4.2.2.3. ESA CCI Toolbox Reproject Dataset

The purpose of this notebook is to demonstrate how to access a dataset, resample it to different coordinates, and reproject it to a new coordinate reference system (CRS).

To run this Notebook, make sure the ESA CCI Toolbox is setup correctly.

The ESA CCI Toolbox is built on xcube, leveraging many of its functionalities in this notebook. For additional examples on resampling and reprojection, visit the xcube GitHub repository.

from xcube.core.gridmapping import GridMapping
from xcube.core.resampling import resample_in_space
from xcube.core.store import new_data_store

---

4.2.2.3.1. Reproject Dataset from ESA Open Data Portal

To access datasets from the ESA Open Data Portal store, we begin by initializing a new data store using the store ID ‘esa-cci’.

cci_store = new_data_store('esa-cci')

All provided data are datasets. So, let’s have a look what data sets are available.

datasets = cci_store.list_data_ids()
datasets

['esacci.GHG.satellite-orbit-frequency.L2.CH4.TROPOMI.Sentinel-5P.WFMD.v1-8.201711-202406',
 'esacci.AEROSOL.5-days.L3C.AEX.GOMOS.Envisat.AERGOM.3-00.r1',
 'esacci.AEROSOL.climatology.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',
 'esacci.AEROSOL.day.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.AATSR.Envisat.ADV.2-31.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.AATSR.Envisat.ORAC.04-01-.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.AATSR.Envisat.ORAC.04-01_seg-.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.AATSR.Envisat.SU.4-3.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ADV.2-31.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ORAC.04-01-.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ORAC.04-01_seg-.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.ATSR-2.ERS-2.SU.4-3.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.multi-sensor.multi-platform.AATSR-ENVISAT-ENS_DAILY.v2-6.r1',
 'esacci.AEROSOL.day.L3C.AER_PRODUCTS.multi-sensor.multi-platform.ATSR2-ENVISAT-ENS_DAILY.v2-6.r1',
 'esacci.AEROSOL.day.L3C.AOD.MERIS.Envisat.MERIS_ENVISAT.2-2.r1',
 'esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.AATSR.Envisat.ADV.2-31.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.AATSR.Envisat.ORAC.04-01-.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.AATSR.Envisat.ORAC.04-01_seg-.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.AATSR.Envisat.SU.4-3.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ADV.2-31.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ORAC.04-01-.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.ATSR-2.ERS-2.ORAC.04-01_seg-.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.ATSR-2.ERS-2.SU.4-3.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.multi-sensor.multi-platform.AATSR-ENVISAT-ENS_MONTHLY.v2-6.r1',
 'esacci.AEROSOL.mon.L3C.AER_PRODUCTS.multi-sensor.multi-platform.ATSR2-ENVISAT-ENS_MONTHLY.v2-6.r1',
 'esacci.AEROSOL.mon.L3C.AOD.MERIS.Envisat.MERIS_ENVISAT.2-2.r1',
 'esacci.AEROSOL.yr.L3C.AER_PRODUCTS.AATSR.Envisat.AATSR-ENVISAT-ENS_ANNUAL.v2-6.r1',
 'esacci.AEROSOL.yr.L3C.AER_PRODUCTS.ATSR-2.Envisat.ATSR2-ENVISAT-ENS_ANNUAL.v2-6.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.4-0.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.5-0.2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2010-2007',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.CHANGE.6-0.100m-2022-2021',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2010-2007',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.10000m-2022-2021',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.1000m-2022-2021',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2010-2007',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.25000m-2022-2021',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2010-2007',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2016-2015',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2017-2016',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2018-2017',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2019-2018',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2020-2010',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2020-2019',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2021-2020',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.50000m-2022-2021',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.DIFF.6-0.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.2-0.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.3-0.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.4-0.r1',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.10000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.1000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.100m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.25000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.5-0.50000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.10000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.1000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.100m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.25000m',
 'esacci.BIOMASS.yr.L4.AGB.multi-sensor.multi-platform.MERGED.6-0.50000m',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.MODIS.Aqua.MODIS_AQUA.2-0.r1',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.MODIS.Terra.MODIS_TERRA.2-0.r1',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.multi-sensor.multi-platform.ATSR2-AATSR.3-0.r1',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.multi-sensor.multi-platform.AVHRR-AM.3-0.r1',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.multi-sensor.multi-platform.AVHRR-PM.3-0.r1',
 'esacci.CLOUD.mon.L3C.CLD_PRODUCTS.multi-sensor.multi-platform.MERIS-AATSR.2-0.r1',
 'esacci.FIRE.mon.L4.BA.MODIS.Terra.MODIS_TERRA.v5-1.grid',
 'esacci.FIRE.mon.L4.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.2-0.grid',
 'esacci.FIRE.mon.L4.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.v1-1.grid',
 'esacci.FIRE.mon.L4.BA.multi-sensor.multi-platform.SYN.v1-1.grid',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-3.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRPR.v2-0-3.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-3.r1',
 'esacci.ICESHEETS.mon.IND.GMB.GRACE-instrument.GRACE.VARIOUS.1-3.greenland_gmb_time_series',
 'esacci.ICESHEETS.unspecified.L4.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150610_20170321_Helheim',
 'esacci.ICESHEETS.unspecified.L4.SEC.multi-sensor.multi-platform.UNSPECIFIED.0-1.greenland_sec_saral_altika',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.AMI-SAR.ERS-1.UNSPECIFIED.1-1.greenland_northern_drainage_basin_winter_1991_1992',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.AMI-SAR.ERS-2.UNSPECIFIED.1-1.greenland_margin_1995_1996',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).Sentinel-2A.UNSPECIFIED.1-0.greenland_s2_50m_20160508_20160518_docker_smith',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170501_20170829_Helheim',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170501_20170914_Petermann',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170603_20170908_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170625_20170810_79Fjord',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170625_20170810_Zachariae',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170630_20170814_Hagen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170715_20170814_Upernavik',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_seasonal_20170721_20170820_Kangerdlugssuaq',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170501_20170829_Helheim',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170501_20170914_Petermann',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170603_20170908_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170625_20170810_79Fjord',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170625_20170810_Zachariae',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170630_20170814_Hagen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170715_20170814_Upernavik',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.MSI-(Sentinel-2).multi-platform.UNSPECIFIED.1-1.greenland_s2_50m_timeseries_20170721_20170820_Kangerdlugssuaq',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-(RadarSat-2).RadarSat-2.UNSPECIFIED.1-0.greenland_map_winter_2013_2014',