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Introduction

The BARRA2  reanalysis dataset provides researchers with historical long-term and spatially complete records of the atmosphere from 1979 to the present day. BARPA are climate simulations based on CORDEX-CMIP6 experiment design, providing CMIP6 downscaled data for historical, SSP1-2.6 and SSP3-7.0 scenarios.  See our CORDEX-CMIP6 (Coordinated Regional Climate Downscaling Experiment) page for more details.

To simplify the process of searching and loading BARRA2/BARPA datasets for quick analysis and visualization, you can utilize those catalog files provided by our NCI intake-esm package. These catalog files are regularly updated by crawling the metadata of BARRA2/BARPA data collection.      

You must have connected to project dk92 to access NCI intake-esm catalog files under /g/data/dk92/catalog/v2/esm. 

You must have connected to project py18 to access the BARPA dataset.

You must have connected to project ob53 to access the BARRA2 dataset.

Catalog Fields

You have the option to perform the listed operations either through an NCI ARE JupyterLab session or within a Python script. Both the BARRA2 and BARPA datasets offer numerous columns/keywords for your analysis.


BARRA2BARPA
Open Catalog Files

import intake
data_catalog = intake.open_esm_datastore("/g/data/dk92/catalog/v2/esm/barra2-ob53/catalog.json")

data_catalog

import intake
data_catalog = intake.open_esm_datastore("/g/data/dk92/catalog/v2/esm/barpa-py18/catalog.json")

data_catalog

Catalog fields

barra2-ob53 catalog with 550 dataset(s) from 285714 asset(s):


unique
path285714
file_type1
project_id1
activity_id1
domain_id1
RCM_institution_id1
driving_source_id1
driving_experiment_id1
driving_variant_label1
source_id1
version_realisation1
freq5
variable_id190
version1
start_time522
end_time522
time_range523
derived_variable_id0

Note: BARPA has 1 more field than BARRA2, i.e. "MIP_era".

barpa-py18 catalog with 8265 dataset(s) from 564946 asset(s):


unique
path564946
file_type1
project_id1
MIP_era1
activity_id1
domain_id1
RCM_institution_id1
driving_source_id8
driving_experiment_id3
driving_variant_label4
source_id1
version_realisation1
version1
variable_id191
freq5
start_time141
end_time141
time_range142
derived_variable0

You can obtain a list of unique values for each column/keyword, via the method data_catalog.df[KEYWORD].unique(). 

All unique values for each column/keyword are listed in the Table below.

Columns/KeywordsBARRA2BARPA
path

['/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/mon/zg70/v20231001/zg70_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_mon_198203-198203.nc' '/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/mon/wa250/v20231001/wa250_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_mon_200205-200205.nc' '/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/mon/wa250/v20231001/wa250_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_mon_198511-198511.nc'

 ...

 '/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/3hr/mrro/v20231001/mrro_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_3hr_198610-198610.nc' '/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/3hr/ua30/v20231001/ua30_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_3hr_198905-198905.nc' '/g/data/ob53/BARRA2/output/reanalysis/AUS-11/BOM/ERA5/historical/hres/BARRA-R2/v1/3hr/ua150/v20231001/ua150_AUS-11_ERA5_historical_hres_BOM_BARRA-R2_v1_3hr_199001-199001.nc']

['/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/EC-Earth3/ssp370/r1i1p1f1/BARPA-R/v1-r1/day/ua200/v20231001/ua200_AUS-15_EC-Earth3_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_day_204001-204012.nc' '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/EC-Earth3/ssp370/r1i1p1f1/BARPA-R/v1-r1/day/sfcWindmax/v20231001/sfcWindmax_AUS-15_EC-Earth3_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_day_208301-208312.nc' '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/EC-Earth3/ssp370/r1i1p1f1/BARPA-R/v1-r1/day/tasmin/v20231001/tasmin_AUS-15_EC-Earth3_ssp370_r1i1p1f1_BOM_BARPA-R_v1-r1_day_205801-205812.nc'

 ...

 '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/CESM2/ssp370/r11i1p1f1/BARPA-R/v1-r1/day/ta50m/v20231001/ta50m_AUS-15_CESM2_ssp370_r11i1p1f1_BOM_BARPA-R_v1-r1_day_204501-204512.nc' '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/CESM2/ssp370/r11i1p1f1/BARPA-R/v1-r1/day/ta50m/v20231001/ta50m_AUS-15_CESM2_ssp370_r11i1p1f1_BOM_BARPA-R_v1-r1_day_209901-209912.nc' '/g/data/py18/BARPA/output/CMIP6/DD/AUS-15/BOM/CESM2/ssp370/r11i1p1f1/BARPA-R/v1-r1/day/clwvi/v20231001/clwvi_AUS-15_CESM2_ssp370_r11i1p1f1_BOM_BARPA-R_v1-r1_day_207001-207012.nc']

file_type
['f']
['f']
project_id
['output']
['output']
MIP_era
---
['CMIP6']
activity_id
['reanalysis']
['DD']
domain_id
['AUS-11']
['AUS-15']
RCM_institution_id
['BOM']

