1) Data sources:
- Carbon dioxide (CO2)
- Methane (CH4)
- Nitrogen dioxide (NO2)
2) Station parameter database
- Database information - template
- Accessing the database
3) Station overpass statistics for CO2M
4) Map server for visualisation of satellite L3 files, emission inventories and station parameter database
- Creation of satellite level-3 files
- Selection of emission database
- Geoserver for visualisation
Section 1: Data sources
- Nitrogen dioxide – NO2 and Aerosols (last updated 26 June 2023)
Ground-based validation of CO2M NO2 data is planned to rely on three types of UV–visible DOAS instruments, which, thanks to complementary measurement techniques, provide correlative observations sensitive to the three components of the satellite data product:
- Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) can be used to measure the tropospheric column (and profile) during the day,
- Zenith-Scattered-Light DOAS (ZSL-DOAS) allows measurements of the stratospheric column at dawn and dusk, and
- Pandora direct Sun instruments provide the total column during the day.
Total columns are also measured in the infrared using Fourier Transform IR (FTIR) instruments, but these measurements have very low tropospheric sensitivity and are as such mainly suited for clean conditions and/or stratospheric column validation. Moreover, there are some unexplained discrepancies between the IR and Visible measurements, most likely related to the underlying cross sections.
Currently, these four types of instruments contribute to global monitoring networks. The lists provided here cover the NDACC (Network for the Detection of Atmospheric Composition Change) and the PGN (Pandonia Global Network), and the instruments are either operational now or planned for the near future. For most sites, data access is possible through public archives (NDACC DHF, PGN website, EVDC) but for some (e.g., several MAX-DOAS instruments), direct contact with the PI is necessary. For more details and a representative application of the three types of UV-Vis DOAS instruments in satellite NO2 validation, we refer to Verhoelst et al., AMT v14, 2021 (https://doi.org/10.5194/amt-14-481-2021).
An accurate estimate of the atmospheric aerosol load is important both because of its impact on the retrieval of primary products and because it can help identify the origin of the emissions. The CO2M aerosol products are planned to be validated using AERONET aerosol optical depth (AOD) data, and using LIDAR-based estimates of their vertical distribution, e.g., from the European EARLINET network.
For the CO2M station/instrument database, station information obtained through the NDACC, PGN, AERONET and EARLINET data portals was merged with equivalent information for the GHG FTIR sites, where instruments located within 2 km of each other were considered to be on the same site. In this case, of co-located instruments, the naming convention used in the TCCON/NDACC/COCCON networks was followed.
NO2 station overpass statistics for CO2M have been calculated using strict co-location criteria (pixel covers station) as NO2 gradients are very strong (kilometer scale) due to the short photochemical lifetime. Moderate cloud cover is not a major issue for NO2 validation.
Actual co-location of GHG, NO2 and/or AOD measuring instruments is rather limited, with just a couple of such sites representing urban/industrial conditions and a few representing clean background conditions. For the latter, none is located in mainland Europe. The only stations identified hosting instruments for GHG, NO2, and AOD are Toronto (Canada), Thessaloniki (Greece), Izaña (Tenerife) and Mauna Loa (Hawaï).
Section 2: Station parameter database (last updated 26 June 2023)
- Database information - template
The database information consists of four categories:
- Site information – information about the site latitude, longitude, altitude, solar zenith angle range covered by the instruments at the site, instrument types, network association, detectors, spectral resolution;
- Meteorological information – availability of meteorological information at the site, atmospheric pressure, air temperature, air relative humidity, wind direction, wind speed, solar spectral irradiance, precipitation, vertical profile of atmospheric pressure, vertical profile of atmospheric temperature;
- Gases information – availability of atmospheric trace gases measurements at the site, total column, vertical profile, and surface concentrations of CO2M relevant target gases such as carbon dioxide, methane, nitrogen dioxide (including tropospheric and stratospheric columns), water vapour, carbon monoxide, glyoxal (total column), solar induced fluorescence;
- Ancillary information – availability of relevant ancillary information that are useful for validation and understanding of biases in the satellite data. These parameters include spectral emissivity of surface, spectral albedo of surface, spectral brdf of surface, snow ice flags, cloud top height, cloud optical thickness, aerosol optical depth, aerosol vertical distribution, shape-size-type- number concentration of aerosol, aerosol vertical profile, vertical mixing depth of planetary boundary layer depth.