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-2000.multi-platform.UNSPECIFIED.1-0.greenland_csk_250m_timeseries_20120604_20141223_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).Sentinel-1A.UNSPECIFIED.1-0.greenland_map_winter_2014_2015',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).Sentinel-1A.UNSPECIFIED.1-2.greenland_map_winter_2015_2016',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-0.greenland_map_winter_2016_2017',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-0.greenland_map_winter_2017_2018',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20141010_20170317_Upernavik',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20141011_20170317_20150122_20170322_Hagen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20141011_20170317_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150118_20170321_Kangerlussuaq',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150122_20170319_Petermann',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150122_20170322_79-Fjord',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150124_20170322_Storstroemmen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.SAR-C-(Sentinel-1).multi-platform.UNSPECIFIED.1-1.greenland_s1_250m_20150126_20170322_Zachariae',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-0.greenland_timeseries_Kangerlussuaq',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_2002_2010_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_Hagen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_Helheim',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_Petermann',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_Storstrommen',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-1.greenland_timeseries_Zachariae_79Fjord',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-2.greenland_timeseries_1992_2010_Jakobshavn',
 'esacci.ICESHEETS.unspecified.Unspecified.IV.multi-sensor.multi-platform.UNSPECIFIED.1-2.greenland_timeseries_Upernarvik',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-2.greenland_gmb_timeseries',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-4.greenland_gmb_time_series',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-5.greenland_gmb_time_series',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.3-0.greenland_gmb_mass_trends',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.3-0.greenland_gmb_time_series',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.v2-2.r1',
 'esacci.ICESHEETS.yr.Unspecified.IV.PALSAR.ALOS.UNSPECIFIED.1-1.greenland_margin_2006_2011',
 'esacci.ICESHEETS.yr.Unspecified.SEC.SIRAL.CryoSat-2.UNSPECIFIED.2-2.greenland_sec_cryosat_2yr',
 'esacci.ICESHEETS.yr.Unspecified.SEC.SIRAL.CryoSat-2.UNSPECIFIED.2-2.greenland_sec_cryosat_5yr',
 'esacci.ICESHEETS.yr.Unspecified.SEC.multi-sensor.multi-platform.UNSPECIFIED.1-2.r1',
 'esacci.LAKES.day.L3S.LK_PRODUCTS.multi-sensor.multi-platform.MERGED.v1-0.r1',
 'esacci.LAKES.day.L3S.LK_PRODUCTS.multi-sensor.multi-platform.MERGED.v1-1.r1',
 'esacci.LAKES.day.L3S.LK_PRODUCTS.multi-sensor.multi-platform.MERGED.v2-0-2.r1',
 'esacci.LAKES.day.L3S.LK_PRODUCTS.multi-sensor.multi-platform.MERGED.v2-1-0.r1',
 'esacci.LC.13-yrs.L4.WB.ASAR.Envisat.Map.4-0.r1',
 'esacci.LC.yr.L4.LCCS.multi-sensor.multi-platform.Map.2-0-7.r1',
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 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.EMMA.v2-2c.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.OCFP.v7-0-1.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.TANSO-FTS.GOSAT.SRFP.v2-3-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.multi-sensor.multi-platform.EMMA.v2-2a.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CO2.multi-sensor.multi-platform.EMMA.v2-2b.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.SCIAMACHY.Envisat.IMAP.v7-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.SCIAMACHY.Envisat.WFMD.v4-0.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRFP.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS-2.GOSAT-2.SRPR.v2-0-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.EMMA.ch4_v1-2.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.OCFP.v2-1.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.OCPR.v7-0.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.SRFP.v2-3-8.r1',
 'esacci.GHG.satellite-orbit-frequency.L2.CH4.TANSO-FTS.GOSAT.SRPR.v2-3-8.r1',
 'esacci.ICESHEETS.unspecified.Unspecified.CFL.multi-sensor.multi-platform.UNSPECIFIED.v3-0.greenland',
 'esacci.ICESHEETS.unspecified.Unspecified.GLL.multi-sensor.multi-platform.UNSPECIFIED.v1-3.greenland',
 'esacci.RD.satellite-orbit-frequency.L3S.WL.multi-sensor.multi-platform.MERGED.v1-1.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.RA-2.Envisat.NH.2-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.RA-2.Envisat.NH.3-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.RA-2.Envisat.SH.2-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.RA-2.Envisat.SH.3-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.SIRAL.CryoSat-2.NH.2-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.SIRAL.CryoSat-2.NH.3-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.SIRAL.CryoSat-2.SH.2-0.r1',
 'esacci.SEAICE.satellite-orbit-frequency.L2P.SITHICK.SIRAL.CryoSat-2.SH.3-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.AltiKa.SARAL.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.GFO-RA.GFO.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.Poseidon-2.Jason-1.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.Poseidon-3.Jason-2.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.RA-2.Envisat.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.RA.ERS-1.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.RA.ERS-2.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.SIRAL.CryoSat-2.UNSPECIFIED.v2-0.r1',
 'esacci.SEALEVEL.satellite-orbit-frequency.L1.UNSPECIFIED.SSALT.Topex-Poseidon.UNSPECIFIED.v2-0.r1',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-2.greenland_gmb_mass_trends',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-3.greenland_gmb_mass_trends',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-4.greenland_gmb_mass_trends',
 'esacci.ICESHEETS.yr.Unspecified.GMB.GRACE-instrument.GRACE.UNSPECIFIED.1-5.greenland_gmb_mass_trends',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.ASA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.BENGUELA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.CARIBBEAN',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.GULFSTREAM',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.HUMBOLDT',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.MED_SEA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.NE_ATL',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.N_INDIAN',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.SE_AFRICA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.SE_ASIA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.S_AUSTRALIA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.WAFRICA',
 'esacci.SEALEVEL.mon.IND.MSLTR.multi-sensor.multi-platform.MERGED.2-2.r1',
 'esacci.FIRE.mon.L3S.BA.MODIS.Terra.MODIS_TERRA.v5-1.pixel',
 'esacci.FIRE.mon.L3S.BA.multi-sensor.multi-platform.SYN.v1-1.pixel',
 'esacci.FIRE.mon.L3S.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.2-0.pixel',
 'esacci.FIRE.mon.L3S.BA.MSI-(Sentinel-2).Sentinel-2A.MSI.v1-1.pixel',
 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A03.v1-2.Siberia',
 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A02.v1-2.Amazonia',
 'esacci.LC.yr.L4.Map.multi-sensor.multi-platform.HRLC10-A01.v1-2.Africa',
 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A03.v1-2.Siberia',
 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A02.v1-2.Amazonia',
 'esacci.LC.5-yrs.L4.Map.multi-sensor.multi-platform.HRLC30-A01.v1-2.Africa',
 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A03.v1-2.Siberia',
 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A02.v1-2.Amazonia',
 'esacci.LC.5-yrs.L4.CHANGE.multi-sensor.multi-platform.HRLCC30-A01.v1-2.Africa',
 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETATION.SPOT-5.MERGED.v1-0.r1',
 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.VEGETATION.multi-platform.MERGED.v1-0.r1',
 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.Végétation-P.PROBA-V.MERGED.v1-0.r1',
 'esacci.VEGETATION.5-days.L3S.VP_PRODUCTS.multi-sensor.multi-platform.MERGED.v1-0.r1']

