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This dataset includes atmospheric measurements obtained with the Black Swift Technologies S0 uncrewed aircraft system at the ARM SGP facility and a nearby mesonet site (Marshall). Flights were conducted over a one-week period, alongside platforms deployed by the University of Nebraska - Lincoln.
Real-time, immersion mode ambient ice nucleating particle (INP) concentrations were measured at ARM’s Southern Great Plains (SGP) site during the AGINSGP field experiment using a continuous flow diffusion chamber (CFDC) from Pacific Northwest National Laboratory (PNNL). The PNNL-CFDC is an ice-thermal gradient diffusion chamber that optically detects the freezing of single aerosol particles (Kulkarni et al. 2020). The instrument was located in the guest instrument facility (GIF) and measured INP concentrations from April 8th to April 29th. Air was drawn into the GIF through custom aluminum stack inlets (6” inner diameter) attached vertically to the outer GIF wall and the GIF outside platform. A blower pulled air through the stacks at a velocity of 1 m/s. Aerosol instruments subsampled via wall ports, through either 3/8” or 1/4" copper lines, depending on sample flow of the instrument. Kulkarni, G., Hiranuma, N., Möhler, O., Höhler, K., China, S., Cziczo, D. J., & DeMott, P. J. (2020). A new method for operating a continuous-flow diffusion chamber to investigate immersion freezing: Assessment and performance study. Atmospheric Measurement Techniques, 13(12), 6631–6643. https://doi.org/10.5194/amt-13-6631-2020.
Real-time, immersion mode ambient ice nucleating particle (INP) concentrations were measured at ARM’s Southern Great Plains (SGP) site during the AGINSGP field experiment using a continuous flow diffusion chamber (CFDC) from Colorado State University (CSU). The CSU-CFDC is an ice-thermal gradient diffusion chamber that optically detects the freezing of single aerosol particles (Rogers 1988; Rogers et al. 2001; and Eidhammer et al. 2010). The instrument was located in the guest instrument facility (GIF) and measured INP concentrations from April 8th to April 29th. Air was drawn into the GIF through custom aluminum stack inlets (6” inner diameter) attached vertically to the outer GIF wall and the GIF outside platform. A blower pulled air through the stacks at a velocity of 1 m/s. Aerosol instruments subsampled via wall ports, through either 3/8” or 1/4" copper lines, depending on sample flow of the instrument. Rogers, D. C., Development of a continuous flow thermal gradient diffusion chamber for ice nucleation studies, Atmospheric Research, 22(2), 149-181, doi:10.1016/0169-8095(88)90005-1, 1988. Rogers, D. C., P. J. DeMott, S. M. Kreidenweis and Y. Chen, A continuous flow diffusion chamber for airborne measurements of ice nuclei, J. Atmos. Oceanic Technol., 18, 725-741, doi:10.1175/1520-0426(2001)018<0725:ACFDCF>2.0.CO;2, 2001. Eidhammer, T., DeMott, P. J., Prenni, A. J., Petters, M. D., Twohy, C. H., Rogers, D. C., Stith, J., Heymsfield, A., Wang, Z., Haimov, S., French, J., Pratt, K., Prather, K., Murphy, S., Seinfeld, J., Subramanian, R., and Kreidenweis, S. M., Ice initiation by aerosol particles: Measured and predicted ice nuclei concentrations versus measured ice crystal concentrations in an orographic wave cloud, J. Atmos. Sci., 67, 2417-2436, doi:10.1175/2010JAS3266.1, 2010
Real-time, immersion mode ambient fluorescent bioaerosol concentrations were measured at ARM’s Southern Great Plains (SGP) site during the AGINSGP field experiment using a Wideband Integrated Bioaerosol Sensor (DMT Inc.; WIBS-4A). The WIBS-4A is an optical particle counter that also measures single particle fluorescence in three channels, along with particle asymmetry. Particles are grouped into different types depending on which channels they fluoresce in. The instrument was located in the guest instrument facility (GIF) and measured fluorescent particle concentrations from April 7th to April 29th. Data presented here are the concentrations for each particle type, binned in 5 minute increments. Air was drawn into the GIF through custom aluminum stack inlets (6” inner diameter) attached vertically to the outer GIF wall and the GIF outside platform. A blower pulled air through the stacks at a velocity of 1 m/s. Aerosol instruments subsampled via wall ports, through either 3/8” or 1/4" copper lines, depending on sample flow of the instrument. Particles were dried using a silica diffusion dryer prior to analysis.
There is a need for long-term observations of cloud and precipitation fall speeds in validating and improving rainfall forecasts from climate models. To this end, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) site at Lamont, Oklahoma, hosts five ARM Doppler lidars that can measure cloud and aerosol properties. In particular, the ARM Doppler lidars record Doppler spectra that contain information about the fall speeds of cloud and precipitation particles. In this dataset, we provide the Doppler spectra from the ARM Doppler lidar at C1 recorded in April 2022 during ARMing the Edge.
