.. _findingdata:
**********
Input data
**********
Overview
========
Data discovery and retrieval is the first step in any evaluation process;
ESMValTool uses a `semi-automated` data finding mechanism with inputs from both
the user configuration file and the recipe file: this means that the user will
have to provide the tool with a set of parameters related to the data needed
and once these parameters have been provided, the tool will automatically find
the right data. We will detail below the data finding and retrieval process and
the input the user needs to specify, giving examples on how to use the data
finding routine under different scenarios.
Data types
==========
.. _CMOR-DRS:
CMIP data
---------
CMIP data is widely available via the Earth System Grid Federation
(`ESGF `_) and is accessible to users either
via automatic download by ``esmvaltool`` or through the ESGF data nodes hosted
by large computing facilities (like CEDA-Jasmin, DKRZ, etc). This data
adheres to, among other standards, the DRS and Controlled Vocabulary
standard for naming files and structured paths; the `DRS
`_
ensures that files and paths to them are named according to a
standardized convention. Examples of this convention, also used by
ESMValTool for file discovery and data retrieval, include:
* CMIP6 file: ``{variable_short_name}_{mip}_{dataset_name}_{experiment}_{ensemble}_{grid}_{start-date}-{end-date}.nc``
* CMIP5 file: ``{variable_short_name}_{mip}_{dataset_name}_{experiment}_{ensemble}_{start-date}-{end-date}.nc``
* OBS file: ``{project}_{dataset_name}_{type}_{version}_{mip}_{short_name}_{start-date}-{end-date}.nc``
Similar standards exist for the standard paths (input directories); for the
ESGF data nodes, these paths differ slightly, for example:
* CMIP6 path for BADC: ``ROOT-BADC/{institute}/{dataset_name}/{experiment}/{ensemble}/{mip}/
{variable_short_name}/{grid}``;
* CMIP6 path for ETHZ: ``ROOT-ETHZ/{experiment}/{mip}/{variable_short_name}/{dataset_name}/{ensemble}/{grid}``
From the ESMValTool user perspective the number of data input parameters is
optimized to allow for ease of use. We detail this procedure in the next
section.
Observational data
------------------
Part of observational data is retrieved in the same manner as CMIP data, for example
using the ``OBS`` root path set to:
.. code-block:: yaml
OBS: /gws/nopw/j04/esmeval/obsdata-v2
and the dataset:
.. code-block:: yaml
- {dataset: ERA-Interim, project: OBS6, type: reanaly, version: 1, start_year: 2014, end_year: 2015, tier: 3}
in ``recipe.yml`` in ``datasets`` or ``additional_datasets``, the rules set in
CMOR-DRS_ are used again and the file will be automatically found:
.. code-block::
/gws/nopw/j04/esmeval/obsdata-v2/Tier3/ERA-Interim/OBS_ERA-Interim_reanaly_1_Amon_ta_201401-201412.nc
Since observational data are organized in Tiers depending on their level of
public availability, the ``default`` directory must be structured accordingly
with sub-directories ``TierX`` (``Tier1``, ``Tier2`` or ``Tier3``), even when
``drs: default``.
.. _read_native_datasets:
Datasets in native format
-------------------------
Some datasets are supported in their native format (i.e., the data is not
formatted according to a CMIP data request) through the ``native6`` project
(mostly native reanalysis/observational datasets) or through a dedicated
project, e.g., ``ICON`` (mostly native models).
A detailed description of how to include new native datasets is given
:ref:`here `.
.. hint::
When using native datasets, it might be helpful to specify a custom location
for the :ref:`custom_cmor_tables`.
This allows reading arbitrary variables from native datasets.
Note that this requires the option ``cmor_strict: false`` in the
:ref:`project configuration ` used for the native
model output.
.. _read_native_obs:
Supported native reanalysis/observational datasets
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The following native reanalysis/observational datasets are supported under the
``native6`` project.
