Daily deposition fluxes (assimilated)

Produced by

CNRM

Data type

Gridded

Horizontal coverage

Europe (west=25.0° W, east=45.0° E, south=30.0° N, north=70.0° N)

Horizontal resolution

0.1° x 0.1°

Temporal resolution

Daily

Temporal coverage

2019-2020

Model

SURFEX land surface model

File format

Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_emep_cnrm_20190101-20201231_surfex_ekf_v1/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_emep_cnrm_20190101-20201231_surfex_ekf_v1/dices.zarr

How to download the data?

Variables

Click on a variable to see it on the viewer.

Nitric acid

HNO_3_ddep

Ammonia

NH_3_ddep

Nitrogen dioxide

NO_2_ddep

Nitrogen monoxide

NO_ddep

Ozone

O_x_ddep

Sulphur dioxide

SO_2_ddep

Description

Dry deposition of gases to the land surface is an important sink for reactive trace gases in the troposphere. Understanding dry deposition is of importance for understanding air quality and its impacts on human health and vegetation. SURFEX calculates dry deposition using a software scheme that was recently developed in the SEEDS project. The dry deposition software scheme calculates dry deposition velocities, which are used as an input to calculate dry deposition fluxes in the MOCAGE air quality model. The dry deposition fluxes are provided for ozone, sulphur dioxide, and reactive nitrogen compounds. The dry deposition flux data are output on a daily time resolution.

Citation

CNRM. (2024). Daily deposition fluxes (assimilated) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731858

Download instructions

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_emep_cnrm_20190101-20201231_surfex_ekf_v1/slices.zarr")
)

# Subset a time range, save as NetCDF
time_subset = ds.HNO_3_ddep.sel(time=slice('2019-06-01', '2019-07-01'))
time_subset.to_netcdf('./HNO_3_ddep_time_subset.nc')

# Subset a time and geographical range, save as NetCDF
time_geo_subset = ds.HNO_3_ddep.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5))
time_geo_subset.to_netcdf('./HNO_3_ddep_time_geo_subset.nc')