Adding ancillary spatial data#

This tutorial demonstrates how to add ancillary spatial data to the OpenGHG store. These are split into several data types which currently include:

  • “footprints”: regional outputs from an LPDM model [1] (e.g. NAME)

  • “emissions”: estimates of species emissions/flux [2] within a region

  • “boundary_conditions”: vertical curtains at the boundary of a regional domain

  • “eulerian_model”: Global CTM output (e.g. GEOSChem) [3]

These inputs must adhere to an expected netCDF format with a minimum fixed set of inputs (see below).

Note

For some often used databases and inputs, there will be limited transformation functions available to create and store data in this format directly. See the openghg.transform sub-module for options.

Using the tutorial object store#

As in the previous tutorial, we will use the tutorial object store to avoid cluttering your personal object store.

from openghg.tutorial import use_tutorial_store

use_tutorial_store()

1. Adding and standardising data#

Note

Outside of this tutorial, if you have write access to multiple object stores you will need to pass the name of the object store you wish to write to to the store argument of the standardise functions.

Data can be added to the object store using appropriate functions from the openghg.standardise sub module. This includes:

  • standardise_footprints

  • standardise_flux

  • standardise_bc

  • standardise_eulerian [3]

For all data types, a set of keywords should be supplied in addition to the path to the data file. These keywords depend on the data type; they are used to categorise the data.

We can grab some data which is in the expected format for our different types so these can be added to the object store.

from openghg.tutorial import retrieve_example_data

fp_url = "https://github.com/openghg/example_data/raw/main/footprint/tac_footprint_inert_201607.tar.gz"
data_file_fp = retrieve_example_data(url=fp_url)[0]

flux_url = "https://github.com/openghg/example_data/raw/main/flux/ch4-ukghg-all_EUROPE_2016.tar.gz"
data_file_flux = retrieve_example_data(url=flux_url)[0]

bc_url = "https://github.com/openghg/example_data/raw/main/boundary_conditions/ch4_EUROPE_201607.tar.gz"
data_file_bc = retrieve_example_data(url=bc_url)[0]

Downloading tac_footprint_inert_201607.tar.gz: 100%|██████████| 67.0M/67.0M [00:17<00:00, 3.95MB/s]
Downloading ch4-ukghg-all_EUROPE_2016.tar.gz: 100%|██████████| 82.5k/82.5k [00:00<00:00, 2.38MB/s]
Downloading ch4_EUROPE_201607.tar.gz: 100%|██████████| 77.4k/77.4k [00:00<00:00, 4.22MB/s]

Data domains#

For spatial data, a domain label indicates consistent areas and resolution. For multiple pieces of data, a single domain name should refer to the exact same set of latitude and longitude bounds.

Pre-defined domains can be listed as follows:

from openghg.util import get_domain_info

get_domain_info()

You can also add new domain labels and definitions as needed.

Footprints#

To standardise [4] footprint data, we pass the following arguments to standardise_footprint:

  • filepath: path to footprint file (this is the first positional argument)

  • site: site identifier (use openghg.standardise.summary_site_codes() function to check this)

  • inlet: inlet height associated with the site

  • domain: regional domain covered by the footprint

  • model: name of model used to create the footprint

Additional details can also be specified, in particular, for the meteorological model used (metmodel) and the species name (if relevant).

For the example below, the footprint data generated from the NAME model for the Tacolneston (TAC) site at 100m inlet. This covers an area over Europe which we have defined as the “EUROPE” domain. Unless a specific species is specified, this will be assumed to be a generic inert species.

from openghg.standardise import standardise_footprint

standardise_footprint(data_file_fp, site="TAC", domain="EUROPE", inlet="100m", model="NAME")

WARNING:openghg.store:This file has been uploaded previously with the filename : TAC-100magl_UKV_EUROPE_201607.nc - skipping.

This standardised data can then be accessed and retrieved from the object store using the get_footprint function available from the openghg.retrieve submodule.

from openghg.retrieve import get_footprint

footprint_data = get_footprint(site="TAC", domain="EUROPE", inlet="100m")

For the standards associated the footprint files, there are also flags which can be passed to sub-categorise the footprint inputs: - high_spatial_resolution - footprints containing multiple spatial resolutions. This is usually an embedded high resolution region within a larger lower resolution domain. - high_time_resolution - footprints which include an additional dimension for resolving on the time axis. This is associated with shorter term flux changes (e.g. natural sources of carbon dioxide). A species will normally be associated with this footprint (e.g. “co2”). - short_lifetime - footprints for species with a shorter lifetime (<30 days). An explicit species input should be associated with this footprint as well.

  • high_spatial_res: footprints containing multiple spatial resolutions. This is usually an embedded high resolution region within a larger lower resolution domain.

  • high_time_res: footprints which include an additional dimension for resolving on the time axis. This is associated with shorter term flux changes (e.g. natural sources of carbon dioxide). A species will normally be associated with this footprint (e.g. “co2”).

  • short_lifetime: footprints for species with a shorter lifetime (less than 30 days). An explicit species input should be associated with this footprint as well.

