Databrowser

All files available on Freva are scanned and indexed in a special server (SOLR). This allows us to query the server which responds almost immediately. Because of the Freva configuration the first time you call the tool it might take up to a couple of seconds to start. After that normally you should see results within a second.

The draw back of such a system is that the metadata regarding the files is detached from the file system where they are being held. This means that you might get files that are not there anymore or don’t get some that are indeed there. Normally files does not change that fast and you should see everything in there.

You might also use this command to get much more data than just a list of paths. Please see the use cases for a fast introduction to what can be done.

Help

$ freva --databrowser --help

The query is of the form key=value. <value> might use *, ? as wildcards or any regular expression enclosed in forward slashes. Depending on your shell and the symbols used, remember to escape the sequences properly.
The safest would be to enclosed those in single quotes.

For Example:
    %s project=baseline1 model=MPI-ESM-LR experiment=/decadal200[0-3]/ time_frequency=*hr variable='/ta|tas|vu/'

Usage: freva --databrowser [options]

Options:
  -d, --debug           turn on debugging info and show stack trace on
                        exceptions.
  -h, --help            show this help message and exit
  --multiversion        select not only the latest version but all of them
  --relevant-only       show only facets that filter results (i.e. >1 possible
                        values)
  --batch-size=N        Number of files to retrieve
  --count-facet-values  Show the number of files for each values in each facet
  --attributes          retrieve all possible attributes for the current
                        search instead of the files
  --all-facets          retrieve all facets (attributes & values) instead of
                        the files
  --facet=FACET         retrieve these facets (attributes & values) instead of
                        the files

Usage in CLI

The freva --databrowser command expects a list of attribute=value (or key=value) pairs. There are a few differences and many more options (explained next). The most important thing is that you don’t need to split the search according to the type of data you are searching for. You might as well search for files both on observations, reanalysis and model data all at the same time.

Also important is that all searches are made case insensitive (so don’t worry about upper or lower casing)

You can also search for attributes themselves instead of file paths. For example you can search for the list of variables available that satisfies a certain constraint (e.g. sampled 6hr, from a certain model, etc).

The search is defined in the following terms:

$ freva --databrowser project=baseline* variable=tas time_frequency=mon

There are 12 different attributes to search for. You can see them if you double <TAB> after calling the databrowser (this feature works under BASH, for more info go to bash auto completion):

$ freva --databrowser <TAB><TAB>
cmor_table=      ensemble=        grid=            model=           project=         time_frequency=
data_type=       experiment=      institute=       product=         realm=           variable=

1. Defining the possible values

There are many more options for defining a value for a given attribute:

Attribute syntax

Meaning

attribute=value

Search for files containing exactly that attribute

attribute=val*

Search for files containing a value for attribute that starts with the prefix val

attribute=*lue

Search for files containing a value for attribute that ends with the suffix lue

attribute=*alu*

Search for files containing a value for attribute that has alu somewhere

attribute=/.*alu.*/

Search for files containing a value for attribute that matches the given regular expression (check this table for some common regular expressions)

attribute=value1 attribute=value2

Search for files containing either value1 OR value2 for the given attribute (note that’s the same attribute twice!)

attribute1=value1 attribute2=value2

Search for files containing value1 for attribute1 AND value2 for attribute2

attribute_not_=value

Search for files NOT containing value

attribute_not_=value1 attribute_not_=value2

Search for files containin NEITHER value1 or value2

Note

When using * remember that your shell might give it a different meaning, normally it will try to match files with that name.

To turn that off you can use backslash \ in most shells (\*).

For more in to that go to the commonly used regular expressions list.

2. Searching for metadata

You might as well want to now about possible values that an attribute can take after a certain search is done. For this you use the --facet flag (facets are the possible attributes that partition the result set).

For example to see the time frequency (time resolution) in which reanalysis are available you might issue the following query:

$ freva --databrowser data_type=reanalysis --facet time_frequency
time_frequency: 1hr,3hr,6hr,amon,ann,day,fx,mon,yr

or

$ freva --databrowser --facet time_frequency data_type=reanalysis
time_frequency: 1hr,3hr,6hr,amon,ann,day,fx,mon,yr

Also note that you can further define this as usual with a given query. For example check which files are at 6hr frequency:

$ freva --databrowser --facet variable data_type=reanalysis time_frequency=6hr
variable:cape,cin,clh,clivi,cll,clm,clt,clwvi,hur1000,hur250,hur500,hur700,hur850,hur925,hurs,hus1000,hus250,hus500,hus700,hus850,hus925,pr,prhmax,prw,psl,sfcwind,sfcwindmax,sli,ta,ta1000,ta250,ta500,ta700,ta850,ta925,tas,tasmax,tasmin,td2m,tos,ua,ua1000,ua250,ua500,ua700,ua850,ua925,uas,va,va1000,va250,va500,va700,va850,va925,vas,wap,zg,zg1000,zg250,zg500,zg700,zg850,zg925

If you want to see how many files would return if you further select that variable (drill down query) you may add the --count-facet-values flag (simply --count will also do):

$ freva --databrowser --count-facet-values --facet variable data_type=reanalysis time_frequency=6hr
variable: cape (1),cin (1),clh (1),clivi (1),cll (1),clm (1),clt (1),clwvi (1),hur1000 (1),hur250 (1),hur500 (1),hur700 (1),hur850 (1),hur925 (1),hurs (1),hus1000 (1),hus250 (1),hus500 (1),hus700 (1),hus850 (1),hus925 (1),pr (34),prhmax (34),prw (1),psl (8169),sfcwind (1),sfcwindmax (34),sli (1),ta (238),ta1000 (1),ta250 (1),ta500 (1),ta700 (1),ta850 (1),ta925 (1),tas (101),tasmax (34),tasmin (34),td2m (1),tos (34),ua (143),ua1000 (1),ua250 (1),ua500 (1),ua700 (1),ua850 (1),ua925 (1),uas (7922),va (143),va1000 (1),va250 (1),va500 (1),va700 (1),va850 (1),va925 (1),vas (7922),wap (139),zg (383),zg1000 (1),zg250 (1),zg500 (1),zg700 (1),zg850 (1),zg925 (1)

This means that there are 8169 files containing the variable psl, 7922 files containing the variables vas and uas, and so on.

Currently there is no possibility to show the values of more than one attribute at a time (i.e. --facet variable --facet data_type or --facet variable,data_type does not work). However, you can check ALL facets at once. For that you may use the --all-facets flag:

$ freva --databrowser --all-facets data_type=reanalysis time_frequency=6hr cmor_table=6hrplev
cmor_table: 6hrplev
product: reanalysis
v\ersion:
data_type: reanalysis
institute: ecmwf,jma-criepi,nasa-gmao,ncep-ncar,noaa-cires
project: reanalysis
time_frequency: 6hr
experiment: 20cr,20cr-em,cfsr,era40,era40-hr,eraint,jra-25,jra-25-fc,merra,ncep1,ncep2
grid:
variable: pr,prhmax,psl,sfcwindmax,ta,tas,tasmax,tasmin,tos,ua,uas,va,vas,wap,zg
realm: atmos,ocean
model: cdas,cfs,geos-5,gfs,ifs,jcdas
ensemble: r10i1p1,r11i1p1,r12i1p1,r13i1p1,r14i1p1,r15i1p1,r16i1p1,r17i1p1,r18i1p1,r19i1p1,r1i1p1,r20i1p1,r21i1p1,r22i1p1,r23i1p1,r24i1p1,r25i1p1,r26i1p1,r27i1p1,r28i1p1,r29i1p1,r2i1p1,r30i1p1,r31i1p1,r32i1p1,r33i1p1,r34i1p1,r35i1p1,r36i1p1,r37i1p1,r38i1p1,r39i1p1,r3i1p1,r40i1p1,r41i1p1,r42i1p1,r43i1p1,r44i1p1,r45i1p1,r46i1p1,r47i1p1,r48i1p1,r49i1p1,r4i1p1,r50i1p1,r51i1p1,r52i1p1,r53i1p1,r54i1p1,r55i1p1,r56i1p1,r5i1p1,r6i1p1,r7i1p1,r8i1p1,r9i1p1

And again you can also have the --count flag:

$ freva --databrowser --all-facets --count data_type=reanalysis time_frequency=6hr
cmor_table: 6hr (51),6hrplev (25362)
product: reanalysis (25413)
v\ersion:
data_type: reanalysis (25413)
institute: ecmwf (693),jma-criepi (336),miklip-module-c (51),nasa-gmao (101),ncep-ncar (520),noaa-cires (23712)
project: reanalysis (25413)
time_frequency: 6hr (25413)
experiment: 20cr (23520),20cr-em (192),cfsr (96),era40 (200),era40-hr (126),eraint (367),eraint-eur-22-cclm (51),jra-25 (132),jra-25-fc (204),merra (101),ncep1 (390),ncep2 (34)
grid:
variable: cape (1),cin (1),clh (1),clivi (1),cll (1),clm (1),clt (1),clwvi (1),hur1000 (1),hur250 (1),hur500 (1),hur700 (1),hur850 (1),hur925 (1),hurs (1),hus1000 (1),hus250 (1),hus500 (1),hus700 (1),hus850 (1),hus925 (1),pr (34),prhmax (34),prw (1),psl (8169),sfcwind (1),sfcwindmax (34),sli (1),ta (238),ta1000 (1),ta250 (1),ta500 (1),ta700 (1),ta850 (1),ta925 (1),tas (101),tasmax (34),tasmin (34),td2m (1),tos (34),ua (143),ua1000 (1),ua250 (1),ua500 (1),ua700 (1),ua850 (1),ua925 (1),uas (7922),va (143),va1000 (1),va250 (1),va500 (1),va700 (1),va850 (1),va925 (1),vas (7922),wap (139),zg (383),zg1000 (1),zg250 (1),zg500 (1),zg700 (1),zg850 (1),zg925 (1)
realm: atmos (25379),ocean (34)
model: cclm-eur-22 (51),cdas (424),cfs (96),geos-5 (101),gfs (23712),ifs (693),jcdas (336)
ensemble: r10i1p1 (420),r11i1p1 (420),r12i1p1 (420),r13i1p1 (420),r14i1p1 (420),r15i1p1 (420),r16i1p1 (420),r17i1p1 (420),r18i1p1 (420),r19i1p1 (420),r1i1p1 (2262),r1i1p1-ds4r1e8 (51),r20i1p1 (420),r21i1p1 (420),r22i1p1 (420),r23i1p1 (420),r24i1p1 (420),r25i1p1 (420),r26i1p1 (420),r27i1p1 (420),r28i1p1 (420),r29i1p1 (420),r2i1p1 (420),r30i1p1 (420),r31i1p1 (420),r32i1p1 (420),r33i1p1 (420),r34i1p1 (420),r35i1p1 (420),r36i1p1 (420),r37i1p1 (420),r38i1p1 (420),r39i1p1 (420),r3i1p1 (420),r40i1p1 (420),r41i1p1 (420),r42i1p1 (420),r43i1p1 (420),r44i1p1 (420),r45i1p1 (420),r46i1p1 (420),r47i1p1 (420),r48i1p1 (420),r49i1p1 (420),r4i1p1 (420),r50i1p1 (420),r51i1p1 (420),r52i1p1 (420),r53i1p1 (420),r54i1p1 (420),r55i1p1 (420),r56i1p1 (420),r5i1p1 (420),r6i1p1 (420),r7i1p1 (420),r8i1p1 (420),r9i1p1 (420)

You might have also seen that some facets are not relevant at all as they are not partitioning the resulting data (e.g. see version or grid). You can leave them out by adding the --relevant-only flag