To use these datasets, put the files containing the data in the directory that
you have configured for the ``native6`` project in your :ref:`user
configuration file`, in a subdirectory called
``Tier{tier}/{dataset}/{version}/{frequency}/{short_name}``.
Replace the items in curly braces by the values used in the variable/dataset
definition in the :ref:`recipe `.
Below is a list of native reanalysis/observational datasets currently
supported.
.. _read_native_era5:
ERA5
^^^^
- Supported variables: ``cl``, ``clt``, ``evspsbl``, ``evspsblpot``, ``mrro``, ``pr``, ``prsn``, ``ps``, ``psl``, ``ptype``, ``rls``, ``rlds``, ``rsds``, ``rsdt``, ``rss``, ``uas``, ``vas``, ``tas``, ``tasmax``, ``tasmin``, ``tdps``, ``ts``, ``tsn`` (``E1hr``/``Amon``), ``orog`` (``fx``)
- Tier: 3
.. note:: According to the description of Evapotranspiration and potential Evapotranspiration on the Copernicus page
(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview):
"The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive.
Therefore, negative values indicate evaporation and positive values indicate condensation."
In the CMOR table, these fluxes are defined as positive, if they go from the surface into the atmosphere:
"Evaporation at surface (also known as evapotranspiration): flux of water into the atmosphere due to conversion
of both liquid and solid phases to vapor (from underlying surface and vegetation)."
Therefore, the ERA5 (and ERA5-Land) CMORizer switches the signs of ``evspsbl`` and ``evspsblpot`` to be compatible with the CMOR standard used e.g. by the CMIP models.
.. _read_native_mswep:
MSWEP
^^^^^
- Supported variables: ``pr``
- Supported frequencies: ``mon``, ``day``, ``3hr``.
- Tier: 3
For example for monthly data, place the files in the ``/Tier3/MSWEP/version/mon/pr`` subdirectory of your ``native6`` project location.
.. note::
For monthly data (``V220``), the data must be postfixed with the date, i.e. rename ``global_monthly_050deg.nc`` to ``global_monthly_050deg_197901-201710.nc``
For more info: http://www.gloh2o.org/
Data for the version ``V220`` can be downloaded from: https://hydrology.princeton.edu/data/hylkeb/MSWEP_V220/.
.. _read_native_models:
Supported native models
~~~~~~~~~~~~~~~~~~~~~~~
The following models are natively supported by ESMValCore.
In contrast to the native observational datasets listed above, they use
dedicated projects instead of the project ``native6``.
.. _read_cesm:
CESM
^^^^
ESMValTool is able to read native `CESM `__ model
output.
.. warning::
The support for native CESM output is still experimental.
Currently, only one variable (`tas`) is fully supported. Other 2D variables
might be supported by specifying appropriate facets in the recipe or extra
facets files (see text below).
3D variables (data that uses a vertical dimension) are not supported, yet.
The default naming conventions for input directories and files for CESM are
* input directories: 3 different types supported:
* ``/`` (run directory)
* ``{case}/{gcomp}/hist`` (short-term archiving)
* ``{case}/{gcomp}/proc/{tdir}/{tperiod}`` (post-processed data)
* input files: ``{case}.{scomp}.{type}.{string}*nc``
as configured in the :ref:`config-developer file ` (using the
default DRS ``drs: default`` in the :ref:`user configuration file`).
More information about CESM naming conventions are given `here
`__.
.. note::
The ``{string}`` entry in the input file names above does not only
correspond to the (optional) ``$string`` entry for `CESM model output files
`__,
but can also be used to read `post-processed files
`__.
In the latter case, ``{string}`` corresponds to the combination
``$SSTRING.$TSTRING``.
