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Representing chemical history in ozone time-series predictions - a model experiment study building on the MLAir (v1.5) deep learning framework: Data 4/4
No full textThis record contains a part (4/4) of the data used for the manuscript "Representing chemical history in ozone time-series predictions - a model experiment study building on the MLAir (v1.5) deep learning framework" by F. Kleinert, L. H. Leufen, A. Lupascu, T. Butler and M. G. Schultz.
Please refer to https://doi.org/10.34730/19c94b0b77374395b11cb54991cc497d for an overview of all records related to this manuscript
irods
No full textThe Integrated Rule-Oriented Data System (iRODS) is open source data management software used by research, commercial, and governmental organizations worldwide
Global_Aerosol_OPP_profile_reanalysis_from_MERRA-2, vol.1.
No full textGlobal distribution of monthly aerosol optical properties (extinction, single scattering albedo and assymetry parameter) created from the NASA MERRA-2 3-hourly reanalysis profiles of aerosol mixing ratio and relative humidity (1), and MERRA-2 species level aerosol optical properties for different humidities and spectral bands (2). The data are given on the native MERRA2 vertical coordinate grid along with correponding MERRA2 surface pressure, pressure layer thickness and layer air density fields (3). Spectral bands refer to RRTMG spectral intervals and file names are flagged by wave band wb_X ( e.g. wb_10 refers to RRTMG band 10, visible), see Technicalinfo for further band definitions .
(1) Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: [Data Access Date], 10.5067/LTVB4GPCOTK2.
Relevant web page : https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
MERRA-2 General overview :
Gelaro, Ronald, McCarty, Will, Suarez, Max J., Todling, Ricardo, Molod, Andrea, Takacs, Lawrence, Randles, Cynthia A., Darmenov, Anton, Bosilovich, Michael G., Reichle, Rolf, Wargan, Krzysztof, Coy, Lawrence, Cullather, Richard, Draper, Clara, Akella, Santha, Buchard, Virginie, Conaty, Austin, da Silva, Arlindo M., Gu, Wei, Kim, Gi-Kong, Koster, Randal, Lucchesi, Robert, Merkova, Dagmar, Nielsen, Jon Eric, Partyka, Gary, Pawson, Steven, Putman, William, Rienecker, Michele, Schubert, Siegfried D., Sienkiewicz, Meta, Zhao, Bin. 2017. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim.. Vol. 30, No. 14, pp. 5419-5454. DOI: 10.1175/JCLI-D-16-0758.1 ISSN: 0894-8755
(2) Relevant publications for optical properties :
Chin, M. et al. Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements. J Atmos Sci59, 461–483 (2002).
Colarco, P., Silva, A. D., Chin, M. & Diehl, T. Online simulations of global aerosol distributions in the NASA GEOS‐4 model and comparisons to satellite and ground‐based aerosol optical depth. J Geophys Res Atmospheres 1984 2012 115, D10S07 (2010).
Colarco, P. R. et al. Impact of radiatively interactive dust aerosols in the NASA GEOS‐5 climate model: Sensitivity to dust particle shape and refractive index. J Geophys Res Atmospheres 119, 753 786 (2014).
(3) Scientific evaluation of MERRA-2 Aerosol reanalysis :
Randles, C. A., da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., Flynn, C. J.. 2017. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation. J. Clim.. Vol. 30, No. 17, pp. 6823-6850. DOI: 10.1175/JCLI-D-16-0609.1 ISSN: 0894-8755, 1520-0442
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., Yu, H.. 2017. The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies. J. Clim.. Vol. 30, No. 17, pp. 6851-6872. DOI: 10.1175/JCLI-D-16-0613.1 ISSN: 0894-8755, 1520-0442RRTMG Visible Spectral Bands , given here as wavelenght interval and mean value in nm.
