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Atmoshack2018

Join EUMETSAT, ECMWF, FMI and University of Helsinki scientists for a weekend hackathon working with atmospheric data at the Finnish Meteorological Service in Helsinki on 16-18 November 2018.

This Github repository contains the resources for Copernicus Hackathon 2018 in Helsinki, it can be used to share code. More info at

https://ultrahack.org/atmoshack2018

https://www.eumetsat.int/website/home/News/ConferencesandEvents/DAT_4070861.html?lang=EN

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Table of contents

  1. Challenge
  2. Dataset offering
  3. Platform offering
  4. Solution Domains
  5. Background
  6. Data Access
  7. Useful Links

Challenge: Hack the Atmosphere! <a name="challenge"></a>

Urban air pollution poses a significant threat to human health and the quality of life of millions of people worldwide. Nine out of ten people are estimated to breathe air containing high levels of pollutants, causing around 7 million deaths every year. More than 90% of air pollution-related deaths occur in the developing world. Many of these areas have no surface air-quality monitoring networks and rely mainly on modelling and satellite information. Besides air pollution, there are other aspects of atmospheric composition that can affect health: UV radiation, which strongly depends on the stratospheric ozone layer, is another example. Urban air pollution poses a significant threat to human health and the quality of life of millions of people worldwide. Nine out of ten people are estimated to breathe air containing high levels of pollutants, causing around 7 million deaths every year. More than 90% of air pollution-related deaths occur in the developing world. Many of these areas have no surface air-quality monitoring networks and rely mainly on modelling and satellite information.

Your challenge is to create solutions that help people in their daily lives to reduce their exposure to pollutants and UV radiation. To solve this challenge, you are encouraged to use Copernicus atmospheric monitoring data.

Various approaches are welcome: Create new solutions or add atmosphere-smart features to existing solutions. Propose innovative data visualisations for academic purposes or for raising awareness around the atmospheric environment. Mobile, web apps, platforms or hardware - it’s your call how to improve the quality of life for many!

Dataset offering <a name="datasets"></a>

An overview of the complete set of datasets available for this event is listed below

ProductSourceUsed forGeo CoverageTime CoverageResolutionFormatHTTP Access
PMAP Aerosol Optical Depth - particles in the atmosphereMetopAir pollution (basis for PM10, PM2.5 etc)GlobalApril 2016 - onwards. Aim 2017 onwards. (14 per day). January 2017 until December 20175x40km and 10x40kmNetCDFGOME-PMAP
NO2, Aerosols IndexMetop GOME-2 and IASIAir pollution monitoring, volcanic eruptions, forest firesGlobal2016 onwards40x80kmNetCDF/ HDF5Aerosols-Index
NO2, CO, O3Sentinel-5p TropomiAir pollution monitoringGlobalJuly 2018 onwards5x5km and 7x5kmNetCDFSentinel-5P
UV index (corrected for clouds with ECMWF data)Metop (AC SAF)UV - risk of harmGlobalJune 2007 until May 2017250 x 250PNG/HDF5UV-Index
Dust RGBMeteosat Second Generation (geo orbit)Dust storms - health and transport problemsAtlantic and Indian OceanAvailable from 28th September 20184kmGeoTiff/ PNGDust-RGB
Ash RGBMeteosat Second Generation (geo orbit)Volcanic ash - aviation, health etc.Atlantic and Indian OceanAvailable from 28th September 20184kmGeoTiff/ PNGAsh-RGB
SmartSMEAR Aerosols, gases, meteo, fluxes, soil,...SMEARResearchFinland1996 onwards depending on station and measured parameterPoint observations, no geospatial datatxt/csv, hdf5<a href="https://avaa.tdata.fi/web/smart/smear/api" target="_blank">SmartSMEAR</a>
Regional forecasts/ analyses of PM, O3, NO2, SO2CAMSEuropean air pollutionEurope2018Point data (as used in Euronews air quality forecasts), gridded data @ ~10kmCSV (details, NetCDF (gridded data)Air-Pollution
Global total ozone columnCAMSTotal ozone columnsGlobal2003-present. Reanalysis from 2003 until 2016~40kmNetCDF, GRIBCAMS Reanalysis data
Global ozone (Metop GOME-2 instrument)EUMETSAT/DLROzone O3, nitrogen dioxide NO2 and sulphur dioxide SO2GlobalDaily-WMSLatest, Daily
Global UV forecastsCAMSUV indexGlobal2003-present. NRT from 2016 June onwards, Reanalysis from 2003 to 2016~40kmNetCDFCAMS
Global fire emissionsCAMSWildland fire emission estimatesGlobal2003-present~10kmNetCDFCAMS
Air Quality stationsEuropean Environmental AgencyNO2, O3, PM10, PM2.5, CO - additional depending on countriesEurope2013-presentin-situCSVEEA Airbase website
Sentinel_5p Monthly mapsTemis ServiceNO2Global02/2018-present0,125 degreesasciiTemis KNMI website

