to improve our understanding of the coupled water and energy cycles and to better monitor any changes in observed trends and variability of linked hydrological processes. With recent international efforts guiding the development of long-term and globally distributed flux estimates, continued product assessments are required to inform upon the selection of suitable model structures and also to establish the appropriateness of these multi-model simulations for global application. In support of the objectives of the Global Energy and Water Cycle Exchanges (GEWEX) LandFlux project, four commonly used evaporation models are evaluated against data from tower-based eddy-covariance observations, distributed across a range of biomes and climate zones. The selected schemes include the Surface Energy Balance System (SEBS) approach, the Priestley–Taylor Jet Propulsion Laboratory (PT-JPL) model, the Penman–Monteith-based Mu model (PM-Mu) and the Global Land Evaporation Amsterdam Model (GLEAM). Here we seek to examine the fidelity of global evaporation simulations by examining the multi-model response to varying sources of forcing data. To do this, we perform parallel and collocated model simulations using tower-based data together with a global-scale grid-based forcing product. Through quantifying the multi-model response to high-quality tower data, a better understanding of the subsequent model response to the coarse-scale globally gridded data that underlies the LandFlux product can be obtained, while also providing a relative evaluation and assessment of model performance. Using surface flux observations from 45 globally distributed eddy-covariance stations as independent metrics of performance, the tower-based analysis indicated that PT-JPL provided the highest overall statistical performance (0.72; 61 W m –2 ; 0.65), followed closely by GLEAM (0.68; 64 W m –2 ; 0.62), with values in parentheses representing the R 2 , RMSD and Nash–Sutcliffe efficiency (NSE), respectively. PM-Mu (0.51; 78 W m –2 ; 0.45) tended to underestimate fluxes, while SEBS (0.72; 101 W m –2 ; 0.24) overestimated values relative to observations. A focused analysis across specific biome types and climate zones showed considerable variability in the performance of all models, with no single model consistently able to outperform any other. Results also indicated that the global gridded data tended to reduce the performance for all of the studied models when compared to the tower data, likely a response to scale mismatch and issues related to forcing quality. Rather than relying on any single model simulation, the spatial and temporal variability at both the tower- and grid-scale highlighted the potential benefits of developing an ensemble or blended evaporation product for global-scale LandFlux applications. Hence, challenges related to the robust assessment of the LandFlux product are also discussed.

58 GEOSCIENCES↗

DOE OSTI2020

US East Coast synthetic aperture radar wind atlas for offshore wind energy

We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from -0.8 to 0.6 m s -1 . Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to -0.2 to 0.0 m s -1 , while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s -1 . The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s -1 for SAR and less than 0.2m s -1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

17 WIND ENERGY↗

DOE OSTI2024

Offshore low-level jet observations and model representation using lidar buoy data off the California coast

Abstract. Low-level jets (LLJs) occur under a variety of atmospheric conditions and influence the available wind resource for wind energy projects. In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas administered by the Bureau of Ocean Energy Management: Humboldt and Morro Bay. The wind profile observations from the lidars and collocated near-surface meteorological stations (4–240 m) provide valuable year-long analyses of offshore LLJ characteristics at heights relevant to wind turbines. At Humboldt, LLJs were associated with flow reversals and north-northeasterly winds, directions that are more aligned with terrain influences than the predominant northerly flow. At Morro Bay, coastal LLJs were observed primarily during northerly flow as opposed to the predominant north-northwesterly flow. LLJs were observed more frequently in colder seasons within the lowest 250 m a.s.l. (above sea level), in contrast with the summertime occurrence of the higher-altitude California coastal jet influenced by the North Pacific High, which typically occurs at heights of 300–400 m. The lidar buoy observations also validate LLJ representation in atmospheric models that estimate potential energy yield of offshore wind farms. The European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) was unsuccessful at identifying all observed LLJs at both buoy locations within the lowest 200 m. An extension of the National Renewable Energy Laboratory (NREL) 20-year wind resource dataset for the Outer Continental Shelf off the coast of California (CA20-Ext) yielded marginally greater captures of observed LLJs using the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary layer (PBL) scheme than the 2023 National Offshore Wind dataset (NOW-23), which uses the Yonsei University (YSU) scheme. However, CA20-Ext also produced the most LLJ false alarms, which are instances when a model identified an LLJ but no LLJ was observed. CA20-Ext and NOW-23 exhibited a tendency to overestimate the duration of LLJ events and underestimate LLJ core heights.