$ freva --databrowser --all-facets --count --relevant-only data_type=reanalysis time_frequency=6hr
cmor_table: 6hr (51),6hrplev (25362)
institute: ecmwf (693),jma-criepi (336),miklip-module-c (51),nasa-gmao (101),ncep-ncar (520),noaa-cires (23712)
experiment: 20cr (23520),20cr-em (192),cfsr (96),era40 (200),era40-hr (126),eraint (367),eraint-eur-22-cclm (51),jra-25 (132),jra-25-fc (204),merra (101),ncep1 (390),ncep2 (34)
variable: cape (1),cin (1),clh (1),clivi (1),cll (1),clm (1),clt (1),clwvi (1),hur1000 (1),hur250 (1),hur500 (1),hur700 (1),hur850 (1),hur925 (1),hurs (1),hus1000 (1),hus250 (1),hus500 (1),hus700 (1),hus850 (1),hus925 (1),pr (34),prhmax (34),prw (1),psl (8169),sfcwind (1),sfcwindmax (34),sli (1),ta (238),ta1000 (1),ta250 (1),ta500 (1),ta700 (1),ta850 (1),ta925 (1),tas (101),tasmax (34),tasmin (34),td2m (1),tos (34),ua (143),ua1000 (1),ua250 (1),ua500 (1),ua700 (1),ua850 (1),ua925 (1),uas (7922),va (143),va1000 (1),va250 (1),va500 (1),va700 (1),va850 (1),va925 (1),vas (7922),wap (139),zg (383),zg1000 (1),zg250 (1),zg500 (1),zg700 (1),zg850 (1),zg925 (1)
realm: atmos (25379),ocean (34)
model: cclm-eur-22 (51),cdas (424),cfs (96),geos-5 (101),gfs (23712),ifs (693),jcdas (336)
ensemble: r10i1p1 (420),r11i1p1 (420),r12i1p1 (420),r13i1p1 (420),r14i1p1 (420),r15i1p1 (420),r16i1p1 (420),r17i1p1 (420),r18i1p1 (420),r19i1p1 (420),r1i1p1 (2262),r1i1p1-ds4r1e8 (51),r20i1p1 (420),r21i1p1 (420),r22i1p1 (420),r23i1p1 (420),r24i1p1 (420),r25i1p1 (420),r26i1p1 (420),r27i1p1 (420),r28i1p1 (420),r29i1p1 (420),r2i1p1 (420),r30i1p1 (420),r31i1p1 (420),r32i1p1 (420),r33i1p1 (420),r34i1p1 (420),r35i1p1 (420),r36i1p1 (420),r37i1p1 (420),r38i1p1 (420),r39i1p1 (420),r3i1p1 (420),r40i1p1 (420),r41i1p1 (420),r42i1p1 (420),r43i1p1 (420),r44i1p1 (420),r45i1p1 (420),r46i1p1 (420),r47i1p1 (420),r48i1p1 (420),r49i1p1 (420),r4i1p1 (420),r50i1p1 (420),r51i1p1 (420),r52i1p1 (420),r53i1p1 (420),r54i1p1 (420),r55i1p1 (420),r56i1p1 (420),r5i1p1 (420),r6i1p1 (420),r7i1p1 (420),r8i1p1 (420),r9i1p1 (420)

If you try to retrieve all variables stored (remember there are over 15 million files!) you’ll notice that some searches might bring overwhelming results:

$ freva --databrowser --facet variable
variable: abs550aer,acabf,acabfis,aerasymbnd,aeroptbnd,aerssabnd,ageice,agesno,agessc,airmass,al,albc,albdiffbnd,albdirbnd,albedo,albisccp,albsn,amo,amo-g,aoanh,aod550volso4,arag,aragos,areacella,areacello,areacellr,ares,atlantic_moc,avspsbl,bacc,baccos,baresoilfrac,basin,bbi-tslen,bbihghatm90frac,bbitm90frac,bddtalk,bddtdic,bddtdife,bddtdin,bddtdip,bddtdisi,bfe,bfeos,bigthetao,bigthetaoga,bldep,bmelt,bry,bsi,bsios,budg,budgsatm,burntarea,burntfractionall,c13land,c13litter,c13soil,c13veg,c14land,c14litter,c14soil,c14veg,c2h2,c2h6,c3h6,c3h8,c3pftfrac,c4pftfrac,calc,calcos,cape,ccb,ccldncl,ccn,cct,ccwd,cdd,cddetccdi,cdnc,cfad2lidarsr532,cfaddbze94,cfadlidarsr532,cfc11,cfc113global,cfc11global,cfc12,cfc12global,cfl,cflstddev,ch3coch3,ch4,ch4global,cheaqpso4,chegpso4,chepasoa,chepsoa,chl,chl3d,chlcalc,chlcalcos,chldiat,chldiatos,chldiaz,chldiazos,chlmisc,chlmiscos,chlos,chlpico,chlpicoos,ci,cin,cl,cland,clc,clcalipso,clcalipso2,clcalipsoice,clcalipsoliq,clccalipso,cldicemxrat,cldnci,cldncl,cldnvi,cldwatmxrat,cleaf,clh,clhcalipso,cli,clic,climodis,clis,clisccp,clitter,clitterabove,clitterbelow,clittercwd,clittergrass,clitterlut [...]