We would like to reproject Aerosol CCI product. But first we view the available opening parameters for the respective dataset.

cci_store.get_open_data_params_schema(
    data_id="esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1"
)

<xcube.util.jsonschema.JsonObjectSchema at 0x70d8cab709f0>

Now we can open the dataset and select a subset which covers Europe and the last half year of the data.

ds = cci_store.open_data(
    "esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1",
    time_range=["2015-07-01", "2015-12-31"],
    bbox=[0, 45, 20, 60]
)
ds

<xarray.Dataset> Size: 51kB
Dimensions:                           (time: 6, lat: 15, lon: 20, bnds: 2)
Coordinates:
  * lat                               (lat) float32 60B 45.5 46.5 ... 58.5 59.5
  * lon                               (lon) float32 80B 0.5 1.5 ... 18.5 19.5
  * time                              (time) datetime64[ns] 48B 2015-07-16T12...
    time_bnds                         (time, bnds) datetime64[ns] 96B dask.array<chunksize=(6, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
    absorbing_aerosol_index           (time, lat, lon) float32 7kB dask.array<chunksize=(1, 15, 20), meta=np.ndarray>
    number_of_observations            (time, lat, lon) float64 14kB dask.array<chunksize=(1, 15, 20), meta=np.ndarray>
    number_of_positive_observations   (time, lat, lon) float64 14kB dask.array<chunksize=(1, 15, 20), meta=np.ndarray>
    positive_absorbing_aerosol_index  (time, lat, lon) float32 7kB dask.array<chunksize=(1, 15, 20), meta=np.ndarray>
    solar_zenith_angle                (time, lat, lon) float32 7kB dask.array<chunksize=(1, 15, 20), meta=np.ndarray>
Attributes:
    Conventions:             CF-1.7
    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...
    date_created:            2025-12-08T12:14:17.027657
    processing_level:        L3
    time_coverage_start:     2015-07-01T00:00:00
    time_coverage_end:       2016-01-01T00:00:00
    time_coverage_duration:  P184DT0H0M0S
    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...

