The Cloud Condensation Nuclei (CCN) Counter measures nucleied particle number concentration at different supersaturation. Rather than the traditional version of CCN counter where the supersaturation ratio is controlled by adjusting the temperature difference between coolers and thermocouples, in this version the supersaturation ratio is adjusted by scanning the sheath and sampling flow. This dataset contains ASCII text files listing particle number concentration under different supersaturation ratio using CCN mostly over investigator-defined sampling periods. It covers most of the ICE-T campaign period except RF13.

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. Therefore, the CCN retrieved from the CAMS aerosol reanalysis succeed the sole use of AOD as a proxy for global CCN. This CCN dataset features CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2024 with daily frequency and a spatial resolution of 0.75×0.75 degree and 60 vertical levels. Apart from the CAMSRA data, which is available every 3 hours, CCN are currently only computed once a day at 00:00 UTC. The data comprises 3-D fields of total CCN computed for six different supersaturations (s: 0.1, 0.2, 0.4, 0.6, 0.8 and 1 %) and 3-D CCN fields containing aerosol species CCN from sulfate (SO4), hydrophilic black carbon (BCh) and organic matter (OMh) and three size bins of sea salt aerosol (SS) computed for two supersaturations (s: 0.02 % and 0.8 %) comprising additional aerosol information in the lower and upper supersaturation range, respectively. The current choice of data frequency, resolution and variable dependencies such as supersaturation is made regarding general interest and suitability as well as file size, data storage and computational costs. This dataset offers the opportunity to be used for evaluation of general circulation and earth system models as well as in studies of aerosol-cloud interactions.

The file name of the data sets is composed as follows.

project: QUAERERE (Quantifying aerosol-cloud-climate effects by regime) experiment: CCNCAMS (Cloud condensation nuclei derived from the CAMS reanalysis) version: v1 dataset: Total_CCN (total cloud condensation nuclei) and Aerosol_species_CCN (aerosol species cloud condensation nuclei) year: 2003 to 2024 mon: 1 to 12

Acknowledgement: This dataset was generated using Copernicus Atmosphere Monitoring Service information [2003-2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The source data is downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview)

This data set contains NSF/NCAR C-130 CCN (Cloud Condensation Nuclei) Number Concentration Data collected during the WE-CAN (Western Wildfire Experiment for Cloud Chemistry, Aerosol, Absorption and Nitrogen) field project from 24 July through 13 September 2018. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleations particles (INP). This network enables the investigation of small-scale variations in complex terrain.This specific dataset provides the cleaned data recorded from the CloudPuck, an in house instrument made by Handix Scientific that counts CCN concentrations. The CloudPuck was deployed at the sites for the summer/fall of 2022 before winter conditions were too harsh to maintain the instrument. For more information on the the CloudPuck or how the raw data are processed, see the read me.

Context

From GitHub: "Since 2009, at least 177 of America’s schools experienced a shooting. These tragedies are as diverse as our nation, but the depth of trauma is hard to convey. There is no standard definition for what qualifies as a school shooting in the US. Nor is there a universally accepted database. So CNN built our own. We examined 10 years of shootings on K-12 campuses and found two sobering truths: School shootings are increasing, and no type of community is spared."

Acknowledgements

This data was posted by CNN on GitHub. Read the full story. Image from StockSnap.io. Per GitHub, this data is licensed under the MIT license.

A Certificate of Convenience and Necessity (CCN) is issued by the Public Utility Commission of Texas (PUCT), and authorizes a utility to provide water and/or sewer service to a specific service area. The CCN obligates the water or sewer retail public utility to provide continuous and adequate service to every customer who requests service in that area. The maps and digital data provided in the Water and Sewer CCN Viewer delineate the official CCN service areas and CCN facility lines issued by the PUCT and its predecessor agencies. This dataset is a Texas statewide polygon layer of sewer CCN service areas. The CCNs were digitized from Texas Department of Transportation (TxDOT) county mylar maps. The mylar maps were the base maps on which the CCNs were originally drawn and maintained. CCNs are currently created and maintained using digitizing methods, coordinate geography or imported from digital files submitted by the applicant. TxDOT digital county urban road files are used as the base maps on which the CCNs are geo-referenced. It is best to view the sewer CCN service area data in conjunction with the sewer CCN facility line data, since these two layers together represent all of the retail public sewer utilities in Texas.*Important Notes: The CCN spatial dataset and metadata were last updated on: January 29, 2024The official state-wide CCN spatial dataset includes all types of CCN services areas: water and sewer CCN service areas; water and sewer CCN facility lines. This CCN spatial dataset is updated on a quarterly, or as needed basis using Geographic Information System (GIS) software called ArcGIS 10.8.2.The complete state-wide CCN spatial dataset is available for download from the following website: http://www.puc.texas.gov/industry/water/utilities/gis.aspxThe Water and Sewer CCN Viewer may be accessed from the following web site: http://www.puc.texas.gov/industry/water/utilities/map.htmlIf you have questions about this CCN spatial dataset or about CCN mapping requirements, please e-mail CCN Mapping Staff: water@puc.texas.govTYPE - Indicates whether a CCN is considered a water or a sewer system. If the CCN number begins with a '"1", the CCN is considered a water system (utility). If a CCN number begins with a "2", the CCN is considered a sewer system (utility).CCN_NO - A unique five-digit number assigned to each CCN when it is created and approved by the Commission. *CCN number starting with an ‘N’ indicates an exempt utility.UTILITY - The name of the utility which owns the CCN.COUNTY - The name(s) of the county(ies) in which the CCN exist.CCN_TYPE –One of three types:Bounded Service Area: A certificated service area with closed boundaries that often follow identifiable physical and cultural features such as roads, rivers, streams and political boundaries. Facilities +200 Feet: A certificated service area represented by lines. They include a buffer of a specified number of feet (usually 200 feet). The lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.Facilities Only: A certificated service area represented by lines. They are granted for a "point of use" that covers only the customer connections at the time the CCN is granted. Facility only service lines normally follow along roads and may or may not correspond to distribution lines or facilities in the ground.STATUS – For pending dockets check the PUC Interchange Filing Search