There is no upper limit for the number of facets shown. Here [...] has been written ad hoc because for this particular case the output comprised more than different 1,700 variable types. To avoid that, we can stablish an upper bound number of outputs with facet.limit. That’s the number of results that will be retrieved. Remember that setting it to -1 retrieves just everything again… be aware that make cause some problems if you don’t know what you are doing (well sometimes it might also cause problems if you do… so use with discretion)

$ freva --databrowser --facet variable facet.limit=100
variable: abs550aer,acabf,acabfis,aerasymbnd,aeroptbnd,aerssabnd,ageice,agesno,agessc,airmass,al,albc,albdiffbnd,albdirbnd,albedo,albisccp,albsn,amo,amo-g,aoanh,aod550volso4,arag,aragos,areacella,areacello,areacellr,ares,atlantic_moc,avspsbl,bacc,baccos,baresoilfrac,basin,bbi-tslen,bbihghatm90frac,bbitm90frac,bddtalk,bddtdic,bddtdife,bddtdin,bddtdip,bddtdisi,bfe,bfeos,bigthetao,bigthetaoga,bldep,bmelt,bry,bsi,bsios,budg,budgsatm,burntarea,burntfractionall,c13land,c13litter,c13soil,c13veg,c14land,c14litter,c14soil,c14veg,c2h2,c2h6,c3h6,c3h8,c3pftfrac,c4pftfrac,calc,calcos,cape,ccb,ccldncl,ccn,cct,ccwd,cdd,cddetccdi,cdnc,cfad2lidarsr532,cfaddbze94,cfadlidarsr532,cfc11,cfc113global,cfc11global,cfc12,cfc12global,cfl,cflstddev,ch3coch3,ch4,ch4global,cheaqpso4,chegpso4,chepasoa,chepsoa,chl,chl3d,chlcalc...

By the way, do you want to count them? Those are 1,756 variables!

$ freva --databrowser --facet variable | tr ',' '\n' | wc -l
1756

3. Auto-completion

The databrowser has auto-completion embedded in it. If you are using bash, everything is already setup when you issued the module load <freva> command. Whenever you hit tab the word will be completed to the longest unique string that matches your previous input. A second tab will bring up a list of all possible completions after that.

For example ( denotes pressing the tab key):

$ freva --databrowser time_freq<TAB>

results in

$ freva --databrowser time_frequency=

Now pressing again will show all other possibilities:

$ freva --databrowser time_frequency=<TAB>
1hr          6hr          amon         dec          grid         mon          monpt        sem          subhrpt      year
3hr          6hrpt        ann          djf          jja          monc         month        son          subyr        yr
3hrpt        amjjas       day          fx           mam          monclim      ondjfm       subhr        time_series  yrpt

But flags are not the only thing being populated, it also work on attributes (Be aware that after --databrowser there is a space):

$ freva --databrowser <TAB><TAB>
cmor_table=      ensemble=        grid=            model=           project=         time_frequency=
data_type=       experiment=      institute=       product=         realm=           variable=

… and of course values:

$ freva --databrowser grid=gr<TAB><TAB>
gr    gr1   gr1z  gr2   gr2z  gr3   gra   grg   grz

And this is also query aware:

$ freva --databrowser institute=<TAB>
as-rcec              cnes                 essl                 inm                  mpi-esm1-2-hr        ncep-ncar            preop-edf
atmos                cnrm                 eth                  inpe                 mpi-esm-hr           nerc                 remss
awi                  cnrm-cerfacs         fio                  ipsl                 mpi-esm-lr           nicam                rmib-ugent
b1                   cola-cfs             fio-qlnm             ipsl-cm6a-lr         mpi-m                nimr-kma             smhi
bcc                  cru                  fub                  ipsl-ineris          mpi-m-1              nims-kma             snu
bnu                  csiro                fub-dwd              jma-criepi           mpi-m-2              niwa                 thu
cams                 csiro-arccss         fub-ifm              knmi                 mpi-m-3              noaa                 ua
canesm5              csiro-bom            geomar               lasg-cess            mpi-m-4              noaa-cires           ub
cas                  csiro-qccce          gerics               lasg-iap             mpi-m-5              noaa-gfdl            [...]


$ freva --databrowser data_type=reanalysis institute=<TAB><TAB>
ecmwf            jma-criepi       miklip-module-c  nasa-gmao        ncep-ncar        noaa             noaa-cires       zmaw

Note

If you mix flags this might not work as intended (or not at all).

