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16 results for “mineral dust”
Cirrus formation regimes - Data driven identification and quantification of mineral dust effect
<p>This repository contains the data for the paper: </p> <p>Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike Lohmann<br>Titel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effect<br>Date: 2024</p> <p>Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)<br><br>Contents:<br><br>├── cirrus_cloud_trajectories.ftr<br>├── cluster_input_data.ftr<br>├── cluster_models<br>│ └── temperature_clustering_k4_12<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>│ └── temperature_clustering_k4_24<br>│ ├── cloud_ids.npy<br>│ ├── model_params.json<br>│ └── trained_model.hdf5</p> <p>├── cluster_predictions.ftr<br>└── readme.txt<br><br>For more info, please have a look at the <em>readme.txt</em><br><br>This is an updated version of the data, containing updated models and predictions based on the Journal revisions</p>
LMDZOR-INCA global model simulations diagnostics for mineral dust direct radiative effet calculations
<p>This dataset contains the diagnostic variable used to estimate the mineral dust aerosol direct radiative effect from LMDZOR-INCA global simulations using different refractive index data and different size modes and a multimodal size distribution.</p> <p>The NetCDF files provide the radiation fields shortwave all sky (solswad, topswad) and clear sky (solswad0, topswad0) (sol is for the surface and top for the top of the atmosphere) and the longwave all sky (sollwad, toplwad) and clear sky (sollwad0, toplwad0), as monthly means over global grids.</p> <p>Data are provided for the mean, minimum and maximum of the complex refractive index from Di Biagio et al. (2017) ( https://doi.org/10.5194/acp-17-1901-2017 ) and the refractive index by Volz et al. (1973) ( <a href="https://doi.org/10.1364/AO.12.000564">https://doi.org/10.1364/AO.12.000564</a> ) in the longwave spectral range and for the refractive index by Balkanski et al. (2007) ( https://doi.org/10.5194/acp-7-81-2007) corresponding to 1.5% hematite by volume in the shortwave range.</p> <p>Simulations are performed for four lognormal size distributions with mass median diameters (sigma) of 1 µm (1.8), 2.5 µm (2), 7 µm (1.9), 22 µm (2). The multimodal run is performed on the size distribution obtained as the sum of the four modes combined follwing the mass fractions of 0.6%, 4.3%, 31.5%, and 63.6% for the four modes, respectively.</p> <p>Variables for the dust atmospheric load and optical depth at 550 nm for each mode are in the mean run for each mode.</p> <p>Input mass extinction efficiency (Ext, m2/g), absorption exitinction efficiency (Abs, m2/g), single scattering albedo (w) and asymmetry factor (g) for the different radiative bands at at some wavelengths used in the MODIS sensor are provided in the 1MODE_xxum_dust_optical_data_1.5dielectric_mixture.</p>
3-D model data used to investigate the role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds
<p>These simulations were run by Chemical Transport Model TM4-ECPL covering the years 2009-01 to 2016-12 and are used for the bellow publication:</p> <p>Chatziparaschos, M., Daskalakis, N., Myriokefalitakis, S., Kalivitis, N., Nenes, A.,<br> Gonçalves Ageitos, M., Costa-Surós, M., Pérez García-Pando, C., Zanoli, M., Vrekoussis,<br> M., and Kanakidou, M.: Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds,<br> Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-551, in press 2023.</p> <p>Laboratory: Environmental Chemical Processes Laboratory (EPCL), Department of Chemistry, University of Crete, Heraklion.<br> contact: Kanakidou Maria <mariak@uoc.gr></p> <p>Model resolution: 2x3<br> Model Levels: 25</p> <p>Data info:</p> <p>DU_m2m(time, lev, lat, lon)<br> short_name :DU_m2m<br> long_name : Dust mode 2 mass accumulation</p> <p>DU_m3m(time, lev, lat, lon)<br> short_name : DU_m3m<br> long_name : Dust mode 3 mass coarse</p> <p>qua2_acc(time, lev, lat, lon)<br> short_name :qua2_acc<br> long_name :Quartz – accumulation mode</p> <p>qua2_coa(time, lev, lat, lon)<br> short_name :qua2_coa<br> long_name :Quartz – coarse mode</p> <p>FEL_acc(time, lev, lat, lon)<br> short_name :FEL_acc<br> long_name : K-Feldspar – accumulation mode</p> <p>FEL_coa(time, lev, lat, lon)<br> short_name :FEL_coa<br> long_name : K-Feldspar – coarse mode</p> <p>INP_QUA(time, lev, lat, lon)<br> short_name :INP_QUA<br> long_name :Ice Nucleating Particles derived form Quartz</p> <p>INP_FELD(time, lev, lat, lon)<br> short_name :INP_FELD<br> long_name :Ice Nucleating Particles derived form K-Feldpsar</p> <p> </p>
Modeling dust mineralogical composition: sensitivity to soil mineralogy atlases and their expected climate impacts. Soil and airborne mineral fraction datasets.