Thus, example dataset entries could look like this:
.. code-block:: yaml
datasets:
- {project: CESM, dataset: CESM2, case: f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001, type: h0, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
- {project: CESM, dataset: CESM2, case: f.e21.F1850_BGC.f09_f09_mg17.CFMIP-hadsst-piForcing.001, type: h0, gcomp: atm, scomp: cam, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
Variable-specific defaults for the facet ``gcomp`` and ``scomp`` are given in
the extra facets (see next paragraph) for some variables, but this can be
overwritten in the recipe.
Similar to any other fix, the CESM fix allows the use of :ref:`extra
facets`.
By default, the file :download:`cesm-mappings.yml
` is used for that
purpose.
Currently, this file only contains default facets for a single variable
(`tas`); for other variables, these entries need to be defined in the recipe.
Supported keys for extra facets are:
==================== ====================================== =================================
Key Description Default value if not specified
==================== ====================================== =================================
``gcomp`` Generic component-model name No default (needs to be specified
in extra facets or recipe if
default DRS is used)
``raw_name`` Variable name of the variable in the CMOR variable name of the
raw input file corresponding variable
``raw_units`` Units of the variable in the raw If specified, the value given by
input file the ``units`` attribute in the
raw input file; otherwise
``unknown``
``scomp`` Specific component-model name No default (needs to be specified
in extra facets or recipe if
default DRS is used)
``string`` Short string which is used to further ``''`` (empty string)
identify the history file type
(corresponds to ``$string`` or
``$SSTRING.$TSTRING`` in the CESM file
name conventions; see note above)
``tdir`` Entry to distinguish time averages ``''`` (empty string)
from time series from diagnostic plot
sets (only used for post-processed
data)
``tperiod`` Time period over which the data was ``''`` (empty string)
processed (only used for
post-processed data)
==================== ====================================== =================================
.. _read_emac:
EMAC
^^^^
ESMValTool is able to read native `EMAC
`_
model output.
The default naming conventions for input directories and files for EMAC are
* input directories: ``{exp}/{channel}``
* input files: ``{exp}*{channel}{postproc_flag}.nc``
as configured in the :ref:`config-developer file ` (using the
default DRS ``drs: default`` in the :ref:`user configuration file`).
Thus, example dataset entries could look like this:
.. code-block:: yaml
datasets:
- {project: EMAC, dataset: EMAC, exp: historical, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
- {project: EMAC, dataset: EMAC, exp: historical, mip: Omon, short_name: tos, postproc_flag: "-p-mm", start_year: 2000, end_year: 2014}
- {project: EMAC, dataset: EMAC, exp: historical, mip: Amon, short_name: ta, raw_name: tm1_p39_cav, start_year: 2000, end_year: 2014}
Please note the duplication of the name ``EMAC`` in ``project`` and
``dataset``, which is necessary to comply with ESMValTool's data finding and
CMORizing functionalities.
A variable-specific default for the facet ``channel`` is given in the extra
facets (see next paragraph) for many variables, but this can be overwritten in
the recipe.
Similar to any other fix, the EMAC fix allows the use of :ref:`extra
facets`.
By default, the file :download:`emac-mappings.yml
` is used for that
purpose.
For some variables, extra facets are necessary; otherwise ESMValTool cannot
read them properly.
Supported keys for extra facets are:
==================== ====================================== =================================
Key Description Default value if not specified
==================== ====================================== =================================
``channel`` Channel in which the desired variable No default (needs to be specified
is stored in extra facets or recipe if
default DRS is used)
``postproc_flag`` Postprocessing flag of the data ``''`` (empty string)
``raw_name`` Variable name of the variable in the CMOR variable name of the
raw input file corresponding variable
``raw_units`` Units of the variable in the raw If specified, the value given by
input file the ``units`` attribute in the
raw input file; otherwise
``unknown``
==================== ====================================== =================================
.. note::
``raw_name`` can be given as ``str`` or ``list``.
The latter is used to support multiple different variables names in the
input file.
In this case, the prioritization is given by the order of the list; if
possible, use the first entry, if this is not present, use the second, etc.
This is particularly useful for files in which regular averages (``*_ave``)
or conditional averages (``*_cav``) exist.