xarray.Dataset

• Dimensions:
  • time: 6
  • lat: 15
  • lon: 20
  • bnds: 2
• Coordinates: (4)
  • lat
    (lat)
    float32
    45.5 46.5 47.5 ... 57.5 58.5 59.5

    long_name :

    Latitudes of the centre of the grid cells

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    15

    shape :

    [15]

    chunk_sizes :

    [15]

    file_chunk_sizes :

    [15]

    data_type :

    float32

    dimensions :

    ['latitude']

    file_dimensions :

    ['latitude']

    array([45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5,
           57.5, 58.5, 59.5], dtype=float32)

  • lon
    (lon)
    float32
    0.5 1.5 2.5 3.5 ... 17.5 18.5 19.5

    long_name :

    Longitudes of the centre of the grid cells

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    20

    shape :

    [20]

    chunk_sizes :

    [20]

    file_chunk_sizes :

    [20]

    data_type :

    float32

    dimensions :

    ['longitude']

    file_dimensions :

    ['longitude']

    array([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,
           12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5], dtype=float32)

  • time
    (time)
    datetime64[ns]
    2015-07-16T12:00:00 ... 2015-12-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2015-07-16T12:00:00.000000000', '2015-08-16T12:00:00.000000000',
           '2015-09-16T00:00:00.000000000', '2015-10-16T12:00:00.000000000',
           '2015-11-16T00:00:00.000000000', '2015-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • time_bnds
    (time, bnds)
    datetime64[ns]
    dask.array<chunksize=(6, 2), meta=np.ndarray>

    standard_name :

    time_bnds

    Array Chunk Bytes 96 B 96 B Shape (6, 2) (6, 2) Dask graph 1 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

• Data variables: (5)
  • absorbing_aerosol_index
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 15, 20), meta=np.ndarray>

    long_name :

    Absorbing aerosol index averaged for each grid cell

    units :

    1

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 7.03 kiB 1.17 kiB Shape (6, 15, 20) (1, 15, 20) Dask graph 6 chunks in 2 graph layers Data type float32 numpy.ndarray

  • number_of_observations
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 15, 20), meta=np.ndarray>

    long_name :

    Number of observations for each grid cell

    units :

    1

    orig_data_type :

    int32

    fill_value :

    9223372036854775807

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    int64

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 14.06 kiB 2.34 kiB Shape (6, 15, 20) (1, 15, 20) Dask graph 6 chunks in 2 graph layers Data type float64 numpy.ndarray

  • number_of_positive_observations
    (time, lat, lon)
    float64
    dask.array<chunksize=(1, 15, 20), meta=np.ndarray>

    long_name :

    Number of positive observations for each grid cell

    units :

    1

    orig_data_type :

    int32

    fill_value :

    9223372036854775807

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    int64

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 14.06 kiB 2.34 kiB Shape (6, 15, 20) (1, 15, 20) Dask graph 6 chunks in 2 graph layers Data type float64 numpy.ndarray

  • positive_absorbing_aerosol_index
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 15, 20), meta=np.ndarray>

    long_name :

    Positive absorbing aerosol index averaged for each grid cell

    units :

    1

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 7.03 kiB 1.17 kiB Shape (6, 15, 20) (1, 15, 20) Dask graph 6 chunks in 2 graph layers Data type float32 numpy.ndarray

  • solar_zenith_angle
    (time, lat, lon)
    float32
    dask.array<chunksize=(1, 15, 20), meta=np.ndarray>

    long_name :

    Solar zenith angle averaged for each grid cell

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    Array Chunk Bytes 7.03 kiB 1.17 kiB Shape (6, 15, 20) (1, 15, 20) Dask graph 6 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (3)
  • lat
    PandasIndex

    PandasIndex(Index([45.5, 46.5, 47.5, 48.5, 49.5, 50.5, 51.5, 52.5, 53.5, 54.5, 55.5, 56.5,
           57.5, 58.5, 59.5],
          dtype='float32', name='lat'))

  • lon
    PandasIndex

    PandasIndex(Index([ 0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5,  8.5,  9.5, 10.5, 11.5,
           12.5, 13.5, 14.5, 15.5, 16.5, 17.5, 18.5, 19.5],
          dtype='float32', name='lon'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-07-16 12:00:00', '2015-08-16 12:00:00',
                   '2015-09-16 00:00:00', '2015-10-16 12:00:00',
                   '2015-11-16 00:00:00', '2015-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

• Attributes: (8)

  Conventions :

  CF-1.7

  title :

  esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1

  date_created :

