Generate CCI cubes
Generate CCI data cubes¶
A DeepESDL example notebook¶
This notebook demonstrates how to access CCI data via two dedicated xcube stores, which provide dynamic data cube views into each gridded data set. More information on the data sets offered can be found in the Open Data Portal of the ESA Climate Change Initiative (CCI).
The xcube data store cciodp provides direct access to the datasets of the Open Data Portal. On the other hand, the xcube data store ccizarr is a subset of the cciodp store and contains CCI data that is already available in cloud-ready format, resulting in better data access performance. The store is regularly updated with new data. Details on the functionality of the two data stores can be found in the xcube documentation.
Please, also refer to the DeepESDL documentation and visit the platform's website for further information!
Brockmann Consult, 2024
This notebook runs with the python environment deepesdl-xcube-1.7.1, please checkout the documentation for help on changing the environment.
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# mandatory imports
import datetime
import matplotlib.pyplot as plt
# Utilities for notebook visualization
import shapely.geometry
from IPython.display import JSON
from xcube.core.store import (
find_data_store_extensions,
get_data_store_params_schema,
new_data_store,
)
# mandatory imports
import datetime
import matplotlib.pyplot as plt
# Utilities for notebook visualization
import shapely.geometry
from IPython.display import JSON
from xcube.core.store import (
find_data_store_extensions,
get_data_store_params_schema,
new_data_store,
)
Configure matplotlib to display graphs inline directly in the notebook and set a sensible default figure size.
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%matplotlib inline
plt.rcParams["figure.figsize"] = 16, 8
%matplotlib inline
plt.rcParams["figure.figsize"] = 16, 8
1. xcube data store cciodp¶
Check whether the cciodp store is among the available stores, if not please follow the installation information from the top of this notebook.
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JSON({e.name: e.metadata for e in find_data_store_extensions()})
JSON({e.name: e.metadata for e in find_data_store_extensions()})
Out[3]:
<IPython.core.display.JSON object>
Usually we need more information to get the actual data store object. Which data store parameters are available for cciodp?
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get_data_store_params_schema("cciodp")
get_data_store_params_schema("cciodp")
<frozen abc>:106: FutureWarning: xarray subclass VectorDataCube should explicitly define __slots__
Out[4]:
<xcube.util.jsonschema.JsonObjectSchema at 0x7f9ded4eef10>
Provide mandatory parameters to instantiate the store class:
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odp_store = new_data_store("cciodp")
odp_store = new_data_store("cciodp")
Which datasets are provided? (the list may contain both gridded and vector datasets):
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odp_store.list_data_ids()
odp_store.list_data_ids()
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['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', 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'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.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.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']
We may ask for a specific dataset ...
In [7]:
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odp_store.has_data(
"esacci.OC.5-days.L3S.CHLOR_A.multi-sensor.multi-platform.MERGED.3-1.geographic"
)
odp_store.has_data(
"esacci.OC.5-days.L3S.CHLOR_A.multi-sensor.multi-platform.MERGED.3-1.geographic"
)
Out[7]:
False
... but in many cases we want to query for certain criteria. How can we do that?
In [8]:
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odp_store.get_search_params_schema()
odp_store.get_search_params_schema()
Out[8]:
<xcube.util.jsonschema.JsonObjectSchema at 0x7f9dec048690>
Now search, let's search for sea surface temperature data (SST) with a daily frequency:
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iterator = odp_store.search_data(
cci_attrs=dict(ecv="SST", frequency="day", processing_level="L4")
)
JSON([item.to_dict() for item in iterator])
iterator = odp_store.search_data(
cci_attrs=dict(ecv="SST", frequency="day", processing_level="L4")
)
JSON([item.to_dict() for item in iterator])
/home/conda/deepesdl/909ac6b14a2e5a6c9dce2c44580b179aab714a951a659cea07707d516b61742a-20241209-101855-030681-626-xcube-1.7.1/lib/python3.11/site-packages/xcube_cci/cciodp.py:1960: CciOdpWarning: Variable "field_name" has no fill value, cannot set one. For parts where no data is available you will see random values. This is usually the case when data is missing for a time step.
warnings.warn(f'Variable "{fixed_key}" has no fill value, '
Out[9]:
<IPython.core.display.JSON object>
Which parameters must be passsed or are available to open the dataset?
