Top down isoprene emissions

Produced by
BIRA-IASB
Data type
Gridded
Horizontal coverage
Europe (west=11.95° W, east=44.95° E, south=34.05° N, north=71.95° N)
Horizontal resolution
0.1° x 0.1°
Temporal resolution
Daily
Temporal coverage
2018-2022
Model
MAGRITTEv1.1
File format
Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/dices.zarr
How to download the data?

Variables

Click on a variable to see it on the viewer.

daily isoprene emission fluxes
isoprene_flux

Description

The S5p-based top-down biogenic emission inventory is derived based on an inversion with the MAGRITTEv1.1 chemistry transport model (Müller et al. 2019) and its adjoint, and are constrained by TROPOMI HCHO vertical columns.

Citation

BIRA-IASB. (2024). Top down isoprene emissions [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731785

Download instructions

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This sample Python code shows how to download this dataset, create subsets of it and save them as NetCDF, using the Xarray library.

import xarray as xr import fsspec # Open remote dataset ds = xr.open_zarr( store=fsspec.get_mapper("https://data.seedsproject.eu/seeds_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/slices.zarr") ) # Subset a time range, save as NetCDF time_subset = ds.isoprene_flux.sel(time=slice('2019-06-01', '2019-07-01')) time_subset.to_netcdf('./isoprene_flux_time_subset.nc') # Subset a time and geographical range, save as NetCDF time_geo_subset = ds.isoprene_flux.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5)) time_geo_subset.to_netcdf('./isoprene_flux_time_geo_subset.nc')