  2025-12-08T12:14:17.027657

  processing_level :

  L3

  time_coverage_start :

  2015-07-01T00:00:00

  time_coverage_end :

  2016-01-01T00:00:00

  time_coverage_duration :

  P184DT0H0M0S

  history :

  [{'program': 'xcube_cci.chunkstore.CciChunkStore', 'cube_params': {'time_range': ['2015-07-01T00:00:00', '2015-12-31T00:00:00'], 'bbox': [0, 45, 20, 60], 'variable_names': ['absorbing_aerosol_index', 'number_of_observations', 'solar_zenith_angle', 'positive_absorbing_aerosol_index', 'number_of_positive_observations']}}]

We can plot the absorbing aerosol index in the original projection before reprojecting it.

ds.absorbing_aerosol_index.isel(time=-1).plot()

<matplotlib.collections.QuadMesh at 0x70d8c8c163c0>

Now, we can reproject the dataset from the original Geographic Coordinate System (EPSG:4326 - WGS 84) to the European Terrestrial Reference System 1989 ensemble (EPSG:3035). To achieve this, we first create a grid mapping that defines the target projection, which is then used to reproject the dataset.

source_gm = GridMapping.from_dataset(ds)
temp_target_gm = source_gm.transform("EPSG:3035", xy_res=10000)
target_gm = temp_target_gm.to_regular()
target_gm

class: RegularGridMapping

• is_regular: True

• is_j_axis_up: False

• is_lon_360: False

• crs: EPSG:3035

• xy_res: (10000, 10000)

• xy_bbox: (3528853, 2427033, 5118853, 4157033)

• ij_bbox: (0, 0, 159, 173)

• xy_dim_names: (‘x’, ‘y’)

• xy_var_names: (‘x’, ‘y’)

• size: (159, 173)

• tile_size: (20, 15)

ds_reproject = resample_in_space(
    ds,
    source_gm=source_gm,
    target_gm=target_gm,
)
ds_reproject

<xarray.Dataset> Size: 5MB
Dimensions:                           (time: 6, y: 173, x: 159, bnds: 2)
Coordinates:
  * time                              (time) datetime64[ns] 48B 2015-07-16T12...
  * y                                 (y) float64 1kB 4.152e+06 ... 2.432e+06
  * x                                 (x) float64 1kB 3.534e+06 ... 5.114e+06
    x_bnds                            (x, bnds) float64 3kB 3.529e+06 ... 5.1...
    y_bnds                            (y, bnds) float64 3kB 4.157e+06 ... 2.4...
Dimensions without coordinates: bnds
Data variables:
    absorbing_aerosol_index           (time, y, x) float32 660kB dask.array<chunksize=(6, 15, 20), meta=np.ndarray>
    number_of_observations            (time, y, x) float64 1MB dask.array<chunksize=(6, 15, 20), meta=np.ndarray>
    number_of_positive_observations   (time, y, x) float64 1MB dask.array<chunksize=(6, 15, 20), meta=np.ndarray>
    positive_absorbing_aerosol_index  (time, y, x) float32 660kB dask.array<chunksize=(6, 15, 20), meta=np.ndarray>
    solar_zenith_angle                (time, y, x) float32 660kB dask.array<chunksize=(6, 15, 20), meta=np.ndarray>
    crs                               int64 8B 0
Attributes:
    Conventions:             CF-1.7
    title:                   esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-pla...
    date_created:            2025-12-08T12:14:17.027657
    processing_level:        L3
    time_coverage_start:     2015-07-01T00:00:00
    time_coverage_end:       2016-01-01T00:00:00
    time_coverage_duration:  P184DT0H0M0S
    history:                 [{'program': 'xcube_cci.chunkstore.CciChunkStore...

xarray.Dataset

• Dimensions:
  • time: 6
  • y: 173
  • x: 159
  • bnds: 2
• Coordinates: (5)
  • time
    (time)
    datetime64[ns]
    2015-07-16T12:00:00 ... 2015-12-...

    standard_name :

    time

    bounds :

    time_bnds

    array(['2015-07-16T12:00:00.000000000', '2015-08-16T12:00:00.000000000',
           '2015-09-16T00:00:00.000000000', '2015-10-16T12:00:00.000000000',
           '2015-11-16T00:00:00.000000000', '2015-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • y
    (y)
    float64
    4.152e+06 4.142e+06 ... 2.432e+06

    long_name :

    y coordinate of projection

    standard_name :

    projection_y_coordinate

    bounds :

