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Dataset results
22 results for “Aerosol radiative forcing”
Data used to create figures in the ACP Letters manuscipt "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" by Regayre et al. (2020)
<p>This dataset was created from perturbed parameter ensembles (PPEs) using the HadGEM-UKCA atmospheric composition climate model. All data needed to reproduce figures in the Regayre et al. (2020) ACP Letters article "The value of remote marine aerosol measurements for constraining radiative forcing uncertainty" are included. Other output from the PPEs can be obtained by contacting the lead author.</p> <p>The following data are included here:</p> <ul> <li>CCN measurement data degraded to match the model-measurement comparison resolution.</li> <li>Unconstrained and constrained CCN<sub>0.2</sub> output from the PPE used to make Figure 1. These compressed files contain 48 .dat files. Each .dat file contains the PPE mean, variance and 95% creidble interval data. Files are named consecutively, containing data from 90<sup>o</sup>S to 90<sup>o</sup>N at 0<sup>o</sup>E, then continuing Eastward. When combined, these files provide data for each latitude/longitude pair at the N48 spatial resolution.</li> <li>A zip file of an netcdf file containing 26-dimensional data for parameter values, used to create the sample of 1 million model variants from our statistical emulators of model output.</li> <li>A zip file containing a folder of files made of one million ones and zeros that indicate the retention/rejection criteria from applying our constraint methodology for various constraint combination scenarios, for each model variant. A value of 1 indicates the model variant was retained. Data in these files is in the same order as the unconstrained sample file of parameter values.</li> <li>Compressed files containing global, annual mean RF<sub>aci</sub> and ERF<sub>aci</sub> values for the unconstrained set of one million model variants. The compressed netcdf files contain RF (ERF), RF<sub>aci</sub> (ERF<sub>aci</sub>) and RF<sub>ari</sub> (ERF<sub>ari</sub>) values.</li> </ul>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Comparison of radiative transfer schemes for the calculation of aerosol radiative forcing in Mars' atmosphere
<p>Output datasets for Figures (fig. 1 to 10) for the intercomparison of radiative transfer algorithms for the calculation of aerosol radiative forcing in the Martian atmosphere.</p>
Data for paper publication 'Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires'
<p>This repository contains version 2.0 data from the aerosol-climate simulations performed with the ECHAM6.3-HAM2.3 model and aerosol lidar profiles, as presented in the paper publication by Heinold et al.: Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires, submitted to Atmos. Chem. Phys. For details, please refer to the enclosed data description (README) file.</p>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
Top-of-the-atmosphere radiative forcing by aerosol due to continuous OCS injection near the tropical tropopause simulated by EMAC