<p>These datasets correspond to soil and airbone mass mineral fractions as described and generated for "Modeling dust mineralogical composition: sensitivity to soil mineralogy" by Gonçalves Ageitos, M., Obiso, V., Miller, R.L., Jorba, O., Klose, M., Dawson, M., Balkanski, Y., Perlwitz, J., Basart, S., Di Tomaso, E., Escribano, J., Macchia, F., Montané, G., Mahowald, M.M., Green, R.O., Thompson, D.R. and Pérez García-Pando, C., ACP, 2023. </p> <p>There are 4 netCDF files that include the soil mass mineralogical fractions (0-1) in the clay (0-2 <span class="math-tex">\(\mu\)</span>m in diameter) and silt (2-63 <span class="math-tex">\(\mu\)</span>m in diameter) size classes as derived from the works of Claquin et al., (1999), and updated by Nickovic et al. (2012): <strong>C1999-SMA</strong>, and Journet et al. (2014): <strong>J2014-SMA</strong>. The data is mapped in a regular global grid with a horizontal resolution of 0.083º. Additional information on the FAO soil units, and soil texture data from HWSDv1.2 is provided in the J2014-SMA files. </p> <p>File details: </p> <ul> <li>C1999-SMA_CLAY_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>C1999-SMA_SILT_minfrac_0.083deg.nc - Claquin et al. (1999), Nickovic et al. (2012) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_CLAY_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> <li>J2014-C2-SMA_SILT_minfrac_0.083deg.nc - Journet et al. (2014) case 2 with the changes reported in Gonçalves Ageitos et al. (2023) soil mineralogy data for the clay fraction.</li> </ul> <p>There are 2 additional files that report the multiannual (2006-2010 period) monthly mean of the <strong>aerosol mass mineral fractions</strong> as obtained from the <strong>MONARCH model</strong> simulations described in Gonçalves Ageitos et al. (2023). The mass fractions are provided in each of the 8 size bins used in the model (ranging from 0.2 to 20 <span class="math-tex">\(\mu\)</span>m in diameter), and normalized so as to sum 1 (i.e., the sum of all minerals in all bins equals 1). Note that in order to reduce the size of these files, the variables have been compressed to short format and include an offset and scale factor as attributes. </p> <p>File details: </p> <ul> <li>20062010_monarch_minfrac_C1999.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH C1999 experiment. </li> <li>20062010_monarch_minfrac_J2014.nc - climatology (2006-2010 multiannual monthly mean) of size distributed mass mineral fractions as derived from the MONARCH J2014 experiment. </li> </ul> <p> </p> <p><em>Legend for the minerals:</em></p> <p>quar: quartz, feld: feldspars, calc: calcite, gyps: gypsum, illi: illite, mont: montmorillonite/smectite, kaol: kaolinite, verm:vermiculite, chlo: chlorite, mica: mica, hema: hematite, goet: goethite, irox:iron oxides (hematite and goethite). </p> <p>References:</p> <p>Claquin, T., Schulz, M., and Balkanski, Y. J.: Modeling the mineralogy of atmospheric dust sources, Journal of Geophysical Research<br> Atmospheres, https://doi.org/10.1029/1999JD900416, 1999.</p> <p>FAO-UNESCO: Soil Map of the World- Volume I Legend, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Paris, http://www.fao.org/3/as360e/as360e.pdf, 1974.</p> <p>FAO-UNESCO: Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization. Digital Soil Map of the World and Derived Soil Properties, Food and Agriculture Organization - United Nations Educational Scientific and Cultural Organization, Rome, 1995.</p> <p>FAO/IIASA/ISRIC/ISSCAS/JRC: Harmonized World Soil Database (version 1.2), Food and Agriculture Organization, FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012.</p> <p>Journet, E., Balkanski, Y., and Harrison, S. P.: A new data set of soil mineralogy for dust-cycle modeling, Atmospheric Chemistry and<br> Physics, 14, 3801–3816, https://doi.org/10.5194/acp-14-3801-2014, 2014.</p> <p>Nickovic, S., Vukovic, A., Vujadinovic, M., Djurdjevic, V., and Pejanovic, G.: Technical Note: High-resolution mineralogical database of dust-productive soils for atmospheric dust modeling, Atmospheric Chemistry and Physics, 12, 845–855, https://doi.org/10.5194/acp-12-845-2012, 2012.</p> <p> </p>
Drosophila suzukii population dynamics and control efficiency of mineral dusts with a focus on grape protection