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As part of the 3rd Intensive Campaign of SAFARI 2000, the South African Weather Bureau Aerocommander, JRA, flew 19 missions, for a total of 28 separate flights conducted between August 15th and September 7th, 2000. JRA worked closely with the other Aerocommander, JRA, and was dedicated to the measurement of trace gas and aerosol properties. A suite of trace analyzers (for O3, SO2, CO and NO), laser aerosol probes and atmospheric probes were present for all flights. Other instruments and sampling units present for some of the flights included, a nephelometer (Elias), CO flasks (Novelli) for MOPITT validation purposes, and VOC canisters for the collection and characterization of volatile organic compounds present over various land surface types.

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This data set contains best-estimate cloud condensation nuclei (CCN) spectra for five years of data (2009-2013) at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) observatory at 45-minute time resolution. All CCN spectra span a wide SSw range, 0.0001% to ~30%, which is not measurable by CCN instrumentation alone. This product has been constructed by combining merged aerosol size distribution data (Marinescu, Peter, and Levin, Ezra. SGP Merged Aerosol Size Distribution (CPC+SMPS+APS). United States: N. p., 2019. Web. doi:10.5439/1511037 discussed in: Marinescu, PJ, EJT Levin, D Collins, SM Kreidenweis, and SC van den Heever. 2019. "Quantifying Aerosol Size Distributions and Their Temporal Variability in the Southern Great Plains, USA." Atmospheric Chemistry and Physics 19(18): 11985–12006, https://doi.org/10.5194/acp-19-11985-2019) with measurements of aerosol hygroscopicity from the humidified tandem differential mobility analyzer (HTDMA) and measured CCN distributions over a limited water supersaturation (SSw) range. Nephelometer and aerosol chemical speciation monitor (ACSM) data are used to resolve discrepancies between the above measurements, and as quality control measures.

Unlock the full potential of CNN broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

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CalNex was a joint project of The California Air Resources Board (CARB), the National Oceanic and Atmospheric Administration (NOAA) and the California Energy Commission (CEC). This project was a joint field study of atmospheric processes over California and the eastern Pacific coastal region in 2010. The Pacific Marine Environmental Laboratory (PMEL) Atmospheric Chemistry Group made Aerosol chemical, physical, and optical measurements aboard the R/V Atlantis from May 14 through June 8, 2010. cdm_data_type=Trajectory cdm_trajectory_variables=trajectory_id comment=CCN Measurements: A Droplet Measurement Technologies CCN Counter (DMT CCNC) was used to determine CCN concentrations of sub-1 um particles at supersaturations ranging from 0.1 to 0.62%. A multijet cascade impactor with a 50% aerodynamic cut-off diameter of 1.1 um was upstream of the CCNC. The sampled air was dried prior to reaching the CCNC. Details concerning the characteristics of the DMT CCN counter can be found in Roberts and Nenes [2005] and Lance et al. [2006]. The CCN counter was calibrated before and during the experiment as outlined by Lance et al. [2006]. The uncertainty associated with the CCN number concentration is estimated to be less than +/- 10% [Roberts and Nenes, 2005]. Uncertainty in the instrumental supersaturation is less than +/- 10% for the operating conditions of this experiment [Roberts and Nenes, 2005].

The data are in 10 second time intervals and include CCN concentration (in n/cm^3), CCN/CN ratio, and Supersaturation (in %).