A template with the list of parameters to be collected from each reference station for the database has been created. Each parameter should be given a CF complaint name (a name following the CF Metadata Conventions) if available and a value; additional information includes information on processor version, unit, min/max value and a short description.
Download an example file: parameters_sodankyla01T_20230217_FTIR-GHG_FTIR-NO2_ZSL-DOAS-NO2_ZSL-DOAS-NO2.csv
- Accessing the database – implementation of a WSGI application
A PyDAP server reads the list of csv files surveyed for each reference station and creates an in-memory netCDF4 file, which can be reached using the oPeNDAP interface. A WSGI (Web Server Gateway Interface) application is running on a BIRA-IASB server, which provides access to the database via a web browser. Parameters from the database can be accessed directly or after applying filtering and selecting variables as needed by the users. The complete database can be accessed as a data descriptor structure (co2m_sites_db.dds) or a data attribute structure (co2m_sites_db.das). As an example, co2m_sites_db.json?&network>>"TCCON" would provide a list of stations which are part of TCCON. And a second example, co2m_sites_db.json?name,latitude,longitude,altitude&carbon_dioxide_total_column=1&latitude>-25&latitude<70.0&longitude>0.0&longitude<90.0 provides a list of stations which provide carbon dioxide total column concentrations and are in the latitude range between – 25 degrees and + 70 degrees and longitude range between 0 degrees and 90 degrees. The WSGI app is currently only accessible via BIRA-IASB internal servers. Soon, it also will be available on an external server for public access once the IT configurations are set.
Section 3: Station overpass statistics for CO2M
Monthly CO2M overpass statistics were derived by iLab for station footprints computed with the atmospheric transport model TM3 (Heimann and Koerner, 2003). Overpasses are grouped into bins defined by ranges of sun zenith angle and aerosol optical thickness. Samples with cloud cover above 1% were neglected. Cloud cover and aerosol optical thickness at 550nm are taken from the CAMS global reanalysis (EAC4, Inness et al., 2019) at 0.75 degree resolution.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Heimann, M., and Koerner, S.: The global atmospheric tracer model (TM3), Technical Report, Max-Planck Institute für Biogeochemie, Jena, Germany, number 5, 2003.
Section 4: Map server for visualisation of satellite L3 files, emission inventories and station parameter database (last updated 26 June 2023)
- Creation of satellite level-3 files
The satellite level 2 (L2) files were downloaded from data repository of the respective satellite data providers or collected by direct contact with the PIs. These files were then binned on a regular x degree and y degree grid to form the level-3 (L3) files. The harp component of the ESA atmospheric toolbox (https://atmospherictoolbox.org/) was used to perform the latitude longitude regridding, where each satellite pixel contributes to the regridded gas value of the target grid cell if there was an overlap of the pixel and the grid cell. In case multiple pixels overlap, a grid cell weighted average was taken using the overlap area as the weight.
Resolution of the regridded files
CO2_OCO ---> 0.5° (0.1° in the comparator)
CH4_S5P ----> 0.05°
NO2_S5P ----> 0.02°
CO_S5P ----> 0.05°
CO2_TanSat_CCI+ ----> 0.5° (0.1° in the comparator)
Albedo_B3_OCO2 ----> 0.5°
CH4_WFMD ---> 0.1°
EDGAR -----> 0.1°
TNO ------> 0.02°
- Selection of emission database
The EDGARv7.0 database for emissions of CO2, CH4 and EDGARv6.0 for NO2 have been downloaded (from https://edgar.jrc.ec.europa.eu/emissions_data_and_maps last access 10 October 2022) and stored for visualisation using the map server. The EDGAR data are provided at 0.1 x 0.1 degree resolution at global level representing the emission sources. The maps are yearly average of emissions for 2021 for CO2 and CH4 and 2018 for NO2.
EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database (a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA), and comprising IEA-EDGAR CO2, EDGAR CH4, EDGAR N2O, EDGAR F-GASES version 7.0, (2022) European Commission, JRC (Datasets). The complete citation of the EDGAR Community GHG Database is available in the 'Sources and References' section.