<p>The invasive pest<em> Drosophila suzukii</em> is threatening berry production. It is mainly managed via chemical control, which is associated with consumer and environmental concerns. Here, we tested the efficiency of mineral dusts under field and laboratory conditions. Furthermore, population dynamics were studied in a vineyard and its surroundings. The kaolin products Cutisan and Surround®, as well as the CaCO<sub>3</sub> product Carboliq had neither insecticidal nor repellent effects on <em>Drosophila suzukii</em> adults in laboratory choice tests with grapes at concentrations of up to 2% (w/v). Cutisan and Surround® significantly reduced the number of deposited eggs (-41.9% and -49.3%, respectively) while Carboliq had no effect on the oviposition under laboratory conditions. The Surround® treatment significantly reduced the number of flies trapped on 09/09/2020 at a test vineyard. Depending on the assessment date and treatment, between 59 and 84% of the flies in the bait traps were females. The number of eggs found in fruit treated with Carboliq in the field was higher at each assessment date than in the control but this difference was not statistically significant. Cutisan and Surround® applications in the field showed an equivalent or lower average number of eggs compared to the control but this difference was only significant on 24/09/2020. The highest number of <em>D. suzukii</em> adults was observed around September in the field and a decline of the population occurred in the winter months until July. In epidemic years, temperature - humidity - combinations prior to population peaks were quite stable with low humidity being associated with a high temperature and <em>vice-versa</em>. In non-epidemic years, humidity fluctuated more than in epidemic years and temperatures were lower before population peaks. The effect of global radiation on population maxima seemed to be minor.</p>
The role of mineral dust aerosol particles in aviation soot-cirrus interactions
<p>The files contain the datasets shown in the publication "The role of mineral dust aerosol particles in aviation soot-cirrus interactions" to appear in J. Geophys. Res. Atmos. (revised manuscript submitted) The files are xmgrace plot files containing the research data (ASCII) shown in all figures that appear in the main text (4) and appendix (2).</p> <p> </p>
Data example and code used in the publication "Is transport of microplastics different from that of mineral dust? Results from idealized wind tunnel studies"
<p>Background</p> <p>The code labels microspheres and counts them. Further, the code determines which microspheres are independent of microsphere-microsphere collisions by their relative position to the other microspheres in an image. Images were taken with a full-frame visual camera (Sony Alpha 7RII) with a long-distance-microscopy lens (K2 DistaMax).</p> <p>Description of the dataset</p> <ul> <li>image_data_all.zip contains 228 tif-format images taken in a single experiment <ul> <li>the images show borosilicate microspheres with diameters from 63 to 75 µm</li> <li>during the experiment, the microspheres are detached from the substrate and are transported out of the image</li> </ul> </li> <li>functions_particle_labeling.jl contains all necessary functions for particle labeling</li> <li>analysis_protocol.jl is an example, that first determines a color threshold, and then labels all microspheres in all images stored in "image_data_all/substrate_a/image_data_single_experiment"</li> <li>post_processing_visualisation.R is an r-script, that reads the output of analysis_protocol.jl and demonstrates how logistic functions were fitted to the data</li> </ul> <p> </p> <p>We used julia 1.8.5 and R 4.3.0.</p> <p> </p>
Model simulation data used in "Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54" (Beer et al., Geosci. Model Dev., 2020)
<p>This dataset contains the output and the namelist setups of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. (<em>Geosci. Model Dev.</em>, 2020).</p>
Supporting datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor"
<p>This archive contains datasets used in the paper entitled "Aircraft-based observation of mineral dust particles over the western North Pacific in summer using a complex amplitude sensor."</p>
Optical properties of mineral dust aerosols with non-absorptive coating: a numerical investigation