['BOM']
driving_source_id
['ERA5']
['EC-Earth3' 'CESM2' 'ACCESS-CM2' 'CMCC-ESM2' 'ERA5' 'ACCESS-ESM1-5'
 'MPI-ESM1-2-HR' 'NorESM2-MM']
driving_experiment_id
['historical']
['ssp370' 'historical' 'evaluation']
driving_variant_label
['hres']
['r1i1p1f1' 'r11i1p1f1' 'r4i1p1f1' 'r6i1p1f1']
source_id
['BARRA-R2']
['BARPA-R']
version_realisation
['v1']
['v1-r1']
freq
['mon' '1hr' 'day' '3hr' 'fx']
['day' 'mon' '6hr' '1hr' 'fx']
variable_id
['zg70' 'wa250' 'ta500' 'rsutcs' 'tauu' 'hurs' 'zg150' 'mrsos' 'psl'
 'zg925' 'zg700' 'zg100' 'va150m' 'va70' 'hus250' 'huss' 'va10' 'ta50'
 'rlds' 'ua925' 'zg500' 'wa600' 'ta200' 'ta1500m' 'va400' 'ta100m' 'wa20'
 'ts' 'rsdt' 'rluscs' 'va100m' 'CIN' 'tauv' 'ua100' 'ua70' 'hus50' 'tas'
 'ps' 'zg50' 'zg30' 'wa200' 'ta150' 'wa70' 'va1500m' 'va200' 'va850'
 'ta250m' 'mrsol' 'wa850' 'ta50m' 'clwvi' 'zg850' 'mrfsos' 'hus850'
 'ta1000' 'va925' 'prsn' 'rlut' 'rlus' 'wa500' 'va200m' 'zg300' 'ua250m'
 'clt' 'ua1000' 'ta400' 'wa300' 'va250m' 'omega500' 'hfls' 'rlutcs' 'clm'
 'wa700' 'uas' 'ua50m' 'evspsblpot' 'va500' 'hus400' 'wa1000' 'ta300'
 'zg200' 'ua100m' 'hus600' 'zg400' 'ta925' 'prw' 'zg600' 'ta850' 'ua700'
 'uasmean' 'ua600' 'rldscs' 'hus500' 'hus200' 'clivi' 'ta950' 'vasmax'
 'hus1000' 'va700' 'rsds' 'zmla' 'ta200m' 'va300' 'CAPE' 'ua200' 'ta700'
 'zg1000' 'clh' 'va1000' 'rsdsdir' 'tasmin' 'vas' 'hus700' 'ta150m' 'va20'
 'prc' 'ua150m' 'zg250' 'tasmax' 'ua10' 'ua400' 'ta600' 'va50m' 'ua850'
 'sfcWind' 'hus30' 'wa100' 'mrros' 'ua250' 'hus20' 'wa30' 'ua200m' 'wa400'
 'ua500' 'hus150' 'ua20' 'va150' 'va250' 'ua50' 'va100' 'mrro' 'ta10'
 'snd' 'ua300' 'rsuscs' 'ua1500m' 'va600' 'hus300' 'hus950' 'hus925'
 'rsdscs' 'pr' 'hfss' 'wa925' 'vasmean' 'rsut' 'cll' 'ua30' 'ua150' 'zg10'
 'hus10' 'wa10' 'hus70' 'ta30' 'mrfso' 'z0' 'wa50' 'mrfsol' 'sic' 'wa150'
 'sund' 'hus100' 'sfcWindmax' 'ta250' 'zg20' 'ta100' 'snm' 'rsus' 'snw'
 'wsgsmax' 'ta70' 'tsl' 'mrso' 'va30' 'va50' 'ta20' 'tasmean' 'uasmax'
 'orog' 'sftlf']
['ua200' 'sfcWindmax' 'tasmin' 'ta100' 'snw' 'va1500m' 'wa250' 'tauu'
 'hurs' 'va200' 'zg700' 'zg100' 'va70' 'hus250' 'va10' 'zg500' 'tauv'
 'ua70' 'ps' 'zg50' 'wa70' 'tsl' 'prhmax' 'hus300' 'rsdscs' 'va50' 'prc'
 'ua10' 'va50m' 'rldscs' 'mrros' 'ta50m' 'zg850' 'mrfsos' 'va925' 'hus10'
 'wa10' 'hus70' 'rlut' 'va200m' 'ua250m' 'z0' 'wa300' 'va250m' 'omega500'
 'hfls' 'rlutcs' 'wa50' 'clm' 'evspsblpot' 'mrfsol' 'hus400' 'ua100m'
 'hus600' 'zg600' 'ta850' 'va150' 'va250' 'mrro' 'ua30' 'zmla' 'hus100'
 'CAPE' 'clh' 'rsdsdir' 'ta250' 'vas' 'snm' 'wsgsmax' 'rsus' 'psl' 'zg925'
 'va150m' 'va850' 'ta1500m' 'ta100m' 'rluscs' 'CIN' 'mrsol' 'wa200' 'ta70'
 'va600' 'zg250' 'hus30' 'zg300' 'mrfso' 'ua1000' 'zg200' 'ta925' 'hus150'
 'ua50' 'hus1000' 'ta700' 'zg20' 'zg70' 'ua100' 'hus50' 'ta150' 'rsuscs'
 'ua1500m' 'pr' 'rlus' 'ua50m' 'prw' 'clivi' 'uasmean' 'vasmax' 'ua200m'
 'rsds' 'ta200m' 'rlds' 'va100m' 'tas' 'va30' 'ta20' 'ua850' 'ua250'
 'wa30' 'ta30' 'hus500' 'hus200' 'ua600' 'va1000' 'ta150m' 'zg150' 'huss'
 'ta50' 'ua925' 'wa925' 'cll' 'ua150m' 'sfcWind' 'wa100' 'wa850' 'hus20'
 'wa700' 'va500' 'sic' 'zg400' 'snd' 'va300' 'wa400' 'ta500' 'rsutcs'
 'mrsos' 'wa600' 'va400' 'ua300' 'hus925' 'ua400' 'ta1000' 'zg10' 'wa500'
 'uas' 'ta300' 'va700' 'zg1000' 'ua500' 'wa20' 'rsdt' 'ta250m' 'zg30'
 'mrso' 'hus950' 'hus850' 'wa150' 'wa1000' 'ua20' 'va100' 'ta10' 'hfss'
 'vasmean' 'rsut' 'clt' 'va20' 'tasmax' 'ua700' 'clwvi' 'hus700' 'ta600'
 'sund' 'ua150' 'ta200' 'ta400' 'ta950' 'ts' 'prsn' 'tasmean' 'uasmax'
 'sftlf' 'orog']
version
['v20231001']
['v20231001']
start_time
[198203. 200205. 198511. 198411. 201803. 200201. 200706. 198508. 201805.
 201406. 199405. 200708. 198304. 198610. 198705. 198512. 199205. 200507.
 199706. 199705. 200901. 201205. 202004. 201501. 202102. 201802. 199210.
 200612. 200907. 199511. 199010. 199103. 199401. 200806. 201901. 198706.
 202006. 201302. 199310. 199001. 198911. 198803. 201109. 201308. 201209.