The Southern Great Plains (SGP) carbon dioxide flux (CO2FLUX) measurement systems provide half-hour average fluxes of CO2, H2O (latent heat), sensible heat, and momentum. The systems use the eddy covariance technique, which computes the fluxes from the vertical wind speed in combination with the concentrations of CO2 and H2O, temperature, and horizontal wind speed, respectively. A 3D sonic anemometer obtains the wind components and the temperature, while an infrared gas analyzer measures CO2 and H2O. A sub-system also measures half-hour averages of radiation, meteorological, and soil measurements.
An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.
This study introduces a new technique to compute direct normal irradiance (DNI) for improving the National Solar Radiation Data Base (NSRDB). A finite-surface integration algorithm is developed to compute solar radiation in differential solid angles and efficiently infer its contribution to a surface perpendicular to the solar direction. A lookup table of cloud bi-directional transmittance distribution function (BTDF) is developed by use of the discrete ordinates radiative transfer (DISORT) model for possible solar and observing directions and various cloud optical and microphysical properties. In each solar incident direction, DNI is given by the cloud BTDFs from approximately 200 differential solid angles. The simulated DNI is calibrated and evaluated using surface observations at the National Renewable Energy Laboratory's (NREL's) Solar Energy Research Laboratory (SRRL) and the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility.
Abstract Airborne measurements of non‐refractory bulk aerosol chemical composition, aerosol size distributions, and cloud condensation nuclei (CCN) were conducted onboard a research aircraft during the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems field campaign in the spring and summer of 2016. A CCN closure study for the entire campaign period was performed where measured CCN concentrations at 0.24% and 0.46% supersaturation were compared with the predicted CCN concentrations calculated using κ ‐Köhler theory, three different assumptions of aerosol mixing state, and various assumptions about hygroscopicity, density, and the insoluble fraction of organic particles. We found that the Closure Ratio (CR), calculated as the ratio of predicted to measured CCN concentrations, was equal to one under two different aerosol mixing state assumptions: (a) all particles are composed of 100% organic particles, and (b) particles are externally mixed and composed of pure sulfates, nitrates, and organic particles assuming hygroscopicity values for organic particles ( ) between 0.04 and 0.17. The use of internal mixing state assumption often led to overprediction of CCN concentrations but the agreement within ±20% with the measured CCN concentrations was observed under certain closure permutations. A similar agreement, that is, within ±20%, was also observed using permuted parameters concerning density (1 and 1.5 g cm −3 ) and an insoluble fraction (0% and 20%) of organic particles. These findings may provide constraints on to predict CCN concentrations at a remote continental Southern Great Plains site.
Abstract Long-term weather and climate observatories can be affected by the changing environments in their vicinity, such as the growth of urban areas or changing vegetation. Wind plants can also impact local atmospheric conditions through their wakes, characterized by reduced wind speed and increased turbulence. We explore the extent to which the wind plants near an atmospheric measurement site in the central United States have affected their long-term measurements. Both direct observations and mesoscale numerical weather prediction simulations demonstrate how the wind plants induce a wind deficit aloft, especially in stable conditions, and a wind speed acceleration near the surface, which extend $$\sim 30$$ ∼ 30 km downwind of the wind plant. Turbulence kinetic energy is significantly enhanced within the wind plant wake in stable conditions, with near-surface observations seeing an increase of more than 30% a few kilometers downwind of the plants.
Mesoscale numerical weather prediction (NWP) models are generally considered more accurate than reanalysis products in characterizing the wind resource at heights of interest for wind energy, given their finer spatial resolution and more comprehensive physics. However, advancements in the latest ERA-5 reanalysis product motivate an assessment on whether ERA-5 can model wind speeds as well as a state-of-the-art NWP model – the Weather Research and Forecasting (WRF) Model. We consider this research question for both simple terrain and offshore applications. Specifically, we compare wind profiles from ERA-5 and the preliminary WRF runs of the Wind Integration National Dataset (WIND) Toolkit Long-term Ensemble Dataset (WTK-LED) to those observed by lidars at a site in Oklahoma, United States, and in a United States Atlantic offshore wind energy area. We find that ERA-5 shows a significant negative bias (~-1ms-1) at both locations, with a larger bias at the land-based site. WTK-LED-predicted wind speed profiles show a limited negative bias (~-0.5ms-1) offshore and a slight positive bias (~+0.5ms-1) at the land-based site. On the other hand, we find that ERA-5 outperforms WTK-LED in terms of the centered root-mean-square error (cRMSE) and correlation coefficient, for both the land-based and offshore cases, in all atmospheric stability conditions. We find that WTK-LED's higher cRMSE is caused by its tendency to overpredict the amplitude of the wind speed diurnal cycle. At the land-based site, this is partially caused by wind plant wake effects not being accurately captured by WTK-LED.
The site data in a station data product format used to produce the gridded cloud grid data product, sfccldgrid2caracena.
Rapid Refresh 20km forecast model data converted from GRIB data files generated by NCEP.
Rapid Refresh 20km forecast model data converted from GRIB data files generated by NCEP. Subgrids of 4 adjacent points averaged together.