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odp_store.get_open_data_params_schema(
"esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst"
)
odp_store.get_open_data_params_schema(
"esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst"
)
Out[10]:
<xcube.util.jsonschema.JsonObjectSchema at 0x7f9dec057710>
There are no required parameters, so we can decide what parameters we would like to provide them to open a dataset:
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odp_dataset = odp_store.open_data(
"esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst",
variable_names=["analysed_sst"],
time_range=["1981-08-31", "2016-12-31"],
)
odp_dataset = odp_store.open_data(
"esacci.SST.day.L4.SSTdepth.multi-sensor.multi-platform.OSTIA.2-1.sst",
variable_names=["analysed_sst"],
time_range=["1981-08-31", "2016-12-31"],
)
Plot one time stamp of the dataset for a analysed_sst in order to take a brief look at the dataset:
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odp_dataset
odp_dataset
Out[12]:
<xarray.Dataset> Size: 3TB
Dimensions: (time: 12907, lat: 3600, lon: 7200, bnds: 2)
Coordinates:
* lat (lat) float32 14kB -89.97 -89.93 -89.88 ... 89.88 89.93 89.97
lat_bnds (lat, bnds) float32 29kB dask.array<chunksize=(3600, 2), meta=np.ndarray>
* lon (lon) float32 29kB -180.0 -179.9 -179.9 ... 179.9 179.9 180.0
lon_bnds (lon, bnds) float32 58kB dask.array<chunksize=(7200, 2), meta=np.ndarray>
* time (time) datetime64[ns] 103kB 1981-08-31T12:00:00 ... 2016-12...
time_bnds (time, bnds) datetime64[ns] 207kB dask.array<chunksize=(12907, 2), meta=np.ndarray>
Dimensions without coordinates: bnds
Data variables:
analysed_sst (time, lat, lon) float64 3TB dask.array<chunksize=(1, 1200, 2400), meta=np.ndarray>
Attributes:
Conventions: CF-1.7
title: esacci.SST.day.L4.SSTdepth.multi-sensor.multi-pl...
date_created: 2024-12-10T14:29:32.620686
processing_level: L4
time_coverage_start: 1981-08-31T00:00:00
time_coverage_end: 2017-01-01T00:00:00
time_coverage_duration: P12907DT0H0M0S
history: [{'program': 'xcube_cci.chunkstore.CciChunkStore...In [13]:
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odp_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")
odp_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")
Out[13]:
<matplotlib.image.AxesImage at 0x7f9ddc208d10>
2. xcube data store ccizarr¶
Some of the CCI data is already available in a cloud-ready format, and new data is added on a regular basis. To access this data, select the ccizarr store instead of the cciodp store.
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zarr_store = new_data_store("ccizarr")
zarr_store = new_data_store("ccizarr")
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zarr_store.list_data_ids()
zarr_store.list_data_ids()
Out[15]:
['ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2018-fv2.0.zarr', 'ESACCI-BIOMASS-L4-AGB-MERGED-100m-2010-2020-fv4.0.zarr', 'ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr', 'ESACCI-GHG-L2-CO2-OCO-2-FOCAL-2014-2021-v10.zarr', 'ESACCI-GHG-L2-CO2-SCIAMACHY-WFMD-2002-2012-fv1.zarr', 'ESACCI-ICESHEETS_Antarctica_GMB-2002-2016-v1.1.zarr', 'ESACCI-ICESHEETS_Greenland_GMB-2003-2016-v1.1.zarr', 'ESACCI-L3C_CLOUD-CLD_PRODUCTS-AVHRR_NOAA-1982-2016-fv3.0.zarr', 'ESACCI-L3C_SNOW-SWE-1979-2018-fv1.0.zarr', 'ESACCI-L3C_SNOW-SWE-1979-2020-fv2.0.zarr', 'ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr', 'ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992-2015-v2.0.7b.zarr', 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_DAY-2002-2018-fv3.00.zarr', 'ESACCI-LST-L3C-LST-MODISA-0.01deg_1DAILY_NIGHT-2002-2018-fv3.00.zarr', 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_DAY-1995-2020-fv3.00.zarr', 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1DAILY_NIGHT-1995-2020-fv3.00.zarr', 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_DAY-1995-2020-fv3.00.zarr', 'ESACCI-LST-L3S-LST-IRCDR_-0.01deg_1MONTHLY_NIGHT-1995-2020-fv3.00.zarr', 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr', 'ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-IOP-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-IOP-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-IOP-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr', 'ESACCI-OC-L3S-IOP-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-1Y_YEARLY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-5D_DAILY_4km_GEO_PML_OCx_QAA-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-OC_PRODUCTS-MERGED-8D_DAILY_4km_GEO_PML_OCx_QAA-2022-fv6.0.zarr', 'ESACCI-OC-L3S-RRS-MERGED-1M_MONTHLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-RRS-MERGED-1Y_YEARLY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-RRS-MERGED-1D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-RRS-MERGED-5D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr', 'ESACCI-OC-L3S-RRS-MERGED-8D_DAILY_4km_GEO_PML_RRS-1997-2022-fv6.0.zarr', 'ESACCI-PERMAFROST-L4-ALT-MODISLST-AREA4_PP-1997-2018-fv02.0.zarr', 'ESACCI-SEAICE-L3C-SITHICK-RA2_ENVISAT-NH25KMEASE2-2002-2012-fv2.0.zarr', 'ESACCI-SEAICE-L3C-SITHICK-SIRAL_CRYOSAT2-NH25KMEASE2-2010-2017-fv2.0.zarr', 'ESACCI-SEAICE-L4-SICONC-AMSR_50.0kmEASE2-NH-2002-2017-fv2.1.zarr', 'ESACCI-SEALEVEL-IND-MSLTR-MERGED-1993-2016-fv02.zarr', 'ESACCI-SEALEVEL-L4-MSLA-MERGED-1993-2015-fv02.zarr', 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2020-fv05.3.zarr', 'ESACCI-SOILMOISTURE-L3S-SSMV-COMBINED-1978-2021-fv07.1.zarr']
The data store ccizarr has no dedicated data store parameters. The dataset must be opened as a whole, temporal and spatial subsets can follow afterwards.