 201504. 200009. 200206. 198811. 201505. 200302. 200501. 201507. 199605.
 200112. 201509. 201402. 198601. 199502. 198212. 199208. 202002. 199509.
 199111. 201604. 199612. 199109. 199802. 201203. 199901. 200303. 198404.
 198006. 200207. 201907. 202012. 198308. 199904. 200509. 198807. 198703.
 199104. 201202. 202001. 199603. 198904. 198008. 200811. 201311. 201310.
 198312. 198205. 200401. 200406. 199712. 198901. 202109. 201911. 202101.
 201811. 199801. 200607. 200101. 201909. 200210. 200902. 200012. 197903.
 201312. 200309. 199811. 202201. 201606. 199508. 201711. 199211. 198210.
 199312. 200304. 201304. 202205. 197901. 199702. 197904. 201201. 199710.
 198403. 198908. 201510. 199412. 201607. 201001. 200108. 200810. 200602.
 200301. 199304. 200905. 202107. 200310. 199305. 198802. 198506. 200505.
 200511. 199204. 198708. 199808. 200305. 200105. 198202. 199803. 199907.
 200209. 199804. 201211. 201105. 198311. 199701. 201801. 201601. 201611.
 199107. 200103. 200702. 198510. 198010. 201704. 201706. 202003. 198007.
 200007. 201506. 198005. 201010. 199908. 200703. 202112. 199012. 198402.
 200403. 199604. 201004. 202202. 198412. 199105. 198503. 201410. 200010.
 201512. 201306. 198401. 197910. 198309. 198307. 200408. 199506. 201110.
 201709. 198702. 200001. 200912. 202203. 199106. 199403. 201301. 198701.
 198408. 199709. 201104. 201812. 198108. 198209. 201409. 202108. 200005.
 201908. 201809. 198201. 199807. 200604. 201807. 200701. 201207. 202011.
 198003. 198109. 201309. 201707. 199108. 197909. 202009. 199207. 201508.
 201403. 200110. 199308. 198812. 198903. 199810. 199002. 200204. 200106.
 201005. 202111. 200906. 199406. 200609. 200805. 201102. 199306. 201906.
 199704. 201305. 201602. 201701. 200707. 201101. 197907. 198206. 198407.
 200407. 200312. 201712. 198906. 201002. 198809. 198603. 198409. 201108.
 201401. 198004. 200203. 200308. 198106. 200803. 200008. 201012. 200808.
 200512. 200102. 199005. 201806. 200307. 198808. 200911. 200804. 200504.
 198001. 201511. 200601. 200502. 199301. 198305. 198507. 201810. 202010.
 198102. 199909. 201204. 200111. 199201. 198608. 199011. 198605. 201605.
 201106. 199602. 199006. 201904. 199411. 198207. 200606. 200006. 198611.
 199609. 198410. 201307. 200404. 197908. 201612. 198909. 198011. 200603.
 198405. 198406. 201912. 199510. 199101. 199206. 199504. 201007. 201210.
 199505. 199906. 198211. 202106. 199910. 199611. 197905. 201705. 198105.
 198711. 199409. 201808. 198810. 199711. 199102. 200211. 200510. 198805.
 199703. 200608. 201503. 199503. 200311. 198012. 200402. 200802. 199408.
 201610. 201003. 199707. 199203. 198111. 200709. 199812. 200809. 199805.
 201804. 202103. 198101. 198306. 201407. 199410. 200004. 198604. 198107.
 202105. 201408. 201703. 199507. 199004. 200801. 198509. 199512. 198712.
 200812. 200705. 202104. 198606. 200411. 200807. 201910. 201107. 198504.
 198303. 201411. 200109. 200712. 202206. 201006. 199008. 201112. 200208.
 198912. 199307. 197911. 199404. 200306. 198609. 201404. 199809. 200212.
 201111. 199003. 200710. 198501. 201608. 198002. 199608. 198806. 199209.
 198310. 197912. 200711. 201412. 200409. 199601. 199309. 199202. 200202.
 202007. 199402. 199606. 200908. 199905. 198612. 199112. 200506. 201903.
 198602. 200002. 200405. 200909. 201702. 201609. 200610. 200904. 198104.
 199009. 201008. 201009. 201103. 198110. 198710. 198009. 199303. 198704.
 199911. 201603. 198505. 198801. 199311. 200903. 198112. 200104. 200412.
 201502. 202008. 199806. 200107. 198607. 200003. 199610. 201206. 201710.
 200011. 200910. 201303. 201011. 199607. 201902. 200704. 202204. 197906.
 202005. 198204. 199902. 199708. 202110. 198707. 198910. 198301. 199912.
 199302. 201905. 198103. 198804. 198709. 198907. 198502. 201708. 197902.
 198302. 200503. 201405. 198902. 200410. 199501. 200508. 200611. 199212.
 199903. 201208. 199110. 198905. 198208. 199007. 199407. 200605. 201212.
     nan]