File name wb index : Band # 1 2 3 4 5 6 7 8 9 10 11 12 13 14
λav : 3462 2789 2325 2046 1784 1463 1271 1010.1 701.6 533.2 393.1 304.0 231.6 8021
λmin : 3077 2500 2150 1942 1626 1299 1242 778.2 625.0 441.5 344.8 263.2 200.0 3846
λmax : 3846 3077 2500 2150 1942 1626 1299 1242.0 778.2 625.0 441.5 344.8 263.2 1219
DEIMS-SDR - Geodata Collection - April 2022
No full textThis record contains the boundaries and centroid/representative coordinates of all sites registered on DEIMS-SDR (www.deims.org). The provided shapefiles include information about the location and extent of sites as well as their names and IDs. This record will be updated periodically to reflect the latest version of the geodata on DEIMS-SDR. The purpose of this record is to provide researchers with a simple way to have access to the geodata of DEIMS-SDR. While we aim for highest accuracy, we cannot guarantee that the provided information is always completely accurate. For the very latest version of geodata, please download the respective coordinates or boundaries from DEIMS.org.
For further information (citation, data licence, disclaimer) please refer to the following pages:
www.deims.org
www.deims.org/about
www.deims.org/terms
www.deims.org/doc
Representing chemical history in ozone time-series predictions - a model experiment study building on the MLAir (v1.5) deep learning framework: Data 2/4
No full textThis record contains a part (2/4) of the data used for the manuscript "Representing chemical history in ozone time-series predictions - a model experiment study building on the MLAir (v1.5) deep learning framework" by F. Kleinert, L. H. Leufen, A. Lupascu, T. Butler and M. G. Schultz.
Please refer to https://doi.org/10.34730/19c94b0b77374395b11cb54991cc497d for an overview of all records related to this manuscript
DeepRain project presentations
No full textIn this entry a collection of internal publications, presentations, slides and reports from DeepRain. DeepRain is funded by the Bundesministerium für Bildung und Forschung (BMBF) under grant agreement 01 IS18047A-E
O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone: Data
No full textThis record contains data for the manuscript "O3ResNet: A deep learning based forecast system to predict local ground-level daily maximum 8-hour average ozone" by L. H. Leufen, F. Kleinert and M. G. Schultz.We provide the processed input data, the forecasts made by O3ResNet, and the O3ResNet model.To have a better insight into the data and model, we provide a ready-to-run jupyter notebook to load and visualize our data and results. To run this notebook we rely on docker. Download the docker file (leufen-docker.tar.gz), the basic data file (leufen-data-base.tar.gz) and the model file (leufen-model.tar.gz) and follow the instructions (instructions.md and instructions.pdf) to load data, model, and the notebook. Note that changes made by the user to the notebook well be removed on exit as long the option "--rm" is present. When following the instructions, it is *not* required to unpack the input data files (leufen-data-.tar.gz). If you encount issues with disk space limits and docker, it is always possible to use a reduced number of data files or to unpack data for a single country and just parse them to the docker container. When using a windows host system, some commands provided in the instructions might slightly deviate
TOAR-II Data User Workshop June/July 2022
No full textThe Tropospheric Ozone Assessment Report (TOAR) is an initiative of the International Global Atmospheric Chemistry (IGAC) project. TOAR-II is the second phase of TOAR. It builds on the successful completion of the first comprehensive assessment on tropospheric ozone and will last from 2020 to 2024. The TOAR-II Data User Workshop 2022 introduced the new TOAR database and its tools for accessing and analysing TOAR data. This publication contains all lectures that were held during the on-site event.
Furthermore, users' workflows and data analysis problems have been tackled in hands-on sessions. The feedback from this workshop will also influence the further development of the TOAR infrastructure. All workshop material including the hands-on codes can be found at https://gitlab.jsc.fz-juelich.de/esde/toar-public/toar-data-user-workshop-2022
HPSC Terrsys Fall School 2022 Training Material
No full textThese are sample output files of CORDEX simulations from the Community Land Model, version 3, and the COSMO meteorological model, kindly provided for training purposes by Dr. Klaus Görgen, IBG-3.
The primary intention of this repository is the demonstration of data publications and REST API use in a data science lecture of the HPSC Terrsys fall school in Bonn, Germany (https://www.hpsc-terrsys-fallschool.org).The files in this deposit are netCDF data files (https://www.unidata.ucar.edu/software/netcdf/).
See web pages for installing netCDF libraries.
For easy visual inspection of the content, I recommend Panoply (https://www.giss.nasa.gov/tools/panoply/); it requires a Java installation