Platform offering <a name="platform"></a>

WEkEO platform wekeo.eu provides access to Copernicus datasets and cloud based processing resources. Get to know the capabilities of WEkEO from here

You will be offered VM access by participating on the AtmosHack2018 here

Solution domains <a name="solution"></a>

Background <a name="bg"></a>

Copernicus AtmosHack is funded by the EU’s Copernicus Programme and has been organised through a partnership of EUMETSAT, the Copernicus Atmosphere Monitoring Service (CAMS), the Finnish Meteorological Service (FMI), and the University of Helsinki.

Data Access <a name="data"></a>

Object Storage End Point http://atmoshack.obs.eu-de.otc.t-systems.com/

list all product, for example, of CAMS Air Pollution

curl http://atmoshack.obs.eu-de.otc.t-systems.com/?prefix=06-CAMS-AirPollution

download a file

curl -O atmoshack.obs.eu-de.otc.t-systems.com/01-GOME_PMAP/M01-GOME/2017/01/M01-GOME-GOMPMA02-NA-2.0-201701003859.000000000Z-20170101022528-1293482-1.nc

Alternatively the data can be accessed with commonly used Object Storage tools, using the access keys below (AK/SK):

S3cmd https://s3tools.org/s3cmd
S3FS https://linux.die.net/man/1/s3fs

User NameAccess Key IdSecret Access Key
AtmosHack2018IXUCNIYQK5IXQ80TGTSASurkPQ2Z2xrBWxe9nye2Wfbyd3UVZ2ebVntT8ViN

CAMS Data Access

Access CAMS data via the ECMWF MARS archive - Web-API

Retrieve ECMWF key

• Self-register at http://apps.ecmwf.int/registration

• Login at https://apps.ecmwf.int/auth/login

• Retrieve your API key at https://api.ecmwf.int/v1/key/

• Specify the ECMWFDataServer with your url, key and email information

Install the ecmwfapi python library

pip install https://software.ecmwf.int/wiki/download/attachments/56664858/ecmwf-api-client-python.tgz

If you cannot run the pip commands, just download the ecmwf-api-client-python.tgz library. Extract its content and copy the module ecmwfapi to a directory pointed by the environment variable PYTHONPATH

Execute a MARS request and download data either as GRIB or netCDF

NOTE: per default, ECMWF data are on a gaussian grid with longitudes going from 0 to 360 degrees. It can be reprojected to a regular geographic latitude-longitude grid. If a reprojection is wished, the key 'grid' with the respective latitude and longitude resolution has to be specified. The same applies for specifying a longitude range of -180 to 180. The key area can be set.

#!/usr/bin/env python 

from ecmwfapi import ECMWFDataServer

server = ECMWFDataServer(url="https://api.ecmwf.int/v1", key="...", email="...@...")

server.retrieve({
       'stream': "oper",
       'levtype': "sfc",
       'param': "167",
       'dataset': "interim",
       'step': "0",
       'grid': "0.5/0.5",
       'area': "90/-180/-90/179.5",
       'time': "00/06/12/18",
       'date': "2014-07-01/to/2014-07-31",
       'type': "an",
       'class': "ei",
       'format': "netcdf",
       'target': "test.nc"
        })

Useful Links <a name="useful"></a>

How to Visualize NetCDF data - YouTube Video

CAMS: https://atmosphere.copernicus.eu/

Example notebook: https://gist.github.com/erget/467dba7082d31d73b20f3b5e90e740af

GDAL: http://www.gdal.org/

Jupyter: https://jupyter.org/

Scipy stack, including matplotlib and numpy: https://www.scipy.org/

Info on netCDF tool: https://www.unidata.ucar.edu/software/netcdf/

QGIS: https://www.qgis.org/en/site/

Xarray: http://xarray.pydata.org/en/stable/

Scitools: https://scitools.org.uk/