[204001. 208301. 205801. 209001. 208401. 203001. 206601. 204501. 203401.
 204301. 203601. 209401. 203201. 203801. 210001. 209201. 209501. 209601.
 204101. 202601. 205401. 209801. 202101. 208601. 202901. 207301. 205101.
 208201. 201501. 204801. 207401. 208501. 203501. 208101. 206001. 209301.
 205001. 207501. 206901. 209101. 207001. 205901. 209901. 202001. 207801.
 206301. 205701. 208701. 206201. 202801. 208001. 202301. 207101. 206701.
 205201. 207901. 202201. 207701. 207201. 206801. 204201. 205501. 201801.
 204701. 207601. 202701. 205301. 197301. 196001. 198501. 196901. 199601.
 197601. 197901. 200501. 201101. 197201. 200101. 199501. 197401. 197701.
 196201. 198301. 197101. 200801. 200901. 198801. 200401. 199901. 196701.
 201401. 198901. 198401. 198601. 200701. 199001. 199301. 198101. 200001.
 199401. 201001. 198001. 198701. 196601. 197801. 197001. 200201. 196501.
 199701. 200301. 197501. 199101. 201201. 199201. 196801. 200601. 196101.
 201301. 202401. 202501. 206501. 203301. 203701. 206101. 201701. 203901.
 206401. 208801. 208901. 201901. 203101. 204901. 204601. 201601. 205601.
 199801. 196401. 196301. 198201. 209701. 204401.     nan]
end_time
[198203. 200205. 198511. 198411. 201803. 200201. 200706. 198508. 201805.
 201406. 199405. 200708. 198304. 198610. 198705. 198512. 199205. 200507.
 199706. 199705. 200901. 201205. 202004. 201501. 202102. 201802. 199210.
 200612. 200907. 199511. 199010. 199103. 199401. 200806. 201901. 198706.
 202006. 201302. 199310. 199001. 198911. 198803. 201109. 201308. 201209.
 201504. 200009. 200206. 198811. 201505. 200302. 200501. 201507. 199605.
 200112. 201509. 201402. 198601. 199502. 198212. 199208. 202002. 199509.
 199111. 201604. 199612. 199109. 199802. 201203. 199901. 200303. 198404.
 198006. 200207. 201907. 202012. 198308. 199904. 200509. 198807. 198703.
 199104. 201202. 202001. 199603. 198904. 198008. 200811. 201311. 201310.
 198312. 198205. 200401. 200406. 199712. 198901. 202109. 201911. 202101.
 201811. 199801. 200607. 200101. 201909. 200210. 200902. 200012. 197903.
 201312. 200309. 199811. 202201. 201606. 199508. 201711. 199211. 198210.
 199312. 200304. 201304. 202205. 197901. 199702. 197904. 201201. 199710.
 198403. 198908. 201510. 199412. 201607. 201001. 200108. 200810. 200602.
 200301. 199304. 200905. 202107. 200310. 199305. 198802. 198506. 200505.
 200511. 199204. 198708. 199808. 200305. 200105. 198202. 199803. 199907.
 200209. 199804. 201211. 201105. 198311. 199701. 201801. 201601. 201611.
 199107. 200103. 200702. 198510. 198010. 201704. 201706. 202003. 198007.
 200007. 201506. 198005. 201010. 199908. 200703. 202112. 199012. 198402.
 200403. 199604. 201004. 202202. 198412. 199105. 198503. 201410. 200010.
 201512. 201306. 198401. 197910. 198309. 198307. 200408. 199506. 201110.
 201709. 198702. 200001. 200912. 202203. 199106. 199403. 201301. 198701.
 198408. 199709. 201104. 201812. 198108. 198209. 201409. 202108. 200005.
 201908. 201809. 198201. 199807. 200604. 201807. 200701. 201207. 202011.
 198003. 198109. 201309. 201707. 199108. 197909. 202009. 199207. 201508.
 201403. 200110. 199308. 198812. 198903. 199810. 199002. 200204. 200106.
 201005. 202111. 200906. 199406. 200609. 200805. 201102. 199306. 201906.
 199704. 201305. 201602. 201701. 200707. 201101. 197907. 198206. 198407.
 200407. 200312. 201712. 198906. 201002. 198809. 198603. 198409. 201108.
 201401. 198004. 200203. 200308. 198106. 200803. 200008. 201012. 200808.
 200512. 200102. 199005. 201806. 200307. 198808. 200911. 200804. 200504.
 198001. 201511. 200601. 200502. 199301. 198305. 198507. 201810. 202010.
 198102. 199909. 201204. 200111. 199201. 198608. 199011. 198605. 201605.
 201106. 199602. 199006. 201904. 199411. 198207. 200606. 200006. 198611.
 199609. 198410. 201307. 200404. 197908. 201612. 198909. 198011. 200603.
 198405. 198406. 201912. 199510. 199101. 199206. 199504. 201007. 201210.
 199505. 199906. 198211. 202106. 199910. 199611. 197905. 201705. 198105.
 198711. 199409. 201808. 198810. 199711. 199102. 200211. 200510. 198805.
 199703. 200608. 201503. 199503. 200311. 198012. 200402. 200802. 199408.
 201610. 201003. 199707. 199203. 198111. 200709. 199812. 200809. 199805.
 201804. 202103. 198101. 198306. 201407. 199410. 200004. 198604. 198107.
 202105. 201408. 201703. 199507. 199004. 200801. 198509. 199512. 198712.
 200812. 200705. 202104. 198606. 200411. 200807. 201910. 201107. 198504.