<p>This dataset was prepared for a publication by von Hobe et al. (2023):</p> <p><strong>Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022)</strong></p> <p>In that publication, the data are displayed in Figure 4.</p> <p>The dataset contains additional stratospheric aerosol forcing for injections of 6 Tg S a<sup>-1</sup> OCS for several years over 5 tropical cities at the tropopause (97 hPa) calculated with the EMAC (ECHAM5/MESSy Atmospheric Chemistry) CCM (e.g. Brühl et al., 2018; Schallock et al., 2023). Data are given for a four year time series starting in January 2017.</p> <p>- - - - - - - - - - - -</p> <p>File format:</p> <p> netCDF</p> <p>Index Variables:</p> <p> time, 10 hourly, as 'day since 1997-01-01' (note that the two variables named time4 and time6 are identical)</p> <p>Parameters:</p> <p> SOLFORCCSO: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> TOTFORCCSO: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS were fixed to observations</p> <p> SOLFORCCSO_FREE: instantaneous solar radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p> TOTFORCCSO_FREE: instantaneous total radiative forcing at the top of the atmosphere by aerosol due to continuous OCS injection near the tropical tropopause with surface mixing ratios of OCS allowed to increase from downward transport</p> <p>- - - - - - - - - - - -</p> <p><strong>References:</strong></p> <p>Brühl, C., Schallock, J., Klingmüller, K., Robert, C., Bingen, C., Clarisse, L., Heckel, A., North, P., and Rieger, L.: Stratospheric aerosol radiative forcing simulated by the chemistry climate model EMAC using Aerosol CCI satellite data, Atmos. Chem. Phys., 18, 12845-12857, 10.5194/acp-18-12845-2018, 2018</p> <p>Quaglia, I., Visioni, D., Pitari, G., and Kravitz, B.: An approach to sulfate geoengineering with surface emissions of carbonyl<br> sulfide, Atmos. Chem. Phys., 22, 5757-5773, 10.5194/acp-22-5757-2022, 2022.</p> <p>Schallock, J., Brühl, C., Bingen, C., Höpfner, M., Rieger, L., and Lelieveld, J.: Reconstructing volcanic radiative forcing since 1990, using a comprehensive emission inventory and spatially resolved sulfur injections from satellite data in a chemistry climate model, Atmos. Chem. Phys., 23, 1169-1207, 10.5194/acp-23-1169-2023, 2023.</p> <p>von Hobe, M., Brühl, C., Lennartz, S. T., Whelan, M. E., and Kaushik, A.: Comment on “An approach to sulfate geoengineering with surface emissions of carbonyl sulfide” by Quaglia et al. (2022) , EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-268, 2023.</p>
Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4
<p>Netcdf outputs from CESM2 simulations for publication: Radiative Forcing of Nitrate Aerosols from 1975 to 2010 as Simulated by MOSAIC Module in CESM2-MAM4</p>
Effective radiative forcing of anthropogenic aerosols in E3SMv1
<p>This archive contains processed E3SMv1 simulation data documented the following manuscript: </p> <p>Zhang et al. (2021): Effective radiative forcing of anthropogenic aerosols in E3SMv1: historical changes, causality, decomposition, and parameterization sensitivities. In submission to Atmospheric Chemistry and Physics. </p> <p>The E3SM maint-1.0 code (E3SM github hash 849d9ee, https://github.com/E3SM-Project/E3SM/tree/849d9ee1a4996e7bea9a6dcc53784259d8272ab8) used for nudged simulations was obtained from the E3SM project sponsored by the U.S.Department of Energy, Office of Science, Office of Biological and Environmental Research.</p>
Addressing Observational Gaps in Aerosol Parameters using Machine Learning: Implications to Aerosol Radiative Forcing
<p>This dataset represents Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Absorption Parameter (AP) data over Kanpur, India, sourced from AERONET with initial data gaps of approximately 37%, 62%, and 58% respectively. To reduce these gaps, XGBoost, a machine learning model trained with reanalysis and satellite datasets, was employed with optimized hyperparameter tuning. Using AERONET data for training, XGBoost effectively addressed gaps, improving AOD by 10%, SSA by 23%, and AP by 21%.</p>
Data from "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.