For 3D variables defined on pressure levels, only the pressure levels
defined by the CMOR table (e.g., for `Amon`'s `ta`: ``tm1_p19_cav`` and
``tm1_p19_ave``) are given in the default extra facets file.
If other pressure levels are desired, e.g., ``tm1_p39_cav``, this has to be
explicitly specified in the recipe using ``raw_name: tm1_p39_cav`` or
``raw_name: [tm1_p19_cav, tm1_p39_cav]``.
.. _read_icon:
ICON
^^^^
ESMValTool is able to read native `ICON
`_ model output.
The default naming conventions for input directories and files for ICON are
* input directories: ``{exp}`` or ``{exp}/outdata``
* input files: ``{exp}_{var_type}*.nc``
as configured in the :ref:`config-developer file ` (using the
default DRS ``drs: default`` in the :ref:`user configuration file`).
Thus, example dataset entries could look like this:
.. code-block:: yaml
datasets:
- {project: ICON, dataset: ICON, exp: icon-2.6.1_atm_amip_R2B5_r1i1p1f1,
mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
- {project: ICON, dataset: ICON, exp: historical, mip: Amon,
short_name: ta, var_type: atm_dyn_3d_ml, start_year: 2000,
end_year: 2014}
Please note the duplication of the name ``ICON`` in ``project`` and
``dataset``, which is necessary to comply with ESMValTool's data finding and
CMORizing functionalities.
A variable-specific default for the facet ``var_type`` is given in the extra
facets (see below) for many variables, but this can be overwritten in the
recipe.
This is necessary if your ICON output is structured in one variable per file.
For example, if your output is stored in files called
``__atm_2d_ml_YYYYMMDDThhmmss.nc``, use ``var_type:
_atm_2d_ml`` in the recipe for this variable.
Usually, ICON reports aggregated values at the end of the corresponding time
output intervals.
For example, for monthly output, ICON reports the month February as "1 March".
Thus, by default, ESMValCore shifts all time points back by 1/2 of the output
time interval so that the new time point corresponds to the center of the
interval.
This can be disabled by using ``shift_time: false`` in the recipe or the extra
facets (see below).
For point measurements (identified by ``cell_methods = "time: point"``), this
is always disabled.
.. warning::
To get all desired time points, do **not** use ``start_year`` and
``end_year`` in the recipe, but rather ``timerange`` with at least 8 digits.
For example, to get data for the years 2000 and 2001, use ``timerange:
20000101/20020101`` instead of ``timerange: 2000/2001`` or ``start_year:
2000``, ``end_year: 2001``.
See :ref:`timerange_examples` for more information on the ``timerange``
option.
Usually, ESMValCore will need the corresponding ICON grid file of your
simulation to work properly (examples: setting latitude/longitude coordinates
if these are not yet present, UGRIDization [see below], etc.).
This grid file can either be specified as absolute or relative (to
``auxiliary_data_dir`` as defined in the :ref:`user configuration file`) path
with the facet ``horizontal_grid`` in the recipe or the extra facets (see
below), or retrieved automatically from the `grid_file_uri` attribute of the
input files.
In the latter case, ESMValCore first searches the input directories specified
for ICON for a grid file with that name, and if that was not successful, tries
to download the file and cache it.
The cached file is valid for 7 days.
ESMValCore can automatically make native ICON data `UGRID
`__-compliant when
loading the data.
The UGRID conventions provide a standardized format to store data on
unstructured grids, which is required by many software packages or tools to
work correctly.
An example is the horizontal regridding of native ICON data to a regular grid.
While the built-in :ref:`nearest scheme ` can handle unstructured grids not in UGRID format, using more complex
regridding algorithms (for example provided by the
:doc:`iris-esmf-regrid:index` package through :ref:`generic regridding
schemes`) requires the input data in UGRID format.