4. Examples

  1. Apply regular expressions to the search. Find numbered mip products of cmip6, that include either meridional or zonal wind speed, or temperature (surface or at different levels). Reduce the search to certain ensemble members:

$ freva --databrowser project=cmip6 product=/.*[0-9].*/mip variable='/ta[s]*|vu/' ensemble=/r[1,3]i1p[0-9]f1/  --all-facets
...
product: c4mip,ls3mip
...
variable: ta,tas
...
ensemble: r1i1p1f1,r1i1p2f1,r3i1p1f1,r3i1p2f1
  1. Find out all the relevant facets at official (not user crawled) ERA5 data for hourly and monthly time frequencies:

$ freva --databrowser data_type=reanalysis experiment=era5 time_frequency=1hr time_frequency=mon --all-facets --count --relevant-only
cmor_table: 1hr (33166),1hr-cf (8682),1hr-era5 (6128),amon (2451),limon (86),lmon (301),mon-cf (688),mon-era5 (516),oimon (43),omon (43)
realm: atmos (40473),land (8307),landice (2216),ocean (554),seaice (554)
time_frequency: 1hr (47976),mon (4128)
variable: asn (554),bld (553),cape (553),clivi (553),clt (554),clwvi (553),cvh (554),cvl (554),esn (553),evspsbl (553),fal (554),flsr (554),fsr (554),gwd (553),hcc (554),hfls (553),hfss (553),lcc (554),lspf (553),mcc (554),mrro (553),pr (553),prcprof (553),prlsns (553),prlsprof (553),prsn (553),prsnc (553),prw (554),ps (554),psl (554),rlds (553),rldscs (553),rlut (553),rlutcs (553),rsds (553),rsdscs (553),rsn (554),rss (553),rsut (553),rsutcs (553),sic (554),skt (554),snd (554),snm (553),src (554),strd (553),swvl1 (554),swvl2 (554),swvl3 (554),swvl4 (554),ta (2765),tas (554),tasmax (553),tasmaxx (43),tasmin (553),tasminn (43),tauu (553),tauv (553),tco3 (554),tcw (554),tdps (554),tisr (553),tos (554),tsl1 (554),tsl2 (554),tsl3 (554),tsl4 (554),tsn (554),tvh (554),tvl (554),ua (2765),uas (554),uvb (553),va (2765),vas (554),wsgsmax (553),xgwdparam (553),ygwdparam (553),zg (2765),zmla (553)
ensemble: r1i1p1 (41044),r1i1p1-030000pa (2216),r1i1p1-050000pa (2216),r1i1p1-070000pa (2216),r1i1p1-085000pa (2196),r1i1p1-100000pa (2216)
  1. Find out if a file was republished under different versions with --multiversion feature:

$ freva --databrowser --multiversion --facet version data_type=baseline0 variable=tas time_frequency=mon ensemble=r1i1p1
version: 20110812,20111005,20111006,20111014,20111119,20111122,20120308,20120315,20120529,20120602,20120627,20120806,20120823,20120917,20140225
  1. Find what a given variable name stands for (e.g. wetso4):

$ ncdump -h $(freva --databrowser variable=wetso4 | head -n 1 )| grep wetso4:standard_name
                wetso4:standard_name = "tendency_of_atmosphere_mass_content_of_sulfate_expressed_as_sulfur_dry_aerosol_due_to_wet_deposition" ;

Usage in web

The usage through web is more intuitive but less flexible as via CLI. For example, for CMIP-DICAD:

../../../../_images/databrowser1.png



  • The Data Browser is accessed clicking its corresponding tab (box 1). In this menu already the 12 facets are visible, with the number of different values for each facet in parenthesis.

  • Every facet can be unfolded clicking on it (2), revealing all the different values contained within. Beside each value, in parenthesis, is the number of files grouped within. The values can be selected either clicking or writing them in the menu bar below the name of each facet. This text entry can be either an exact name of the value or an approximate name of a variable (e.g. temp or temperature would select variables such as ta, tas but also sst etc.).The values narrow down at the path of the typed text.

  • The instruction are translated to its CLI equivalent (freva --databrowser [options]) ready to be copied into the terminal (3).

  • Everytime one facet value is selected the folder tree collapses narrowing down the number of accessible total files (in 4).

Once the desired query is completed a certain amount of files will be shown. For example:

../../../../_images/databrowser2.png

would correspond to the following query:

$ freva --databrowser project=cmip6 product=c4mip institute=mpi-m experiment=esm-1pctco2 time_frequency=mon realm=atmos variable=tas

Freva allows the inspection of the metadata of each file. For that it runs a ncdump -h over the file once the information icon is clicked. The execution of the command is done via ssh on the credentials of the user, hence, it asks for the LDAP password to complete it (only the first time):

../../../../_images/databrowser3.png

Note

Please be aware that path the SOLR server shows on the data browser only reflects the state of the files at the time of the snapshot. It does not necessarily correspond to a currently existing file anymore, as the file could have been either renamed, moved or erased after the indexation took place.