Data Extraction From Climate Data Store

Amon Melly
Amon Melly

December 02, 2022

Data Extraction From Climate Data Store

Extract data from Copernicus Climate Data Store into a csv file

This program provides an interactive web application for extracting daily data from climate data store using cdsapi. It enables user to enter the following filtering parameters.

  • Area extent
  • Variable of interest
  • Date range
  • Aggregation type


  • Specification of filtering parameters
  • Hourly data for the entire day are extracted (24 hrs)
  • 24 hour data downloaded is saved as netcdf format
  • Perform aggregation as specified by the user (sum, maximum, minimum & average)
  • Reading netcdf data into pandas dataframe
  • Latitude & Longitude coordinates are then added into the dataframe

Python Libraries Used

  • streamlit : For web interface creation
  • streamlit_folium : For rendering folium map in streamlit application
  • folium : Creating and rendering map for easy identification of your area of interest
  • datetime : For reading date range
  • shapely : For Creating polygon for the extent
  • geopandas : Assigning projection to the extent polygon created with shapely
  • cdsapi : Application interface for communication with Climate Data Store, requesting data
  • urllib : For reguesting and opening netcdf file link
  • xarray : Reading and manipulating (Aggregation of data) netcdf file (Reading Multi-dimensional data from Climate Data Store)
  • numpy : Holding multi-dimensional data from xarray as individual variables i.e latitude, longitude & variable of interest for easy extraction into a table form
  • pandas : Organizing converted data into table form (Using dataframe) before exporting it into a csv file

Tools used


Plug-ins used



Data ExtractionRemote SensingSpatial Data Science

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