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ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Cryogenic Refrigerator Applied to Freezing Test
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology
This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."
WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology
This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."
Atmospheric Characterization of the US Offshore Sites and Impact on Turbine Performance (Poster)
Reliable, long term offshore atmospheric data is critical to development of the US offshore wind industry. There exists significant lack of meteorological, oceanographic, and geological data at potential US offshore sites. Assessment of wind resources at heights in the range of 25-200m is needed to understand and characterize offshore wind turbine performance. Data from the US Department of Energy owned WindSentinel buoy from two US offshore sites and one European site is analyzed. Low Level Jet (LLJ) phenomena and its potential impact on the performance of an offshore wind turbine is investigated.
The De-Icing Comparison Experiment – ARM Contribution (DICEXACO) Field Campaign Report
Longwave and shortwave radiative fluxes are fundamental quantities regularly observed globally using broadband radiometers. In regions conducive to frost, rime, and snow, ice frequently builds up on sensor windows, contaminating measurements. Since icing occurs under particular meteorological conditions, associated data loss constitutes a climatological bias. Furthermore, the signal caused by ice is difficult to distinguish from that of clouds, hampering efforts to identify contaminated data in post-processing. Because of the sensitivity of radiometers to internal temperature instabilities, there are limitations to using heat as a de-icing method, and consequently substantial amounts of data are lost. The De-Icing Comparison Experiment (D-ICE) was a campaign carried out at $Utqia\dot{g}vik$ (formerly known as Barrow) and Oliktok Point, Alaska, from August 2017 to July 2018. The purpose of D-ICE was to evaluate ventilation and heating technologies developed to mitigate radiometer icing. D-ICE consisted of 20 pyranometers and 5 pyrgeometers operating in various ventilator housings alongside operational stations run by the NOAA Global Monitoring Division (GMD) at NOAA’s Barrow Atmospheric Baseline Observatory. D-ICE also evaluated the sky radiometer (SKYRAD) systems at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) and Oliktok Point (OLI; third ARM Mobile Facility [AMF3]) observatories. In total, 34 systems were evaluated (8 of which were SKYRAD radiometers). All radiometers were monitored continuously using cameras, and a total of more than one million images of sensor domes were archived collectively between the stations. Data collected as part of D-ICE by NOAA can be found through the project web page, https://www.esrl.noaa.gov/psd/arctic/d-ice/. The DICEXACO component of D-ICE (https://www.arm.gov/research/campaigns/nsa2017dicexaco) are the images collected at 10-min intervals at both OLI and NSA and are the subject of this report. Two cameras were installed at each ARM station, one directed towards the SKYRAD tracker and the other towards the SKYRAD global precision spectral pyranometer (PSP).
NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Utqiagvik (Barrow), Alaska, 2012 - 2019
Measurements began in September 2012 and are generally made throughout the snow-free growing season starting from April (pre-thawing) until November (freeze-up) when the instrumentation is taken down for maintenance and instrument calibration. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGB. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGB for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux https://doi.org/10.17190/AMF/1436326. The tower site is adjacent to the NGEE Arctic Tram system with several complementary time-series measurements https://doi.org/10.5440/1558782.This dataset supersedes the previously released dataset "Eddy-Covariance and auxiliary measurements, NGEE-Barrow, 2012-2013" DOI:10.5440/1124200 as this new product includes data for the longer time period processed using consistent methods for the entire record. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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) near Utqiagvik (Barrow), Alaska and 2) multiple areas in 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).
Use of MERRA-2 in the National Solar Radiation Database and Beyond
The National Solar Radiation Database (NSRDB) is a flagship product of NREL that provides solar radiation and ancillary meteorological information through a GIS based portal. This data is provided at a 4kmx4km spatial and 30 minute temporal resolution covering the period between 1998-2015. The gridded data that is distributed by the NSRDB is derived from satellite measurements using the Physical Solar Model (PSM) that contains a 2-stage approach. This 2-stage approach consists of first retrieving cloud properties using measurement from the GOES series of satellites and using that information in a radiative transfer model to estimate solar radiation at the surface. In addition to the satellite data the model requires ancillary meteorological information that is provided mainly by NASA's Modern Era Retrospecitve Analysis for Research and Applications (MERRA-2) 2 model output. This presentation provides an insight into how the NSRDB is developed using the PSM and how the various sources of data including the MERRA-2 data is used during the process.
NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2017 - 2019
Measurements began in July 2017 and are made year-round (2017 seasonal only) on the tussock tundra with discontinuous permafrost at the Council Road Site on the Seward Peninsula. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGC. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGC for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. The majority of the files are *.csv with paired visualization footprint files as *.jpg. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux BASE US-NGC NGEE Arctic Council (Torn, 2020) https://doi.org/10.17190/AMF/1634883. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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) near Utqiagvik (Barrow), Alaska and 2) multiple areas in 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).
Capturing Inter-Annual Variability of PV Energy Production in South Asia
Long-term variability of solar resource is an important factor in planning a utility-scale photovoltaic (PV) generation plant, and annual generation for a given location can vary significantly from year to year. Based on multiple years of solar irradiance data, an exceedance probability is the amount of energy that could potentially be produced by a power plant in any given year. An exceedance probability accounts for long-term variability and climate cycles (e.g., monsoons or changes in aerosols), which ultimately impact PV energy generation. Study results indicate that a significant bias could be associated with relying solely on typical meteorological year (TMY) resource data to capture long-term variability. While the TMY tends to under-predict annual generation overall compared to the P50, there appear to be pockets of over-prediction as well.
Advancing South American Water and Climate Science Through Multi-Decadal Convection-Permitting Modeling
The South America Affinity Group (SAAG) was established in early 2019 by the National Center for Atmospheric Research (NCAR) Water Systems Program as a community effort focused on improving hydroclimate science over South America. SAAG supports large research efforts such as the ANDEX Regional Hydroclimate Program as well as individual research groups. The group started with a dozen members and quickly grew to over 100 participants from more than ten countries. For the past four years, the SAAG has been meeting online every two weeks and has organized sessions at international conferences such as the American Geophysical Union Fall Meeting and the Convection-Permitting Climate Workshop. At the core of the SAAG effort are two multi-decadal convection permitting (CP) model simulations with 4-km grid spacing for historical and future climates over the South American continent. Additionally, a major observational data collection effort has been undertaken, including in-situ station data from South American meteorological and water services, gridded products, satellite-based observations, and field campaign data. In conclusion, this article discusses the research needs and scientific goals that drive this community of scientists with diverse backgrounds and interests.
Volatile Organic Compounds and Meteorological Conditions in the Missouri Ozark AmeriFlux (MOFLUX) Site, 2023
This data set contains measurements of atmospheric components and meteorological conditions of central Missouri, United States during the summer of 2023. Data serve to examine the impact of meteorological conditions relevant to future climate on the emission and transformation of volatile organic compounds (VOCs). During the field campaign, researchers were also able to incorporate opportunistic analyses of the long-range transport of smoke plumes generated from extreme forest fire activities in Canada. VOC measurements were conducted at the Missouri Ozark AmeriFlux (MOFLUX) site (latitude 38.7441, longitude −92.2000) using a proton transfer reaction time of flight mass spectrometer (PTR-ToF-MS 6000 X2). The sampling campaign was conducted during the summer of 2023 (2023-06-25 to 2023-08-12) with measurements being taken at high temporal resolution (1 hour). Particular VOCs analyzed include: Methanol, Acetonitrile, Acetone, Isoprene, Methylvinyl ketone (MVK) and methacrolein (MACr), Benzene, Toluene, Catechol, and Monoterpene. Meteorological parameters included in this dataset were collected from a nearby Columbia Regional Airport (~10 km). Global solar radiation data were measured at a weather site in Ashland, MO, 5.22 km from the MOFLUX tower. The data were accessed using the MesoWest online website (https://mesowest.utah.edu/) provided by the Department of Atmospheric Sciences, University of Utah. Discussion of the data providers, database, and dissemination were highlighted in prior studies (Horel et al., 2002a; Horel et al., 2002b) . Smoke mixing ratios (in mg m−3) were estimated from the High-Resolution Rapid Refresh (HRRR) 3 km weather model for Missouri at 6-hour intervals (Dowell et al., 2022). This dataset contains three data files in comma separate (*.csv) format. Additional metadata are provided: three data dictionaries and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.