 198303. 201411. 200109. 200712. 202206. 201006. 199008. 201112. 200208.
 198912. 199307. 197911. 199404. 200306. 198609. 201404. 199809. 200212.
 201111. 199003. 200710. 198501. 201608. 198002. 199608. 198806. 199209.
 198310. 197912. 200711. 201412. 200409. 199601. 199309. 199202. 200202.
 202007. 199402. 199606. 200908. 199905. 198612. 199112. 200506. 201903.
 198602. 200002. 200405. 200909. 201702. 201609. 200610. 200904. 198104.
 199009. 201008. 201009. 201103. 198110. 198710. 198009. 199303. 198704.
 199911. 201603. 198505. 198801. 199311. 200903. 198112. 200104. 200412.
 201502. 202008. 199806. 200107. 198607. 200003. 199610. 201206. 201710.
 200011. 200910. 201303. 201011. 199607. 201902. 200704. 202204. 197906.
 202005. 198204. 199902. 199708. 202110. 198707. 198910. 198301. 199912.
 199302. 201905. 198103. 198804. 198709. 198907. 198502. 201708. 197902.
 198302. 200503. 201405. 198902. 200410. 199501. 200508. 200611. 199212.
 199903. 201208. 199110. 198905. 198208. 199007. 199407. 200605. 201212.
     nan]
[204012. 208312. 205812. 209012. 208412. 203012. 206612. 204512. 203412.
 204312. 203612. 209412. 203212. 203812. 210012. 209212. 209512. 209612.
 204112. 202612. 205412. 209812. 202112. 208612. 202912. 207312. 205112.
 208212. 201512. 204812. 207412. 208512. 203512. 208112. 206012. 209312.
 205012. 207512. 206912. 209112. 207012. 205912. 209912. 202012. 207812.
 206312. 205712. 208712. 206212. 202812. 208012. 202312. 207112. 206712.
 205212. 207912. 202212. 207712. 207212. 206812. 204212. 205512. 201812.
 204712. 207612. 202712. 205312. 197312. 196012. 198512. 196912. 199612.
 197612. 197912. 200512. 201112. 197212. 200112. 199512. 197412. 197712.
 196212. 198312. 197112. 200812. 200912. 198812. 200412. 199912. 196712.
 201412. 198912. 198412. 198612. 200712. 199012. 199312. 198112. 200012.
 199412. 201012. 198012. 198712. 196612. 197812. 197012. 200212. 196512.
 199712. 200312. 197512. 199112. 201212. 199212. 196812. 200612. 196112.
 201312. 202412. 202512. 206512. 203312. 203712. 206112. 201712. 203912.
 206412. 208812. 208912. 201912. 203112. 204912. 204612. 201612. 205612.
 199812. 196412. 196312. 198212. 209712. 204412.     nan]
time_range
['198203-198203' '200205-200205' '198511-198511' '198411-198411'
 '201803-201803' '200201-200201' '200706-200706' '198508-198508'
 '201805-201805' '201406-201406' '199405-199405' '200708-200708'
 '198304-198304' '198610-198610' '198705-198705' '198512-198512'
 '199205-199205' '200507-200507' '199706-199706' '199705-199705'
 '200901-200901' '201205-201205' '202004-202004' '201501-201501'
 '202102-202102' '201802-201802' '199210-199210' '200612-200612'
 '200907-200907' '199511-199511' '199010-199010' '199103-199103'
 '199401-199401' '200806-200806' '201901-201901' '198706-198706'
 '202006-202006' '201302-201302' '199310-199310' '199001-199001'
 '198911-198911' '198803-198803' '201109-201109' '201308-201308'
 '201209-201209' '201504-201504' '200009-200009' '200206-200206'
 '198811-198811' '201505-201505' '200302-200302' '200501-200501'
 '201507-201507' '199605-199605' '200112-200112' '201509-201509'
 '201402-201402' '198601-198601' '199502-199502' '198212-198212'
 '199208-199208' '202002-202002' '199509-199509' '199111-199111'
 '201604-201604' '199612-199612' '199109-199109' '199802-199802'
 '201203-201203' '199901-199901' '200303-200303' '198404-198404'
 '198006-198006' '200207-200207' '201907-201907' '202012-202012'
 '198308-198308' '199904-199904' '200509-200509' '198807-198807'
 '198703-198703' '199104-199104' '201202-201202' '202001-202001'
 '199603-199603' '198904-198904' '198008-198008' '200811-200811'
 '201311-201311' '201310-201310' '198312-198312' '198205-198205'
 '200401-200401' '200406-200406' '199712-199712' '198901-198901'
 '202109-202109' '201911-201911' '202101-202101' '201811-201811'
 '199801-199801' '200607-200607' '200101-200101' '201909-201909'
 '200210-200210' '200902-200902' '200012-200012' '197903-197903'
 '201312-201312' '200309-200309' '199811-199811' '202201-202201'
 '201606-201606' '199508-199508' '201711-201711' '199211-199211'
 '198210-198210' '199312-199312' '200304-200304' '201304-201304'
 '202205-202205' '197901-197901' '199702-199702' '197904-197904'
 '201201-201201' '199710-199710' '198403-198403' '198908-198908'