    y_bnds

    array([4152033., 4142033., 4132033., 4122033., 4112033., 4102033., 4092033.,
           4082033., 4072033., 4062033., 4052033., 4042033., 4032033., 4022033.,
           4012033., 4002033., 3992033., 3982033., 3972033., 3962033., 3952033.,
           3942033., 3932033., 3922033., 3912033., 3902033., 3892033., 3882033.,
           3872033., 3862033., 3852033., 3842033., 3832033., 3822033., 3812033.,
           3802033., 3792033., 3782033., 3772033., 3762033., 3752033., 3742033.,
           3732033., 3722033., 3712033., 3702033., 3692033., 3682033., 3672033.,
           3662033., 3652033., 3642033., 3632033., 3622033., 3612033., 3602033.,
           3592033., 3582033., 3572033., 3562033., 3552033., 3542033., 3532033.,
           3522033., 3512033., 3502033., 3492033., 3482033., 3472033., 3462033.,
           3452033., 3442033., 3432033., 3422033., 3412033., 3402033., 3392033.,
           3382033., 3372033., 3362033., 3352033., 3342033., 3332033., 3322033.,
           3312033., 3302033., 3292033., 3282033., 3272033., 3262033., 3252033.,
           3242033., 3232033., 3222033., 3212033., 3202033., 3192033., 3182033.,
           3172033., 3162033., 3152033., 3142033., 3132033., 3122033., 3112033.,
           3102033., 3092033., 3082033., 3072033., 3062033., 3052033., 3042033.,
           3032033., 3022033., 3012033., 3002033., 2992033., 2982033., 2972033.,
           2962033., 2952033., 2942033., 2932033., 2922033., 2912033., 2902033.,
           2892033., 2882033., 2872033., 2862033., 2852033., 2842033., 2832033.,
           2822033., 2812033., 2802033., 2792033., 2782033., 2772033., 2762033.,
           2752033., 2742033., 2732033., 2722033., 2712033., 2702033., 2692033.,
           2682033., 2672033., 2662033., 2652033., 2642033., 2632033., 2622033.,
           2612033., 2602033., 2592033., 2582033., 2572033., 2562033., 2552033.,
           2542033., 2532033., 2522033., 2512033., 2502033., 2492033., 2482033.,
           2472033., 2462033., 2452033., 2442033., 2432033.])

  • x
    (x)
    float64
    3.534e+06 3.544e+06 ... 5.114e+06

    long_name :

    x coordinate of projection

    standard_name :

    projection_x_coordinate

    bounds :

    x_bnds

    array([3533853., 3543853., 3553853., 3563853., 3573853., 3583853., 3593853.,
           3603853., 3613853., 3623853., 3633853., 3643853., 3653853., 3663853.,
           3673853., 3683853., 3693853., 3703853., 3713853., 3723853., 3733853.,
           3743853., 3753853., 3763853., 3773853., 3783853., 3793853., 3803853.,
           3813853., 3823853., 3833853., 3843853., 3853853., 3863853., 3873853.,
           3883853., 3893853., 3903853., 3913853., 3923853., 3933853., 3943853.,
           3953853., 3963853., 3973853., 3983853., 3993853., 4003853., 4013853.,
           4023853., 4033853., 4043853., 4053853., 4063853., 4073853., 4083853.,
           4093853., 4103853., 4113853., 4123853., 4133853., 4143853., 4153853.,
           4163853., 4173853., 4183853., 4193853., 4203853., 4213853., 4223853.,
           4233853., 4243853., 4253853., 4263853., 4273853., 4283853., 4293853.,
           4303853., 4313853., 4323853., 4333853., 4343853., 4353853., 4363853.,
           4373853., 4383853., 4393853., 4403853., 4413853., 4423853., 4433853.,
           4443853., 4453853., 4463853., 4473853., 4483853., 4493853., 4503853.,
           4513853., 4523853., 4533853., 4543853., 4553853., 4563853., 4573853.,
           4583853., 4593853., 4603853., 4613853., 4623853., 4633853., 4643853.,
           4653853., 4663853., 4673853., 4683853., 4693853., 4703853., 4713853.,
           4723853., 4733853., 4743853., 4753853., 4763853., 4773853., 4783853.,
           4793853., 4803853., 4813853., 4823853., 4833853., 4843853., 4853853.,
           4863853., 4873853., 4883853., 4893853., 4903853., 4913853., 4923853.,
           4933853., 4943853., 4953853., 4963853., 4973853., 4983853., 4993853.,
           5003853., 5013853., 5023853., 5033853., 5043853., 5053853., 5063853.,
           5073853., 5083853., 5093853., 5103853., 5113853.])

  • x_bnds
    (x, bnds)
    float64
    3.529e+06 3.539e+06 ... 5.119e+06

    array([[3528853., 3538853.],
           [3538853., 3548853.],
           [3548853., 3558853.],
           [3558853., 3568853.],
           [3568853., 3578853.],
           [3578853., 3588853.],
           [3588853., 3598853.],
           [3598853., 3608853.],
           [3608853., 3618853.],
           [3618853., 3628853.],
           [3628853., 3638853.],
           [3638853., 3648853.],
           [3648853., 3658853.],
           [3658853., 3668853.],
           [3668853., 3678853.],
           [3678853., 3688853.],
           [3688853., 3698853.],
           [3698853., 3708853.],
           [3708853., 3718853.],
           [3718853., 3728853.],
    ...
           [4918853., 4928853.],
           [4928853., 4938853.],
           [4938853., 4948853.],
           [4948853., 4958853.],
           [4958853., 4968853.],
           [4968853., 4978853.],
           [4978853., 4988853.],
           [4988853., 4998853.],
           [4998853., 5008853.],
           [5008853., 5018853.],
           [5018853., 5028853.],
           [5028853., 5038853.],
           [5038853., 5048853.],
           [5048853., 5058853.],
           [5058853., 5068853.],
           [5068853., 5078853.],
           [5078853., 5088853.],
           [5088853., 5098853.],
           [5098853., 5108853.],
           [5108853., 5118853.]])