The following code snippet provides a preprocessor that regrids native ICON
data to a 1°x1° grid using `ESMF's first-order conservative regridding
algorithm `__:
.. code-block:: yaml
preprocessors:
regrid_icon:
regrid:
target_grid: 1x1
scheme:
reference: esmf_regrid.schemes:ESMFAreaWeighted
This automatic UGRIDization is enabled by default, but can be switched off with
the facet ``ugrid: false`` in the recipe or the extra facets (see below).
This is useful for diagnostics that do not support input data in UGRID format
(yet) like the :ref:`Psyplot diagnostic ` or
if you want to use the built-in :ref:`nearest scheme ` regridding scheme.
For 3D ICON variables, ESMValCore tries to add the pressure level information
(from the variables `pfull` and `phalf`) and/or altitude information (from the
variables `zg` and `zghalf`) to the preprocessed output files.
If neither of these variables are available in the input files, it is possible
to specify the location of files that include the corresponding `zg` or
`zghalf` variables with the facets ``zg_file`` and/or ``zghalf_file`` in the
recipe or the extra facets.
The paths to these files can be specified absolute or relative (to
``auxiliary_data_dir`` as defined in the :ref:`user configuration file`).
.. hint::
To use the :func:`~esmvalcore.preprocessor.extract_levels` preprocessor on
native ICON data, you need to specify the name of the vertical coordinate
(e.g., ``coordinate: air_pressure``) since native ICON output usually
provides a 3D air pressure field instead of a simple 1D vertical coordinate.
This also works if your files only contain altitude information (in this
case, the US standard atmosphere is used to convert between altitude and
pressure levels; see :ref:`Vertical interpolation` for details).
Example:
.. code-block:: yaml
preprocessors:
extract_500hPa_level_from_icon:
extract_levels:
levels: 50000
scheme: linear
coordinate: air_pressure
Similar to any other fix, the ICON fix allows the use of :ref:`extra
facets`.
By default, the file :download:`icon-mappings.yml
` is used for that
purpose.
For some variables, extra facets are necessary; otherwise ESMValTool cannot
read them properly.
Supported keys for extra facets are:
=================== ================================ ===================================
Key Description Default value if not specified
=================== ================================ ===================================
``horizontal_grid`` Absolute or relative (to If not given, use file attribute
``auxiliary_data_dir`` defined ``grid_file_uri`` to retrieve ICON
in the grid file (see details above)
:ref:`user configuration file`)
path to the ICON grid file
``latitude`` Standard name of the latitude ``latitude``
coordinate in the raw input
file
``longitude`` Standard name of the ``longitude``
longitude coordinate in the
raw input file
``raw_name`` Variable name of the CMOR variable name of the
variable in the raw input corresponding variable
file
``raw_units`` Units of the variable in the If specified, the value given by
raw input file the ``units`` attribute in the
raw input file; otherwise
``unknown``
``shift_time`` Shift time points back by 1/2 of ``True``
the corresponding output time
interval
``ugrid`` Automatic UGRIDization of ``True``
the input data
``var_type`` Variable type of the No default (needs to be specified
variable in the raw input in extra facets or recipe if
file default DRS is used)
``zg_file`` Absolute or relative (to If possible, use `zg` variable
``auxiliary_data_dir`` defined provided by the raw input file
in the
:ref:`user configuration file`)
path to the input file that
contains `zg`
``zghalf_file`` Absolute or relative (to If possible, use `zghalf` variable
``auxiliary_data_dir`` defined provided by the raw input file
in the
:ref:`user configuration file`)
path to the input file that
contains `zghalf`
=================== ================================ ===================================
.. hint::
In order to read cell area files (``areacella`` and ``areacello``), one
additional manual step is necessary:
Copy the ICON grid file (you can find a download link in the global
attribute ``grid_file_uri`` of your ICON data) to your ICON input directory
and change its name in such a way that only the grid file is found when the
cell area variables are required.
Make sure that this file is not found when other variables are loaded.