 '201510-201510' '199412-199412' '201607-201607' '201001-201001'
 '200108-200108' '200810-200810' '200602-200602' '200301-200301'
 '199304-199304' '200905-200905' '202107-202107' '200310-200310'
 '199305-199305' '198802-198802' '198506-198506' '200505-200505'
 '200511-200511' '199204-199204' '198708-198708' '199808-199808'
 '200305-200305' '200105-200105' '198202-198202' '199803-199803'
 '199907-199907' '200209-200209' '199804-199804' '201211-201211'
 '201105-201105' '198311-198311' '199701-199701' '201801-201801'
 '201601-201601' '201611-201611' '199107-199107' '200103-200103'
 '200702-200702' '198510-198510' '198010-198010' '201704-201704'
 '201706-201706' '202003-202003' '198007-198007' '200007-200007'
 '201506-201506' '198005-198005' '201010-201010' '199908-199908'
 '200703-200703' '202112-202112' '199012-199012' '198402-198402'
 '200403-200403' '199604-199604' '201004-201004' '202202-202202'
 '198412-198412' '199105-199105' '198503-198503' '201410-201410'
 '200010-200010' '201512-201512' '201306-201306' '198401-198401'
 '197910-197910' '198309-198309' '198307-198307' '200408-200408'
 '199506-199506' '201110-201110' '201709-201709' '198702-198702'
 '200001-200001' '200912-200912' '202203-202203' '199106-199106'
 '199403-199403' '201301-201301' '198701-198701' '198408-198408'
 '199709-199709' '201104-201104' '201812-201812' '198108-198108'
 '198209-198209' '201409-201409' '202108-202108' '200005-200005'
 '201908-201908' '201809-201809' '198201-198201' '199807-199807'
 '200604-200604' '201807-201807' '200701-200701' '201207-201207'
 '202011-202011' '198003-198003' '198109-198109' '201309-201309'
 '201707-201707' '199108-199108' '197909-197909' '202009-202009'
 '199207-199207' '201508-201508' '201403-201403' '200110-200110'
 '199308-199308' '198812-198812' '198903-198903' '199810-199810'
 '199002-199002' '200204-200204' '200106-200106' '201005-201005'
 '202111-202111' '200906-200906' '199406-199406' '200609-200609'
 '200805-200805' '201102-201102' '199306-199306' '201906-201906'
 '199704-199704' '201305-201305' '201602-201602' '201701-201701'
 '200707-200707' '201101-201101' '197907-197907' '198206-198206'
 '198407-198407' '200407-200407' '200312-200312' '201712-201712'
 '198906-198906' '201002-201002' '198809-198809' '198603-198603'
 '198409-198409' '201108-201108' '201401-201401' '198004-198004'
 '200203-200203' '200308-200308' '198106-198106' '200803-200803'
 '200008-200008' '201012-201012' '200808-200808' '200512-200512'
 '200102-200102' '199005-199005' '201806-201806' '200307-200307'
 '198808-198808' '200911-200911' '200804-200804' '200504-200504'
 '198001-198001' '201511-201511' '200601-200601' '200502-200502'
 '199301-199301' '198305-198305' '198507-198507' '201810-201810'
 '202010-202010' '198102-198102' '199909-199909' '201204-201204'
 '200111-200111' '199201-199201' '198608-198608' '199011-199011'
 '198605-198605' '201605-201605' '201106-201106' '199602-199602'
 '199006-199006' '201904-201904' '199411-199411' '198207-198207'
 '200606-200606' '200006-200006' '198611-198611' '199609-199609'
 '198410-198410' '201307-201307' '200404-200404' '197908-197908'
 '201612-201612' '198909-198909' '198011-198011' '200603-200603'
 '198405-198405' '198406-198406' '201912-201912' '199510-199510'
 '199101-199101' '199206-199206' '199504-199504' '201007-201007'
 '201210-201210' '199505-199505' '199906-199906' '198211-198211'
 '202106-202106' '199910-199910' '199611-199611' '197905-197905'
 '201705-201705' '198105-198105' '198711-198711' '199409-199409'
 '201808-201808' '198810-198810' '199711-199711' '199102-199102'
 '200211-200211' '200510-200510' '198805-198805' '199703-199703'
 '200608-200608' '201503-201503' '199503-199503' '200311-200311'
 '198012-198012' '200402-200402' '200802-200802' '199408-199408'
 '201610-201610' '201003-201003' '199707-199707' '199203-199203'
 '198111-198111' '200709-200709' '199812-199812' '200809-200809'
 '199805-199805' '201804-201804' '202103-202103' '198101-198101'
 '198306-198306' '201407-201407' '199410-199410' '200004-200004'
 '198604-198604' '198107-198107' '202105-202105' '201408-201408'
 '201703-201703' '199507-199507' '199004-199004' '200801-200801'
 '198509-198509' '199512-199512' '198712-198712' '200812-200812'
 '200705-200705' '202104-202104' '198606-198606' '200411-200411'
 '200807-200807' '201910-201910' '201107-201107' '198504-198504'