  • y_bnds
    (y, bnds)
    float64
    4.157e+06 4.147e+06 ... 2.427e+06

    array([[4157033., 4147033.],
           [4147033., 4137033.],
           [4137033., 4127033.],
           [4127033., 4117033.],
           [4117033., 4107033.],
           [4107033., 4097033.],
           [4097033., 4087033.],
           [4087033., 4077033.],
           [4077033., 4067033.],
           [4067033., 4057033.],
           [4057033., 4047033.],
           [4047033., 4037033.],
           [4037033., 4027033.],
           [4027033., 4017033.],
           [4017033., 4007033.],
           [4007033., 3997033.],
           [3997033., 3987033.],
           [3987033., 3977033.],
           [3977033., 3967033.],
           [3967033., 3957033.],
    ...
           [2627033., 2617033.],
           [2617033., 2607033.],
           [2607033., 2597033.],
           [2597033., 2587033.],
           [2587033., 2577033.],
           [2577033., 2567033.],
           [2567033., 2557033.],
           [2557033., 2547033.],
           [2547033., 2537033.],
           [2537033., 2527033.],
           [2527033., 2517033.],
           [2517033., 2507033.],
           [2507033., 2497033.],
           [2497033., 2487033.],
           [2487033., 2477033.],
           [2477033., 2467033.],
           [2467033., 2457033.],
           [2457033., 2447033.],
           [2447033., 2437033.],
           [2437033., 2427033.]])

• Data variables: (6)
  • absorbing_aerosol_index
    (time, y, x)
    float32
    dask.array<chunksize=(6, 15, 20), meta=np.ndarray>

    long_name :

    Absorbing aerosol index averaged for each grid cell

    units :

    1

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    grid_mapping :

    crs

    Array Chunk Bytes 644.70 kiB 7.03 kiB Shape (6, 173, 159) (6, 15, 20) Dask graph 96 chunks in 134 graph layers Data type float32 numpy.ndarray

  • number_of_observations
    (time, y, x)
    float64
    dask.array<chunksize=(6, 15, 20), meta=np.ndarray>

    long_name :

    Number of observations for each grid cell

    units :

    1

    orig_data_type :

    int32

    fill_value :

    9223372036854775807

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    int64

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    grid_mapping :

    crs

    Array Chunk Bytes 1.26 MiB 14.06 kiB Shape (6, 173, 159) (6, 15, 20) Dask graph 96 chunks in 134 graph layers Data type float64 numpy.ndarray

  • number_of_positive_observations
    (time, y, x)
    float64
    dask.array<chunksize=(6, 15, 20), meta=np.ndarray>

    long_name :

    Number of positive observations for each grid cell

    units :

    1

    orig_data_type :

    int32

    fill_value :

    9223372036854775807

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    int64

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    grid_mapping :

    crs

    Array Chunk Bytes 1.26 MiB 14.06 kiB Shape (6, 173, 159) (6, 15, 20) Dask graph 96 chunks in 134 graph layers Data type float64 numpy.ndarray

  • positive_absorbing_aerosol_index
    (time, y, x)
    float32
    dask.array<chunksize=(6, 15, 20), meta=np.ndarray>

    long_name :

    Positive absorbing aerosol index averaged for each grid cell

    units :

    1

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    grid_mapping :

    crs

    Array Chunk Bytes 644.70 kiB 7.03 kiB Shape (6, 173, 159) (6, 15, 20) Dask graph 96 chunks in 134 graph layers Data type float32 numpy.ndarray

  • solar_zenith_angle
    (time, y, x)
    float32
    dask.array<chunksize=(6, 15, 20), meta=np.ndarray>

    long_name :

    Solar zenith angle averaged for each grid cell

    units :

    degrees

    orig_data_type :

    float32

    fill_value :

    nan

    size :

    1800

    shape :

    [6, 15, 20]

    chunk_sizes :

    [1, 15, 20]

    file_chunk_sizes :

    [15, 20]

    data_type :

    float32

    dimensions :

    ['time', 'latitude', 'longitude']

    file_dimensions :

    ['latitude', 'longitude']

    grid_mapping :

    crs

    Array Chunk Bytes 644.70 kiB 7.03 kiB Shape (6, 173, 159) (6, 15, 20) Dask graph 96 chunks in 134 graph layers Data type float32 numpy.ndarray

  • crs
    ()
    int64
    0

    crs_wkt :