For example, you could use a new ``var_type``, e.g., ``horizontalgrid`` for
this file.
Thus, an ICON grid file located in
``2.6.1_atm_amip_R2B5_r1i1p1f1/2.6.1_atm_amip_R2B5_r1i1p1f1_horizontalgrid.nc``
can be found using ``var_type: horizontalgrid`` in the recipe (assuming the
default naming conventions listed above).
Make sure that no other variable uses this ``var_type``.
If you want to use the :func:`~esmvalcore.preprocessor.area_statistics`
preprocessor on *regridded* ICON data, make sure to **not** use the cell area
files by using the ``skip: true`` syntax in the recipe as described in
:ref:`preprocessors_using_supplementary_variables`, e.g.,
.. code-block:: yaml
datasets:
- {project: ICON, dataset: ICON, exp: amip,
supplementary_variables: [{short_name: areacella, skip: true}]}
.. _read_ipsl-cm6:
IPSL-CM6
^^^^^^^^
Both output formats (i.e. the ``Output`` and the ``Analyse / Time series``
formats) are supported, and should be configured in recipes as e.g.:
.. code-block:: yaml
datasets:
- {simulation: CM61-LR-hist-03.1950, exp: piControl, out: Analyse, freq: TS_MO,
account: p86caub, status: PROD, dataset: IPSL-CM6, project: IPSLCM,
root: /thredds/tgcc/store}
- {simulation: CM61-LR-hist-03.1950, exp: historical, out: Output, freq: MO,
account: p86caub, status: PROD, dataset: IPSL-CM6, project: IPSLCM,
root: /thredds/tgcc/store}
.. _ipslcm_extra_facets_example:
The ``Output`` format is an example of a case where variables are grouped in
multi-variable files, which name cannot be computed directly from datasets
attributes alone but requires to use an extra_facets file, which principles are
explained in :ref:`extra_facets`, and which content is :download:`available here
`. These multi-variable
files must also undergo some data selection.
.. _data-retrieval:
Data retrieval
==============
Data retrieval in ESMValTool has two main aspects from the user's point of
view:
* data can be found by the tool, subject to availability on disk or `ESGF `_;
* it is the user's responsibility to set the correct data retrieval parameters;
The first point is self-explanatory: if the user runs the tool on a machine
that has access to a data repository or multiple data repositories, then
ESMValTool will look for and find the available data requested by the user.
If the files are not found locally, the tool can search the ESGF_ and download
the missing files, provided that they are available.
The second point underlines the fact that the user has full control over what
type and the amount of data is needed for the analyses. Setting the data
retrieval parameters is explained below.
Enabling automatic downloads from the ESGF
------------------------------------------
To enable automatic downloads from ESGF, set ``search_esgf: when_missing`` (use
local files whenever possible) or ``search_esgf: always`` (always search ESGF
for latest version of files and only use local data if it is the latest
version) in the :ref:`user configuration file`, or provide the corresponding
command line arguments ``--search_esgf=when_missing`` or
``--search_esgf=always`` when running the recipe.
The files will be stored in the ``download_dir`` set in
the :ref:`user configuration file`.
Setting the correct root paths
------------------------------
The first step towards providing ESMValTool the correct set of parameters for
data retrieval is setting the root paths to the data. This is done in the user
configuration file ``config-user.yml``. The two sections where the user will
set the paths are ``rootpath`` and ``drs``. ``rootpath`` contains pointers to
``CMIP``, ``OBS``, ``default`` and ``RAWOBS`` root paths; ``drs`` sets the type
of directory structure the root paths are structured by. It is important to
first discuss the ``drs`` parameter: as we've seen in the previous section, the
DRS as a standard is used for both file naming conventions and for directory
structures.
Synda
-----
If the `synda install `_ command is used to download data,
it maintains the directory structure as on ESGF. To find data downloaded by
synda, use the ``SYNDA`` ``drs`` parameter.