 '198303-198303' '201411-201411' '200109-200109' '200712-200712'
 '202206-202206' '201006-201006' '199008-199008' '201112-201112'
 '200208-200208' '198912-198912' '199307-199307' '197911-197911'
 '199404-199404' '200306-200306' '198609-198609' '201404-201404'
 '199809-199809' '200212-200212' '201111-201111' '199003-199003'
 '200710-200710' '198501-198501' '201608-201608' '198002-198002'
 '199608-199608' '198806-198806' '199209-199209' '198310-198310'
 '197912-197912' '200711-200711' '201412-201412' '200409-200409'
 '199601-199601' '199309-199309' '199202-199202' '200202-200202'
 '202007-202007' '199402-199402' '199606-199606' '200908-200908'
 '199905-199905' '198612-198612' '199112-199112' '200506-200506'
 '201903-201903' '198602-198602' '200002-200002' '200405-200405'
 '200909-200909' '201702-201702' '201609-201609' '200610-200610'
 '200904-200904' '198104-198104' '199009-199009' '201008-201008'
 '201009-201009' '201103-201103' '198110-198110' '198710-198710'
 '198009-198009' '199303-199303' '198704-198704' '199911-199911'
 '201603-201603' '198505-198505' '198801-198801' '199311-199311'
 '200903-200903' '198112-198112' '200104-200104' '200412-200412'
 '201502-201502' '202008-202008' '199806-199806' '200107-200107'
 '198607-198607' '200003-200003' '199610-199610' '201206-201206'
 '201710-201710' '200011-200011' '200910-200910' '201303-201303'
 '201011-201011' '199607-199607' '201902-201902' '200704-200704'
 '202204-202204' '197906-197906' '202005-202005' '198204-198204'
 '199902-199902' '199708-199708' '202110-202110' '198707-198707'
 '198910-198910' '198301-198301' '199912-199912' '199302-199302'
 '201905-201905' '198103-198103' '198804-198804' '198709-198709'
 '198907-198907' '198502-198502' '201708-201708' '197902-197902'
 '198302-198302' '200503-200503' '201405-201405' '198902-198902'
 '200410-200410' '199501-199501' '200508-200508' '200611-200611'
 '199212-199212' '199903-199903' '201208-201208' '199110-199110'
 '198905-198905' '198208-198208' '199007-199007' '199407-199407'
 '200605-200605' '201212-201212' 'na']
['204001-204012' '208301-208312' '205801-205812' '209001-209012'
 '208401-208412' '203001-203012' '206601-206612' '204501-204512'
 '203401-203412' '204301-204312' '203601-203612' '209401-209412'
 '203201-203212' '203801-203812' '210001-210012' '209201-209212'
 '209501-209512' '209601-209612' '204101-204112' '202601-202612'
 '205401-205412' '209801-209812' '202101-202112' '208601-208612'
 '202901-202912' '207301-207312' '205101-205112' '208201-208212'
 '201501-201512' '204801-204812' '207401-207412' '208501-208512'
 '203501-203512' '208101-208112' '206001-206012' '209301-209312'
 '205001-205012' '207501-207512' '206901-206912' '209101-209112'
 '207001-207012' '205901-205912' '209901-209912' '202001-202012'
 '207801-207812' '206301-206312' '205701-205712' '208701-208712'
 '206201-206212' '202801-202812' '208001-208012' '202301-202312'
 '207101-207112' '206701-206712' '205201-205212' '207901-207912'
 '202201-202212' '207701-207712' '207201-207212' '206801-206812'
 '204201-204212' '205501-205512' '201801-201812' '204701-204712'
 '207601-207612' '202701-202712' '205301-205312' '197301-197312'
 '196001-196012' '198501-198512' '196901-196912' '199601-199612'
 '197601-197612' '197901-197912' '200501-200512' '201101-201112'
 '197201-197212' '200101-200112' '199501-199512' '197401-197412'
 '197701-197712' '196201-196212' '198301-198312' '197101-197112'
 '200801-200812' '200901-200912' '198801-198812' '200401-200412'
 '199901-199912' '196701-196712' '201401-201412' '198901-198912'
 '198401-198412' '198601-198612' '200701-200712' '199001-199012'
 '199301-199312' '198101-198112' '200001-200012' '199401-199412'
 '201001-201012' '198001-198012' '198701-198712' '196601-196612'
 '197801-197812' '197001-197012' '200201-200212' '196501-196512'
 '199701-199712' '200301-200312' '197501-197512' '199101-199112'
 '201201-201212' '199201-199212' '196801-196812' '200601-200612'
 '196101-196112' '201301-201312' '202401-202412' '202501-202512'
 '206501-206512' '203301-203312' '203701-203712' '206101-206112'
 '201701-201712' '203901-203912' '206401-206412' '208801-208812'
 '208901-208912' '201901-201912' '203101-203112' '204901-204912'
 '204601-204612' '201601-201612' '205601-205612' '199801-199812'
 '196401-196412' '196301-196312' '198201-198212' '209701-209712'
 '204401-204412' 'na']