<p>This is the data presented in "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.</p> <p> </p> <p>Please read the README file for explanations about the data.</p> <p> </p>
Spectral dependence of light absorption and direct radiative forcing of rural carbonaceous aerosol in TSP, PM10, PM2.5, and PM0.1 in northwestern China
<p>Black carbon (BC) and brown carbon (BrC) are major light absorbing components of aerosol, affecting visibility, radiative forcing balance and human health. In this study, we investigated the light absorption and radiative forcing of carbonaceous aerosol in the total suspended particle (TSP), coarse particle (PM<sub>10</sub>: particulate matter with an aerodynamic diameter less than 10 μm, Dp≤10 μm), fine particle (PM<sub>2.5</sub>: Dp≤2.5 μm), and nanoparticle (PM<sub>0.1</sub>: Dp≤0.1 μm) in a rural area of Guanzhong Plain, China. Similar variations of light absorption coefficients and the absorption Ångstrom exponent (AAE) of TSP, PM<sub>10</sub>, and PM<sub>2.5</sub> were observed. Lower light absorption coefficients and higher AAEs were obtained for PM<sub>0.1 </sub>compared with other particle sizes. The direct radiative forcing (DRE) efficiency of BC decreased with size bins of TSP, PM<sub>10</sub>, PM<sub>2.5</sub>, and PM<sub>0.1</sub>, respectively. The DRE of BCs for all particle sizes at top atmosphere (TOA), surface atmosphere (SUF) and the whole atmosphere (ATM) were estimated, and the levels in TSP were ~3.7 times higher than those in PM<sub>0.1</sub>. The optical properties of primary and secondary BrC (PBrC and SBrC) in PM<sub>0.1</sub> were further analyzed. The levels of AAEs indicated that the light absorbing of SBrC was more wavelength dependent than PBrC in PM<sub>0.1</sub>. The DRE of BC, PBrC, and SBrC in PM<sub>0.1</sub> were estimated firstly with the values of 19.9, 2.1, and 1.1 Wm<sup>-2</sup> in the ATM, respectively.</p>
CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX)
The Lidar Atmospheric Sensing Experiment (LASE) Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) data set was collected over the Western Atlantic Ocean in July 1996. The overall goal of TARFOX was to reduce uncertainties in the effects of aerosols on climate by determining the direct radiative impacts, as well as the chemical, physical, and optical properties, of the aerosols carried over the western Atlantic Ocean from the United States. LASE is an airborne autonomous DIAL system which produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude down to the surface. Such profiles show the vertical context in which the TARFOX in situ and radiometric measurements are made, thus supporting the vertical extension of the in situ measurements and detecting any unsampled layers or inhomogeneities, which would impact the airborne and satellite radiative flux measurements. Note that the LASE_TARFOX data set is also available under the TARFOX project as the TARFOX_LASE data set. The data files included in these two data sets are identical.
CAR TARFOX Tropospheric Aerosol Radiative Forcing Observational Experiment L1 V1 (CAR_TARFOX_L1C) at GES DISC
CAR TARFOX mission collected data in the western Atlantic Ocean on the effects of tropospheric aerosols on radiation budgets in cloud free skies. The mission also measured the chemical, physical, and optical properties of aerosols.
Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - Scanning Mobility Particle Sizer from Wallops ground station
TARFOX_WALLOPS_SMPS is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Scanning Mobility Particle Sizer (SMPS) data set from Wallops ground station. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - Vaisala Radiosonde data from Wallops ground station
TARFOX_WALLOPS_SONDE is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Vaisala radiosonde data set from balloons launched at Wallops ground station. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - meteorological data from Wallops ground station
TARFOX_WALLOPS_MET is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) Surface Meteorological data set Wallops ground station.The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
Tropospheric Aerosol Radiative Forcing Observational eXperiment - Ames Sun Photometer - University of Washington C-131A aircraft
TARFOX_UWC131A_SUNP Data Set was collected from the 6-channel Sun Photometer flown on the University of Washington C-131A aircraft during the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) mission. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
Tropospheric Aerosol Radiative Forcing Observational eXperiment - University of Washington instrumented C-131A aircraft Data Set
TARFOX_UWC131A is the Tropospheric Aerosol Radiative Forcing Observational eXperiment (TARFOX) - University of Washington instrumented C-131A aircraft data set. The TARFOX Intensive Field Campaign was conducted July 10-31, 1996. It included coordinated measurements from four satellites (GOES-8, NOAA-14, ERS-2, LANDSAT), four aircraft (ER-2, C-130, C-131A, and a modified Cessna), land sites, and ships. A variety of aerosol conditions was sampled, ranging from relatively clean behind frontal passages to moderately polluted with aerosol optical depths exceeding 0.5 at mid-visible wavelengths. Gradients of aerosol optical thickness were sampled to aid in isolating aerosol effects from other radiative effects and to more tightly constrain closure tests, including those of satellite retrievals. Early results from TARFOX include demonstration of the unexpected importance of carbonaceous compounds and water condensed on aerosol in the US mid-Atlantic haze plume, chemical apportionment of the aerosol optical depth, measurements of the downward component of aerosol radiative forcing, and agreement between forcing measurements and calculations.
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