.. code-block:: yaml
drs:
CMIP6: SYNDA
CMIP5: SYNDA
.. _config-user-drs:
Explaining ``config-user/drs: CMIP5:`` or ``config-user/drs: CMIP6:``
---------------------------------------------------------------------
Whereas ESMValTool will **always** use the CMOR standard for file naming (please
refer above), by setting the ``drs`` parameter the user tells the tool what
type of root paths they need the data from, e.g.:
.. code-block:: yaml
drs:
CMIP6: BADC
will tell the tool that the user needs data from a repository structured
according to the BADC DRS structure, i.e.:
``ROOT/{institute}/{dataset_name}/{experiment}/{ensemble}/{mip}/{variable_short_name}/{grid}``;
setting the ``ROOT`` parameter is explained below. This is a
strictly-structured repository tree and if there are any sort of irregularities
(e.g. there is no ``{mip}`` directory) the data will not be found! ``BADC`` can
be replaced with ``DKRZ`` or ``ETHZ`` depending on the existing ``ROOT``
directory structure.
The snippet
.. code-block:: yaml
drs:
CMIP6: default
is another way to retrieve data from a ``ROOT`` directory that has no DRS-like
structure; ``default`` indicates that the data lies in a directory that
contains all the files without any structure.
.. note::
When using ``CMIP6: default`` or ``CMIP5: default`` it is important to
remember that all the needed files must be in the same top-level directory
set by ``default`` (see below how to set ``default``).
.. _config-user-rootpath:
Explaining ``config-user/rootpath:``
------------------------------------
``rootpath`` identifies the root directory for different data types (``ROOT`` as we used it above):
* ``CMIP`` e.g. ``CMIP5`` or ``CMIP6``: this is the `root` path(s) to where the
CMIP files are stored; it can be a single path or a list of paths; it can
point to an ESGF node or it can point to a user private repository. Example
for a CMIP5 root path pointing to the ESGF node on CEDA-Jasmin (formerly
known as BADC):
.. code-block:: yaml
CMIP5: /badc/cmip5/data/cmip5/output1
Example for a CMIP6 root path pointing to the ESGF node on CEDA-Jasmin:
.. code-block:: yaml
CMIP6: /badc/cmip6/data/CMIP6/CMIP
Example for a mix of CMIP6 root path pointing to the ESGF node on CEDA-Jasmin
and a user-specific data repository for extra data:
.. code-block:: yaml
CMIP6: [/badc/cmip6/data/CMIP6/CMIP, /home/users/johndoe/cmip_data]
* ``OBS``: this is the `root` path(s) to where the observational datasets are
stored; again, this could be a single path or a list of paths, just like for
CMIP data. Example for the OBS path for a large cache of observation datasets
on CEDA-Jasmin:
.. code-block:: yaml
OBS: /gws/nopw/j04/esmeval/obsdata-v2
* ``default``: this is the `root` path(s) where the tool will look for data
from projects that do not have their own rootpath set.
* ``RAWOBS``: this is the `root` path(s) to where the raw observational data
files are stored; this is used by ``esmvaltool data format``.
Dataset definitions in ``recipe``
---------------------------------
Once the correct paths have been established, ESMValTool collects the
information on the specific datasets that are needed for the analysis. This
information, together with the CMOR convention for naming files (see CMOR-DRS_)
will allow the tool to search and find the right files. The specific
datasets are listed in any recipe, under either the ``datasets`` and/or
``additional_datasets`` sections, e.g.
.. code-block:: yaml
datasets:
- {dataset: HadGEM2-CC, project: CMIP5, exp: historical, ensemble: r1i1p1, start_year: 2001, end_year: 2004}
- {dataset: UKESM1-0-LL, project: CMIP6, exp: historical, ensemble: r1i1p1f2, grid: gn, start_year: 2004, end_year: 2014}
The data finding feature will use this information to find data for **all** the variables specified in ``diagnostics/variables``.