17 WIND ENERGY↗

DOE OSTI2018

Calibrated Radar Wind Profiler Precipitation Observations and Vertical Velocity Retrievals

A radar wind profiler (RWP) data set collected during the 2-year Department of Energy Atmospheric Radiation Measurement (ARM) Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign is used to estimate vertical velocity, designate bulk meteorological echo classification (e.g., convective, stratiform), and provide calibrated radar reflectivity factor Z measurements (calibrated against collocated ground disdrometer and raingauge observations from T3). Additional fields include surface precipitation from rain gauge and ARM InterpolatedSonde streams utilized in the echo classification methods. This dataset follows methods established in Giangrande et al. [2013] and Giangrande et al. [2016] for informing on convective vertical velocities, mass flux and area coverage in Amazon convective cells from ARM RWP systems. If RWP datastreams were unavailable on a given date, files still contain raingauge and InterpolatedSonde information.

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 OSTI2026

Wind and Temperature Consensus at Horn Point, HU-Beltsville, Piney Run (Maryland) in support of CoURAGE

The Maryland Department of the Environment (MDE) operates a ground-based atmospheric profiling network consisting of collocated radar wind profilers (RWP) and radio acoustic sounding systems (RASS) as part of its Ambient Air Monitoring Program. This network provides continuous observations of wind and temperature structure in the lower troposphere to support air quality forecasting, regulatory analysis, and atmospheric research. The network currently includes three fixed sites across Maryland: Horn Point (HP, lower eastern shore) [38.587525°,-76.141006°], Howard University-Beltsville (HUB, central Maryland) [39.055277°, -76.878632°], and Piney Run (PR, western Maryland) [39.705950°, -79.012000°] The network is designed to capture regional variability in atmospheric transport and boundary-layer processes. These systems measure vertical profiles of horizontal wind speed and direction using Doppler radar techniques, with observations typically spanning from ~100 m above ground level up to approximately 2.5–4 km. Measurements are derived from the Doppler shift of backscattered electromagnetic signals, enabling retrieval of wind vectors at multiple altitudes with high temporal resolution (e.g., 30-minute averages reported every 6 minutes). Each radar wind profiler is paired with a Radio Acoustic Sounding System (RASS) to provide profiles of virtual temperature in the lower atmosphere (~100–200 m AGL) by measuring the propagation speed of acoustic waves. Together, the RWP/RASS system yields a coupled data set of thermodynamic and kinematic atmospheric structure, including additional parameters such as vertical velocity, radial velocity, signal-to-noise ratio, and spectral width for advanced analysis. There are two types of files for each station: wind data (files with a "w" prefix) and virtual temperature RASS data (files with a "t" prefix). The wind data files are in the format wYYDDD.cns, where YY is the 2-digit year and DDD is the day of the year. The RASS virtual temperature data files are in the format tYYDDD.cns. Each record has the following header structure: Line 1 : Station Name RASS files Line 2 : RASS rev DeTect_2.0, WINDS files Line 2 : WINDS rev ATI 5.1 Line 3 : N latitude, W longitude, and site elevation (m) Line 4 : Date and begin time of consensus: yy mm dd hh mn ss plus # minutes to add to get UTC Line 5 : Consensus averaging time (minutes); number of beams; number of range gates Line 6 : Number of records required to make consensus (num) total number of records (tot) and the consensus window size (m/s) in the format: num:tot (window) RASS files Line 7 : no. of coded cells, no. of spec, pulse width (ns), and inter-pulse period (µs), WINDS files Line 7 : No. of coded cells, no. of spectra, pulse width (ns), and inter-pulse period (µs), each with a pair of values: first value is for oblique beams, second for vertical RASS files Line 8 : Full scale Doppler value (m/s) Delay to first gate (ns) Number of gates Spacing of gates (ns), WINDS files Line 8 : Full scale Doppler velocity (m/s), oblique and vertical Vertical correction applied to oblique beams? (0 = no, 1 = yes) Delay to first gate (ns), oblique and vertical Number of gates, oblique and vertical Spacing of gates (ns), oblique and vertical Line 9 : Azimuth and elevation (9s indicate vertical beam not used) RASS files Line 10, values : HT = Height above ground (km), T = Uncorrected virtual temperature consensus (deg C), Tc = Corrected virtual temperature consensus (deg C), W = Vertical wind consensus (9s indicate vertical beam not used, w-component, positive upward, m/s), CNT = Number of records that made consensus (for the 3 values in same order), SNR = Average signal to noise ratio (dB) of records in consensus (same order) WINDS files Line 10, values : HT = Height above ground (km), SPD = Wind speed (m/s), DIR = Wind direction (deg E of N from N), RAD = Radial velocities for each beam (m/s) in order given in azimuth and elevation line (positive toward radar; 9s indicate vertical beam not used, CNT = Number of records that made consensus, SNR = Average signal to noise ratio (dB) of records in consensus