If possible, standardise_footprint will attempt to infer these details, but they should be supplied to ensure the footprint data is labelled correctly. See schema details below for how these inputs are defined.

Flux / Emissions#

Flux/emissions data typically describes “prior guesses” for emissions of substances, often broken down by region and source of emission.

To standardise flux/emissions data [2] we pass the following arguments to standardise_flux:

  • filepath: path to footprint file (this is the first positional argument)

  • species: a name for the associated species

  • domain: the regional domain covered by the flux data

  • source: a name for the source of that data.

Additional optional identifiers include:

  • database: inventory/database name associated with the flux data

  • database_version: if a database is specified, a version should be included as well

  • model: the name of the model used to generate the flux data

For the example below, the flux data is for methane (“ch4”) and is covered by the same “EUROPE” domain as the footprint data described above.

from openghg.standardise import standardise_flux

standardise_flux(data_file_flux, species="ch4", domain="EUROPE", source="anthro", model="ukghg")

Once the raw flux data is standardised, we can retrieve it from the object store:

from openghg.retrieve import get_flux

flux_data = get_flux(species="ch4", domain="EUROPE", source="anthro")

Boundary conditions#

Boundary conditions describe the vertical curtains of a regional domain.

To standardise boundary conditions data we pass the following arguments to standardise_bc:

  • filepath: path to footprint file (this is the first positional argument)

  • species: a name for the associated species

  • domain: the name of the domain the vertical curtains surround

  • bc_input: a keyword descriptor for the boundary conditions inputs used

For the example below, the boundary conditions are for methane (“ch4”) at the edges of the “EUROPE” domain. They were created using the CAMS climatology product [5]

from openghg.standardise import standardise_bc

standardise_bc(data_file_bc, species="ch4", domain="EUROPE", bc_input="CAMS")

WARNING:openghg.store:This file has been uploaded previously with the filename : ch4_EUROPE_201607.nc - skipping.

User defined keywords: source and bc_input#

Unlike the domain and species inputs which have some pre-defined values, the source and bc_input keywords can be chosen by the user as a way to describe the flux and boundary condition inputs, alongside the additional optional values.

However, once a convention is chosen for a given source or bc_input, consistent keywords should be used to describe like data so this can be associated and distinguished correctly. Combinations of these keywords with the other identifiers (such as species and domain) should allow associated data in a timeseries to be identified.

See the Modifying and deleting data tutorial for how to update stored metadata if needed.

Reviewing what is in the object store#

To check the data and metadata already stored within an object store, use the search function from within the openghg.retrieve sub-module:

from openghg.retrieve import search

search_results = search()
search_results.results
data_type processed by processed on raw file used species domain source author processed source_format ... height high_spatial_resolution heights variables title date_created bc_input min_height max_height input_filename
0 emissions cv18710@bristol.ac.uk 2021-01-08 12:18:49.803837+00:00 /home/cv18710/work_shared/gridded_fluxes/ch4/u... ch4 europe anthro openghg cloud 2023-01-27 09:52:03.717769+00:00 openghg ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 footprints NaN NaN NaN NaN europe NaN NaN NaN NaN ... 100m False [500.0, 1500.0, 2500.0, 3500.0, 4500.0, 5500.0... [fp, temperature, pressure, wind_speed, wind_d... NaN NaN NaN NaN NaN NaN
2 boundary_conditions NaN NaN NaN ch4 europe NaN openghg cloud 2023-01-27 11:45:22.736279+00:00 NaN ... NaN NaN NaN NaN ecmwf cams ch4 volume mixing ratios at domain ... 2018-11-13 09:25:29.112138 cams 500.0 19500.0 ch4_europe_201607.nc

3 rows × 32 columns

Here we see the data types we’ve added, along with the keywords used when standarising the data.

To search for just one data type a specific search_* function can be used (or the data_type input of "emissions" or "boundary_conditions" can be passed to the search function). For example, for flux data:

from openghg.retrieve import search_flux

search_results_flux = search_flux()
search_results_flux.results
data_type processed by processed on raw file used species domain source author processed source_format start_date end_date max_longitude min_longitude max_latitude min_latitude high_time_resolution time_period uuid
0 emissions cv18710@bristol.ac.uk 2021-01-08 12:18:49.803837+00:00 /home/cv18710/work_shared/gridded_fluxes/ch4/u... ch4 europe anthro openghg cloud 2023-01-27 09:52:03.717769+00:00 openghg 2016-01-01 00:00:00+00:00 2016-12-31 23:59:59+00:00 39.38 -97.9 79.057 10.729 standard 1 year b16fefdf-c92d-4cc9-8aac-367bcb6b82fe

In this case the source value has been set to "anthro".

Note that the source and bc_input keywords can also include “-” to logically separate the descriptor e.g. “anthro-waste” but should not include other separators.

2. Input format#

For each of the data types seen above, there is an associated object from the openghg.store sub-module:

  • Footprints

  • Emissions

  • BoundaryConditions

  • EulerianModel [3]

To get information about the expected format [6] for a data type, use the .schema() method for the associated object.