IEA-EDGAR CO2, a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022) including or based on data from IEA (2021) Greenhouse Gas Emissions from Energy, www.iea.org/data-and-statistics, as modified by the Joint Research Centre.
Users of the data are obliged to acknowledge the source of the data also with reference to the EDGARv7.0 website (link) and/or relevant reports.
Crippa, M., Guizzardi, D., Banja, M., Solazzo, E., Muntean, M., Schaaf, E., Pagani, F., Monforti-Ferrario, F., Olivier, J., Quadrelli, R., Risquez Martin, A., Taghavi-Moharamli, P., Grassi, G., Rossi, S., Jacome Felix Oom, D., Branco, A., San-Miguel-Ayanz, J. and Vignati, E., CO2 emissions of all world countries – JRC/IEA/PBL 2022 Report, EUR 31182 EN, Publications Office of the European Union, Luxembourg, 2022, doi:10.2760/730164, JRC130363.
Crippa, M., Guizzardi, D., Solazzo, E., Muntean, M., Schaaf, E., Monforti-Ferrario, F., Banja, M., Olivier, J.G.J., Grassi, G., Rossi, S., Vignati, E.,GHG emissions of all world countries - 2021 Report, EUR 30831 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-41547-3, doi:10.2760/173513, JRC126363.
IEA (2019) World Energy Balances, www.iea.org/data-and-statistics, All rights reserved, as modified by Joint Research Centre, European Commission.
Jalkanen, J. P., Johansson, L., Kukkonen, J., Brink, A., Kalli, J., & Stipa, T. (2012). Extension of an assessment model of ship traffic exhaust emissions for particulate matter and carbon monoxide. Atmospheric Chemistry and Physics, 12(5), 2641–2659. doi:10.5194/acp-12-2641-2012
Johansson, L., Jalkanen, J.-P., & Kukkonen, J. (2017). Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution. Atmospheric Environment, 167, 403–415. doi:10.1016/j.atmosenv.2017.08.042
The high resolution emissions data over Europe are provided by TNO, the Netherlands. The yearly averaged emissions of CO2, CH4, CO, NOx, and NMVOC are provided for the year 2018. While, the uncertainties for CO2 and CO are provided for the year 2015. The resolution is 1/60 degrees longitude x 1/120 degrees latitude, which corresponds to about 1 km x 1 km over central Europe. The domain is 47 degree North to 56 degree North, 2 degree West to 19 degree East.
ODIAC (Open-source Data inventory for Anthropogenic CO2) is a global high resolution (1 km x 1 km) emission data product for fossil fuel carbon dioxide emissions. It makes use of the combined nighttime light data and individual power plant emission/location profiles to estimate the global spatial extent of fossil fuel CO2 emissions.
Data source: Tomohiro Oda, Shamil Maksyutov (2015), ODIAC Fossil Fuel CO2 Emissions Dataset (Version name: ODIAC2022), Center for Global Environmental Research, National Institute for Environmental Studies, DOI:10.17595/20170411.001. (Reference date: 2023/08/04)
- Geoserver for visualisation
A GeoServer has been installed at BIRA-IASB. The server allows to share, process and edit geospatial data. The server also allows requests of web mapping service/web coverage service/web feature service.
In order to access the map server (via https://co2m.aeronomie.be) each user must create an account. If needed, the login credentials can be stored in the memory of the browser for quick future login.
The map server has the possibility to plot satellite L3 files for visualisation, emission data visualisation, list of ground-based stations from the database, cities, and over-plot information from station database. Change the background map to OpenStreet maps or greyEarth and the possibility to change the colour style and opacity are also made available
The map server has two primary plot possibilities, (i) plot individual maps on a global scale and zoom possibilities for regional to local levels, (ii) plot side-by-side two individual maps as a comparator and have the possibility to hover over one or the other map using the divider (cursor). The comparator map also has the zoom possibility to look at regional or local level features. The map server allows the selection of a specific dataset of monthly or yearly averaged dataset by following the options. The comparator page allows the comparison of products from the same time period or the differences in the products between monthly and yearly averages. Both the individual maps as well as the comparator maps have the possibility to zoom on the color scale and change the range of the plots by going towards the lower end or higher end of the scale.