{"wind speed and direction",temperature}↗

DOE OSTI2015

Ground Penetrating Radar, Utqiagvik (Barrow), Alaska

This is 500 MHz Ground Penetrating Radar collected sporadically along the AB Line from 2012 to 2014 and along L2 (Site 0) from 2011 to 2014. A smaller area along AB line has also been surveyed in 2013 and 2014. Some of the surveys have been performed in the summer and fall season to estimate thaw depth (thaw depth measured right before the start of the freezing season have been used as proxy for active layer thickness). Some of the surveys have been performed in late fall when a thin snow layer was covering the ground and thus provide information on both thaw depth and snow depth. Finally, some surveys have been performed in the winter and provide snow depth only. The used velocity to calculate depth from GPR travel time have been estimated based on the site characteristics. Some collocated measurement of thaw and snow depth using a tile probe are also provided. The archive contains 17 *.csv datafile and 18 *.csv metadata files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Improving Lidar Turbulence Estimates for Wind Energy

Remote sensing devices (e.g., lidars) are quickly becoming a cost-effective and reliable alternative to meteorological towers for wind energy applications. Although lidars can measure mean wind speeds accurately, these devices measure different values of turbulence intensity (TI) than an instrument on a tower. In response to these issues, a lidar TI error reduction model was recently developed for commercially available lidars. The TI error model first applies physics-based corrections to the lidar measurements, then uses machine-learning techniques to further reduce errors in lidar TI estimates. The model was tested at two sites in the Southern Plains where vertically profiling lidars were collocated with meteorological towers. This presentation primarily focuses on the physics-based corrections, which include corrections for instrument noise, volume averaging, and variance contamination. As different factors affect TI under different stability conditions, the combination of physical corrections applied in L-TERRA changes depending on the atmospheric stability during each 10-minute time period. This stability-dependent version of L-TERRA performed well at both sites, reducing TI error and bringing lidar TI estimates closer to estimates from instruments on towers. However, there is still scatter evident in the lidar TI estimates, indicating that there are physics that are not being captured in the current version of L-TERRA. Two options are discussed for modeling the remainder of the TI error physics in L-TERRA: machine learning and lidar simulations. Lidar simulations appear to be a better approach, as they can help improve understanding of atmospheric effects on TI error and do not require a large training data set.

17 WIND ENERGY↗