Lance, S., J. Medina, J.N. Smith, and A. Nenes, Mapping the operation of the DMT continuous flow CCN counter, Aer. Sci. Tech., 40, 242 - 254, 2006. Roberts, G.C. and A. Nenes, A continuous-flow streamwise thermal gradient CCN chamber for atmospheric measurements, Aer. Sci. Tech., 39, 206 - 221, 2005. contributor_name=Coffman, Derek/NOAA-PMEL/Address: 7600 Sand Pt. Wy. NE,Seattle,WA 98115 /email: derek.coffman@noaa.gov Conventions=COARDS, CF-1.6, ACDD-1.3, NCCSV-1.0 dimensions=time=36001 Easternmost_Easting=-117.143892 featureType=Trajectory geospatial_lat_max=38.56342 geospatial_lat_min=32.631448 geospatial_lat_units=degrees_north geospatial_lon_max=-117.143892 geospatial_lon_min=-122.981272 geospatial_lon_units=degrees_east geospatial_vertical_max=18.0 geospatial_vertical_min=18.0 geospatial_vertical_positive=up geospatial_vertical_units=m infoUrl=https://www.pmel.noaa.gov/acg/data/index.html institution=NOAA keywords_vocabulary=GCMD Science Keywords Northernmost_Northing=38.56342 platform=Atlantis project=CalNex sourceUrl=(local files) Southernmost_Northing=32.631448 standard_name_vocabulary=CF Standard Name Table v70 subsetVariables=trajectory_id, duration, altitude target_sample_rh=60% time_coverage_end=2010-06-08T12:00:00Z time_coverage_start=2010-05-14T12:00:00Z Westernmost_Easting=-122.981272

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This work in the related paper introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of micro-CT images of real microgeometries. The primary goal is to develop an efficient, machine-learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging Convolutional Neural Networks (CNN) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. Particularly, the described approach employs binarized CT images of porous micro-structure as inputs to predict the symmetric second-order permeability tensor, a critical parameter in continuum porous media flow modeling. The methodology comprises four key steps: (1) constructing a dataset of CT images from Bentheim sandstone at different volumetric strain levels; (2) performing pore-scale simulations of single-phase flow using the lattice Boltzmann method (LBM) to generate permeability data; (3) training the CNN model with the processed CT images as inputs and permeability tensors as outputs; and (4) exploring techniques to improve model generalization, including data augmentation and alternative CNN architectures. Examples are provided to demonstrate the CNN’s capability to accurately predict the permeability tensor, a crucial parameter in various disciplines such as geotechnical engineering, hydrology, and material science.

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates. Methods This is an aggregated dataset, consisting of timeseries with in-situ aircraft or ship campaign measurements from ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. CCN, CCN proxies and measurements necessary to identify particle physical and chemical properties and non-marine contributions to particle concentrations are included. All missing or invalid data flags are converted to ‘Na’. Some datasets have already been filtered for inlet shattering in-cloud, and measurement contamination from ship exhausts; however, methods of filtering ship exhaust vary by campaign. For the NAAMES ship campaigns, the research ship exhaust was identified and filtered out based on the wind direction relative to the ship exhaust and total particle counts. For CAPRICORN2, wind direction, total particle counts, black carbon particle concentration, and CO and CO2 measurements were also utilized in filtering ship exhaust. Finally, the MARCUS ship exhaust contamination periods are identified and filtered using total particle counts and CO measurements. The aggregated dataset is further filtered to eliminate measurements influenced by in-cloud inlet shattering and averaged at 10 second intervals for aircraft measurements and 5-minute intervals for ship measurements (except for CAPRICOR2 which is only publicly available at hourly averaged intervals).

This dataset contains cumulative data-concentrations per cm3 with critical supersaturations (Sc) less than the supersaturation (S) value at the top of the column. Concentrations are normalized to sea level pressure. There are up to three files per flight. The file labeled both (e.g., JY12both.txt) is the best—it is a limited data set for time periods when both DRI CCN spectrometers were sampling ambient and were in agreement with each other over at least an overlapping supersaturation range, sometimes nearly the entire S range from 0.02% to 1% S, or at least at some S value. The other two files are during periods when only one instrument sampled ambient aerosol. The new instrument (e.g., JY12new.txt) is more valid for higher S (>~ 0.1%) whereas the old instrument is more valid for S <~ 0.1%. NOTE: The old spectrometer on the first 3 and last 3 flights has been updated. All concentrations were multiplied by 0.71 for only the old spectrometer for these 6 flights to correct an error in the original files. For 5 of these 6 there is only data from the old spectrometer. July 6 is the only one of these with data from both instruments. All flights have been carefully checked by the data source and the other 7 flights did not need this correction.

Five CCN field campaigns were conducted during 2016 to 2019 in Eastern China. The stations are located in Nanjing (NJ, 32.21°N, 118.70°E, 22 m above sea level), Lushan (LS, 29.58°E, 115.98°E, 1165 m above sea level), Shenzhen (SZ, 22.48°N, 114.56°E, 230 m above sea level), and Zhanjiang (ZJ, 21.01°N, 110.53°E, 51 m above sea level), respectively.Please contact to wang@tropos.de, clu@nuist.edu.cn, or niusj@nuist.educn for requesting these CCN data.

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