    PROJCRS["ETRS89-extended / LAEA Europe",BASEGEOGCRS["ETRS89",ENSEMBLE["European Terrestrial Reference System 1989 ensemble",MEMBER["European Terrestrial Reference Frame 1989"],MEMBER["European Terrestrial Reference Frame 1990"],MEMBER["European Terrestrial Reference Frame 1991"],MEMBER["European Terrestrial Reference Frame 1992"],MEMBER["European Terrestrial Reference Frame 1993"],MEMBER["European Terrestrial Reference Frame 1994"],MEMBER["European Terrestrial Reference Frame 1996"],MEMBER["European Terrestrial Reference Frame 1997"],MEMBER["European Terrestrial Reference Frame 2000"],MEMBER["European Terrestrial Reference Frame 2005"],MEMBER["European Terrestrial Reference Frame 2014"],MEMBER["European Terrestrial Reference Frame 2020"],ELLIPSOID["GRS 1980",6378137,298.257222101,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[0.1]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],ID["EPSG",4258]],CONVERSION["Europe Equal Area 2001",METHOD["Lambert Azimuthal Equal Area",ID["EPSG",9820]],PARAMETER["Latitude of natural origin",52,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8801]],PARAMETER["Longitude of natural origin",10,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8802]],PARAMETER["False easting",4321000,LENGTHUNIT["metre",1],ID["EPSG",8806]],PARAMETER["False northing",3210000,LENGTHUNIT["metre",1],ID["EPSG",8807]]],CS[Cartesian,2],AXIS["northing (Y)",north,ORDER[1],LENGTHUNIT["metre",1]],AXIS["easting (X)",east,ORDER[2],LENGTHUNIT["metre",1]],USAGE[SCOPE["Statistical analysis."],AREA["Europe - European Union (EU) countries and candidates. Europe - onshore and offshore: Albania; Andorra; Austria; Belgium; Bosnia and Herzegovina; Bulgaria; Croatia; Cyprus; Czechia; Denmark; Estonia; Faroe Islands; Finland; France; Germany; Gibraltar; Greece; Hungary; Iceland; Ireland; Italy; Kosovo; Latvia; Liechtenstein; Lithuania; Luxembourg; Malta; Monaco; Montenegro; Netherlands; North Macedonia; Norway including Svalbard and Jan Mayen; Poland; Portugal including Madeira and Azores; Romania; San Marino; Serbia; Slovakia; Slovenia; Spain including Canary Islands; Sweden; Switzerland; Türkiye (Turkey); United Kingdom (UK) including Channel Islands and Isle of Man; Vatican City State."],BBOX[24.6,-35.58,84.73,44.83]],ID["EPSG",3035]]

    semi_major_axis :

    6378137.0

    semi_minor_axis :

    6356752.314140356

    inverse_flattening :

    298.257222101

    reference_ellipsoid_name :

    GRS 1980

    longitude_of_prime_meridian :

    0.0

    prime_meridian_name :

    Greenwich

    geographic_crs_name :

    ETRS89

    horizontal_datum_name :

    European Terrestrial Reference System 1989 ensemble

    projected_crs_name :

    ETRS89-extended / LAEA Europe

    grid_mapping_name :

    lambert_azimuthal_equal_area

    latitude_of_projection_origin :

    52.0

    longitude_of_projection_origin :

    10.0

    false_easting :

    4321000.0

    false_northing :

    3210000.0

    array(0)

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2015-07-16 12:00:00', '2015-08-16 12:00:00',
                   '2015-09-16 00:00:00', '2015-10-16 12:00:00',
                   '2015-11-16 00:00:00', '2015-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • y
    PandasIndex

    PandasIndex(Index([4152033.0, 4142033.0, 4132033.0, 4122033.0, 4112033.0, 4102033.0,
           4092033.0, 4082033.0, 4072033.0, 4062033.0,
           ...
           2522033.0, 2512033.0, 2502033.0, 2492033.0, 2482033.0, 2472033.0,
           2462033.0, 2452033.0, 2442033.0, 2432033.0],
          dtype='float64', name='y', length=173))

  • x
    PandasIndex

    PandasIndex(Index([3533853.0, 3543853.0, 3553853.0, 3563853.0, 3573853.0, 3583853.0,
           3593853.0, 3603853.0, 3613853.0, 3623853.0,
           ...
           5023853.0, 5033853.0, 5043853.0, 5053853.0, 5063853.0, 5073853.0,
           5083853.0, 5093853.0, 5103853.0, 5113853.0],
          dtype='float64', name='x', length=159))

• Attributes: (8)

  Conventions :

  CF-1.7

  title :

  esacci.AEROSOL.mon.L3.AAI.multi-sensor.multi-platform.MSAAI.1-7.r1

  date_created :

  2025-12-08T12:14:17.027657

  processing_level :

  L3

  time_coverage_start :

  2015-07-01T00:00:00

  time_coverage_end :

  2016-01-01T00:00:00

  time_coverage_duration :

  P184DT0H0M0S

  history :

  [{'program': 'xcube_cci.chunkstore.CciChunkStore', 'cube_params': {'time_range': ['2015-07-01T00:00:00', '2015-12-31T00:00:00'], 'bbox': [0, 45, 20, 60], 'variable_names': ['absorbing_aerosol_index', 'number_of_observations', 'solar_zenith_angle', 'positive_absorbing_aerosol_index', 'number_of_positive_observations']}}]

Finally, we can plot the reprojected dataset.

ds_reproject.absorbing_aerosol_index.isel(time=-1).plot()

<matplotlib.collections.QuadMesh at 0x70d8c02c6ad0>