In [16]:
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dataset = zarr_store.open_data(
"ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr"
)
dataset = zarr_store.open_data(
"ESACCI-OC-L3S-IOP-MERGED-1M_MONTHLY_4km_GEO_PML_OCx_QAA-1997-2020-fv5.0.zarr"
)
In [17]:
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dataset
dataset
Out[17]:
<xarray.Dataset> Size: 2TB
Dimensions: (time: 280, lat: 4320, lon: 8640)
Coordinates:
* lat (lat) float64 35kB 89.98 89.94 89.9 ... -89.94 -89.98
* lon (lon) float64 69kB -180.0 -179.9 -179.9 ... 179.9 180.0
* time (time) datetime64[ns] 2kB 1997-09-04 ... 2020-12-01
Data variables: (12/55)
MERIS_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
MODISA_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
OLCI_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
SeaWiFS_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
VIIRS_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
adg_412 (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
... ...
bbp_490 (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
bbp_510 (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
bbp_560 (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
bbp_665 (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
crs (time) int32 1kB dask.array<chunksize=(16,), meta=np.ndarray>
total_nobs_sum (time, lat, lon) float32 42GB dask.array<chunksize=(16, 1080, 1080), meta=np.ndarray>
Attributes: (12/51)
Conventions: CF-1.7
Metadata_Conventions: Unidata Dataset Discovery v1.0
catalogue_url: https://catalogue.ceda.ac.uk/uuid/88c2...
cdm_data_type: Grid
comment: See summary attribute
creation_date: Tue Feb 2 10:58:12 2021
... ...
time_coverage_duration: P1M
time_coverage_end: 19700101T000000Z
time_coverage_resolution: P1M
time_coverage_start: 19700101T000000Z
title: ESA CCI Ocean Colour Product
tracking_id: 95e99081-7011-4044-8f2b-d7db84276a4eGet an overview of the parameter scheme of the datasets in the zarr_store and create subsets accordingly
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zarr_store.get_open_data_params_schema(
"ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr"
)
zarr_store.get_open_data_params_schema(
"ESACCI-GHG-L2-CH4-SCIAMACHY-WFMD-2002-2011-fv1.zarr"
)
Out[18]:
<xcube.util.jsonschema.JsonObjectSchema at 0x7f9dcf92d150>
In [19]:
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def open_zarrstore(filename, time_range, variables):
ds = zarr_store.open_data(filename)
subset = ds.sel(time=slice(time_range[0], time_range[1]))
subset = subset[variables]
return subset
zarr_dataset = open_zarrstore(
"ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr",
time_range=[datetime.datetime(1982, 1, 1), datetime.datetime(2016, 12, 31)],
variables=["analysed_sst"],
)
def open_zarrstore(filename, time_range, variables):
ds = zarr_store.open_data(filename)
subset = ds.sel(time=slice(time_range[0], time_range[1]))
subset = subset[variables]
return subset
zarr_dataset = open_zarrstore(
"ESACCI-L4_GHRSST-SST-GMPE-GLOB_CDR2.0-1981-2016-v02.0-fv01.0.zarr",
time_range=[datetime.datetime(1982, 1, 1), datetime.datetime(2016, 12, 31)],
variables=["analysed_sst"],
)
In [20]:
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zarr_dataset
zarr_dataset
Out[20]:
<xarray.Dataset> Size: 106GB
Dimensions: (time: 12783, lat: 720, lon: 1440)
Coordinates:
* lat (lat) float32 3kB -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
* lon (lon) float32 6kB -179.9 -179.6 -179.4 ... 179.4 179.6 179.9
* time (time) datetime64[ns] 102kB 1982-01-01T12:00:00 ... 2016-12...
Data variables:
analysed_sst (time, lat, lon) float64 106GB dask.array<chunksize=(16, 720, 720), meta=np.ndarray>
Attributes: (12/47)
Conventions: CF-1.4
acknowledgment: Funded by ESA
cdm_data_type: grid
comment:
creator_email: science.leader@esa-sst-cci.org
creator_name: SST_cci
... ...
summary: An ensemble product with input from a number ...
time_coverage_end: 20170101T000000Z
time_coverage_start: 20161231T000000Z
title: Global SST Ensemble, L4 GMPE
uuid: dc0c5b25-93bf-4943-aba1-7f0de9109620
westernmost_longitude: -180.0In [21]:
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zarr_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")
zarr_dataset.analysed_sst.isel(time=0).plot.imshow(cmap="plasma")
Out[21]:
<matplotlib.image.AxesImage at 0x7f9dde164d10>
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