Recap and example
=================
Let us look at a practical example for a recap of the information above:
suppose you are using a ``config-user.yml`` that has the following entries for
data finding:
.. code-block:: yaml
rootpath: # running on CEDA-Jasmin
CMIP6: /badc/cmip6/data/CMIP6/CMIP
drs:
CMIP6: BADC # since you are on CEDA-Jasmin
and the dataset you need is specified in your ``recipe.yml`` as:
.. code-block:: yaml
- {dataset: UKESM1-0-LL, project: CMIP6, mip: Amon, exp: historical, grid: gn, ensemble: r1i1p1f2, start_year: 2004, end_year: 2014}
for a variable, e.g.:
.. code-block:: yaml
diagnostics:
some_diagnostic:
description: some_description
variables:
ta:
preprocessor: some_preprocessor
The tool will then use the root path ``/badc/cmip6/data/CMIP6/CMIP`` and the
dataset information and will assemble the full DRS path using information from
CMOR-DRS_ and establish the path to the files as:
.. code-block:: bash
/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon
then look for variable ``ta`` and specifically the latest version of the data
file:
.. code-block:: bash
/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/ta/gn/latest/
and finally, using the file naming definition from CMOR-DRS_ find the file:
.. code-block:: bash
/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/ta/gn/latest/ta_Amon_UKESM1-0-LL_historical_r1i1p1f2_gn_195001-201412.nc
.. _observations:
Data loading
============
Data loading is done using the data load functionality of `iris`; we will not go into too much detail
about this since we can point the user to the specific functionality
`here `_ but we will underline
that the initial loading is done by adhering to the CF Conventions that `iris` operates by as well (see
`CF Conventions Document `_ and the search
page for CF `standard names `_).
Data concatenation from multiple sources
========================================
Oftentimes data retrieving results in assembling a continuous data stream from
multiple files or even, multiple experiments. The internal mechanism through which
the assembly is done is via cube concatenation. One peculiarity of iris concatenation
(see `iris cube concatenation `_)
is that it doesn't allow for concatenating time-overlapping cubes; this case is rather
frequent with data from models overlapping in time, and is accounted for by a function that performs a
flexible concatenation between two cubes, depending on the particular setup:
* cubes overlap in time: resulting cube is made up of the overlapping data plus left and
right hand sides on each side of the overlapping data; note that in the case of the cubes
coming from different experiments the resulting concatenated cube will have composite data
made up from multiple experiments: assume [cube1: exp1, cube2: exp2] and cube1 starts before cube2,
and cube2 finishes after cube1, then the concatenated cube will be made up of cube2: exp2 plus the
section of cube1: exp1 that contains data not provided in cube2: exp2;
* cubes don't overlap in time: data from the two cubes is bolted together;
Note that two cube concatenation is the base operation of an iterative process of reducing multiple cubes
from multiple data segments via cube concatenation ie if there is no time-overlapping data, the
cubes concatenation is performed in one step.
.. _extra-facets-data-finder:
Use of extra facets in the datafinder
=====================================
Extra facets are a mechanism to provide additional information for certain kinds
of data. The general approach is described in :ref:`extra_facets`. Here, we
describe how they can be used to locate data files within the datafinder
framework.
This is useful to build paths for directory structures and file names
that require more information than what is provided in the recipe.
A common application is the location of variables in multi-variable files as
often found in climate models' native output formats.
Another use case is files that use different names for variables in their
file name than for the netCDF4 variable name.
To apply the extra facets for this purpose, simply use the corresponding tag in
the applicable DRS inside the `config-developer.yml` file. For example, given
the extra facets in :ref:`extra-facets-example-1`, one might write the
following.
.. _extra-facets-example-2:
.. code-block:: yaml
:caption: Example drs use in `config-developer.yml`
native6:
input_file:
default: '{name_in_filename}*.nc'
The same replacement mechanism can be employed everywhere where tags can be
used, particularly in `input_dir` and `input_file`.