Identifying Controlling Variables for Mercury Vapors in Alpha-4 at Y-12: Two Year Data Collection Update
Multiple sensor packages were deployed at Alpha-4 by SRNL, in collaboration with United Cleanup Oak Ridge LLC (UCOR), to monitor mercury vapor concentrations and meteorological parameters. These sensors collected data, inside and outside of the legacy-use facility, for approximately two years. Though data gaps still exist, particularly in colder months, several controlling variables were identified that govern mercury vapor concentrations within Alpha-4. Temperature, barometric pressure gradients, humidity, and wind speed have been identified as controlling variables. A strong positive correlation was seen between mercury vapor concentrations and temperature which generally followed diurnal fluctuations. Temperatures below approximately 10 degrees Celsius did not show any spikes above the PEL, indicating more work can be performed at any time during the winter months – more data should be collected to confirm consistency in this finding. Additionally, spikes in mercury vapor concentrations above that of the permissible exposure limit (PEL; 100 µg/m 3 ) occurred primarily in late afternoon or evening/overnight hours (between 3 PM and 6 AM), which suggests D&D operations might be best scheduled during morning or daytime hours prior to the late afternoon. However, a limited number of spikes did occur outside of the identified window, although this may be attributed to disturbances in air flow and mercury vapor release from work activities performed inside of the Alpha-4 building. The analysis conducted allows for a strong predictive capability for estimating mercury vapor concentrations based upon accurate meteorological parameters. Still, additional data collection, particularly in the winter months, could help to strengthen the predictive power and validate the identified data trends. Further, increased temporal resolution could also help to better characterize the incipient stages of the increases and decreases in the mercury vapor concentration. Continued monitoring support by SRNL at Y-12 is underway at Alpha-4 to further close remaining data gaps and support deactivation and decommissioning work. Within a collaborative effort with UCOR, the SRNL team is collecting mercury vapor data to study the efficacy of a novel mercury suppressant, FerroBlack® which was recently deployed at Alpha-4. In addition, modifications to the current monitoring setup to increase measurement resolution is also being investigated.
PV Fleet Performance Data Initiative (March 2020 Methodology Report)
Data for thousands of systems by multiple sources were transmitted to NREL for evaluation. Initial automated data quality assurance (QA) checks identified hundreds of systems (QA Tier 1) with high-quality meteorological and AC production data and hundreds of QA Tier 2 systems with adequate data quality. A standard RdTools analysis was conducted for these systems, evaluating performance loss on an annualized basis. To date with the systems evaluated so far, we find a median performance loss rate (aka Rd or degradation rates) for the entire fleet of -0.72%/year based on individual inverter data, which is in line with historical degradation rates previously published for modules and systems (-0.5% to -0.9% / yr, Jordan et al. 2016). A fleet-scale distribution based on revenue-grade meter data rather than inverter AC (alternating current) data yields a slightly different result. We are working to understand the difference between this result and the inverter-based result of -0.72%/year.
Surface Meteorological Station - ARL 2m, ancillary flux, Boardman - Raw Data
These data contain measurements from a four-component net radiometer, as well as 2-m temperature, pressure, and relative humidity. In addition, soil moisture and temperature measurements and soil heat fluxes are included.
Surface Meteorological Station - ARL 2m, ancillary flux, Prineville - Raw Data
These data contain measurements from a 4-component net radiometer, as well as 2-m temperature, pressure, and relative humidity (RH). Measurements of soil moisture and temperature and soil heat fluxes also are included in the dataset.