Search the Catalog Data

Now you can conduct search based on the above unique values for specific column/keyword. For example, you could specify the following query for BARRA2 dataset and apply .search(**query) method to conduct the search.

data_catalog = intake.open_esm_datastore("/g/data/dk92/catalog/v2/esm/barra2-ob53/catalog.json")
query = dict(
    variable_id=["ts"],
    start_time=[201404,201405],
    freq=["1hr"],
)

catalog_subset = data_catalog.search(**query)

catalog_subset

It will produce a catalog subset.

barra2-ob53 catalog with 1 dataset(s) from 2 asset(s):


unique
path2
file_type1
project_id1
activity_id1
domain_id1
RCM_institution_id1
driving_source_id1
driving_experiment_id1
driving_variant_label1
source_id1
version_realisation1
freq1
variable_id1
version1
start_time2
end_time2
time_range2
derived_variable_id0

You can run the method "catalog_subset.df" to check the full details of the search results. Obviously, it return two files with each relates to a single year.


pathfile_typeproject_idactivity_iddomain_idRCM_institution_iddriving_source_iddriving_experiment_iddriving_variant_labelsource_idversion_realisationfreqvariable_idversionstart_timeend_timetime_range
0/g/data/ob53/BARRA2/output/reanalysis/AUS-11/B...foutputreanalysisAUS-11BOMERA5historicalhresBARRA-R2v11hrtsv20231001198501.0198501.0198501-198501
1/g/data/ob53/BARRA2/output/reanalysis/AUS-11/B...foutputreanalysisAUS-11BOMERA5historicalhresBARRA-R2v11hrtsv20231001198502.0198502.0198502-198502

Loading Datasets

You can load the dataset of this catalog subset directly as below which returns a dictionary.

dsets = catalog_subset.to_dataset_dict()

You can check all keys in 'dsets' as below.

list(dsets)
>>>
['f.output.reanalysis.AUS-11.BOM.ERA5.historical.hres.BARRA-R2.v1.1hr.ts.v20231001']

For a specific key, dsets contains a Xarray dataset:

dsets['f.output.reanalysis.AUS-11.BOM.ERA5.historical.hres.BARRA-R2.v1.1hr.ts.v20231001']
>>>
xarray.Dataset
    • time: 1416
    • lat: 646
    • lon: 1082
    • time
      (time)
      datetime64[ns]
      1985-01-01 ... 1985-02-28T23:00:00

    • lat
      (lat)
      float64
      -57.97 -57.86 ... 12.87 12.98

    • lon
      (lon)
      float64
      88.48 88.59 88.7 ... 207.3 207.4

    • ts
      (time, lat, lon)
      float64
      dask.array<chunksize=(32, 4, 8), meta=np.ndarray>




  • No labels