<p>Model data for optical calculations of non-absorptive coated mineral dust aerosol</p> <p>This repository provides data used for plots in a manuscript submitted to Optics Express. The folder contains optical data from calculations performed with the T-matrix code. The subdirectory aspect ratio contains results for eleven aspect ratios with the coating ratio equal to 0.5 stored in respective directories. The directory coating ratio contains results for eleven coating ratios with an aspect ratio of 1.37. The directory naming of folders within the subdirectories derives from the parameter choice as indicated in the manuscript.</p>
The complex refraction index of mineral dust
<p>The complex refraction index of mineral dust used in the work "Opposite effects of mineral dust sizes and nonsphericity on snow albedo reduction".</p>
PollyXT and COSMO-MUSCAT data for "Investigating the link between mineral dust hematite content and intensive optical properties by means of lidar measurements and aerosol modelling"
<p>The dataset contains 4 different files: </p> <ul> <li>For the single case example on the 24 August 2021 between 2:45 to 5:27 UTC in Mndelo, Cabo Verde: <ul> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth-info.txt : contains the information of the vertically retrieved optical properties from PollyXT lidar measurements. The information contained refers to the chosen retrieval times, vertical smoothing, and reference heights</li> <li>-Mindelo-PollyXT_CPV-20210824_0245-0527-77smooth.txt : vertically retrieved optical properties per height.</li> <li>-Mindelo-model_24aug.csv : COSMO-MUSCAT vertical results of dust and mineral mass concentrations per height. The columns that end with "int mass" correspond to the integrated mass per dust layer and columns that end with numbers correspond to different size bins. For reference to the size bins see Table 1 in Gómez Maqueo Anaya et al., 2024</li> </ul> </li> <li>Mutiple case studies: <ul> <li>-Mindelo-lidar-uvvisdiff_model.csv : Twenty-two case studies with the following order: first, the mean values of the lidar-derived optical properties, along with their corresponding retrieval times and heights that define the dust plume. This is followed by the POLIPHON (Mamouri and Ansmann, 2014, 2017) data. The mean values from dust and mineral mass concentrations from the model start with the model heights where the dust plumes were calculated. At the end of the dataset rows, the times from which the modeled mean values are calculated can be found.</li> </ul> </li> </ul>
Data in "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"
<p>This is the data for publication "The uncertainties in the laboratory-measured short-wave refractive indices of mineral dust aerosols and the derived optical properties: A theoretical assessment"</p> <p>Version 1: data</p> <p>Version 2: rename the data files and add a readme file</p>
The export of African mineral dust across the Atlantic and its impact over the Amazon Basin
<p>the codes used to analyze the model simulations in the paper</p>
ATom: Dominant Role of Mineral Dust in Cirrus Cloud Formation
This dataset provides: (1) In situ dust aerosol concentration measurements over remote tropical Pacific and Atlantic Oceans by NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) airborne single-particle mass spectrometer combined with Aerosol Microphysical Properties (AMP) aerosol size spectrometers. Measurements were made aboard the NASA DC8 aircraft during the four ATom campaigns that occurred from 2016 to 2018 (2) Model output of dust and meteorology from the CESM global transport model extracted at the time and location of the aircraft; (3) Model output of dust, other aerosol, and meteorology from the GEOS global transport model extracted at the time and location of the aircraft; (4) CESM model global output of dust and meteorology for dust emitted by specific source regions; (5) NCEP Global Forecast System forward trajectories of air parcels initiated at the time and location of the aircraft; and (6) The location and properties of cirrus clouds formed along the forward trajectories simulated using a parcel model. These data have been applied to better understand the role of mineral dust in cirrus cloud formation.
Gene expression data of primary human bronchial epithelial cells exposed to crocidolite asbestos and cristobalite silica mineral dusts
GEO Series GSE62769. Homo sapiens. 12 samples. Type: Expression profiling by array.
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