Input format for flux data#

For instance, to see the schema for flux data:

from openghg.store import Emissions

Emissions.schema()

DataSchema(data_vars={'flux': ('time', 'lat', 'lon')}, dtypes={'lat': <class 'numpy.floating'>, 'lon': <class 'numpy.floating'>, 'time': <class 'numpy.datetime64'>, 'flux': <class 'numpy.floating'>}, dims=None)

This tells us that the netCDF input for “emissions” should contain:

  • Data variables:

    • “flux” data variable with dimensions of (“time”, “lat”, “lon”)

  • Data types:

    • “flux”, “lat”, “lon” variables/coordinates should be float type

    • “time” coordinate should be datetime64

Input format for footprints#

Similarly for Footprints, as described in the standardisations section, there are a few different input options available:

  • inert species (default - integrated footprint)

  • high spatial resolution (high_spatial_res flag)

  • high time resolution (high_time_res flag) (e.g. for carbon dioxide)

  • short-lived species (short_lifetime flag)

  • particle locations (particle_locations - default is True, expect to be included)

These can be shown by passing keywords to the .schema() method.

Default (inert) footprint format#

For example, if nothing is passed this returns the details for an integrated footprint for an inert species:

from openghg.store import Footprints
Footprints.schema()

DataSchema(data_vars={'fp': ('time', 'lat', 'lon'), 'particle_locations_n': ('time', 'lon', 'height'), 'particle_locations_e': ('time', 'lat', 'height'), 'particle_locations_s': ('time', 'lon', 'height'), 'particle_locations_w': ('time', 'lat', 'height')}, dtypes={'lat': <class 'numpy.floating'>, 'lon': <class 'numpy.floating'>, 'time': <class 'numpy.datetime64'>, 'fp': <class 'numpy.floating'>, 'height': <class 'numpy.floating'>, 'particle_locations_n': <class 'numpy.floating'>, 'particle_locations_e': <class 'numpy.floating'>, 'particle_locations_s': <class 'numpy.floating'>, 'particle_locations_w': <class 'numpy.floating'>}, dims=None)

This tells us that the default netCDF input for “footprints” should contain:

  • Data variables:

    • “fp” data variable with dimensions of (“time”, “lat”, “lon”)

    • “particle_locations_n”, “particle_locations_s” with dimensions of (“time”, “lon”, “height”)

    • “particle_locations_e”, “particle_locations_w” with dimensions of (“time”, “lat”, “height”)

  • Data types:

    • “fp”, “lat”, “lon”, “height” variables / coordinates should be float type

    • “particle_locations_n”, “particle_locations_e”, “particle_locations_s”, “particle_locations_w” variables should also be float type

    • “time” coordinate should be datetime64

The “fp” data variable describes the sensivity map within the regional domain. The “particle_locations_*” variables describe the senitivity map at each of the ordinal boundaries of the domain. Setting the particle_locations flag as False (True by default) would remove the requirement for these particle location boundary sensitivies to be included.

Other footprint formats#

For species with a short lifetime the input footprints require additional variables. This can be seen by passing the short_lifetime flag:

Footprints.schema(short_lifetime=True)

DataSchema(data_vars={'fp': ('time', 'lat', 'lon'), 'particle_locations_n': ('time', 'lon', 'height'), 'particle_locations_e': ('time', 'lat', 'height'), 'particle_locations_s': ('time', 'lon', 'height'), 'particle_locations_w': ('time', 'lat', 'height'), 'mean_age_particles_n': ('time', 'lon', 'height'), 'mean_age_particles_e': ('time', 'lat', 'height'), 'mean_age_particles_s': ('time', 'lon', 'height'), 'mean_age_particles_w': ('time', 'lat', 'height')}, dtypes={'lat': <class 'numpy.floating'>, 'lon': <class 'numpy.floating'>, 'time': <class 'numpy.datetime64'>, 'fp': <class 'numpy.floating'>, 'height': <class 'numpy.floating'>, 'particle_locations_n': <class 'numpy.floating'>, 'particle_locations_e': <class 'numpy.floating'>, 'particle_locations_s': <class 'numpy.floating'>, 'particle_locations_w': <class 'numpy.floating'>, 'mean_age_particles_n': <class 'numpy.floating'>, 'mean_age_particles_e': <class 'numpy.floating'>, 'mean_age_particles_s': <class 'numpy.floating'>, 'mean_age_particles_w': <class 'numpy.floating'>}, dims=None)

This tells us that, in addition to the “default” variables, for short-lived species there also must be:

  • Additional data variables:

    • “mean_age_particles_n”, “mean_age_particles_s” with dimensions of (“time”, “lon”, “height”)

    • “mean_age_particles_e”, “mean_age_particles_w” with dimensions of (“time”, “lat”, “height”)

  • Data types:

    • all new variables should be float type

Similiarly for the high_time_res and high_spatial_res flags to the Footprints.schema() method, these require additional variables within the input footprint files.

3. Cleanup#

If you’re finished with the data in this tutorial you can clean up the tutorial object store using the clear_tutorial_store function.

from openghg.tutorial import clear_tutorial_store

clear_tutorial_store()

Footnotes