Comparing observations to emissions#

In addition to observation files, ancillary data can also be added to an openghg object store which can be used to perform analysis.

At the moment, the accepted files include: - Footprints - regional outputs from an LPDM model (e.g. NAME) - Emissions/Flux - estimates of species emissions within a region - Boundary conditions - vertical curtains at the boundary of a regional domain - Global CTM output (e.g. GEOSChem)

These inputs must adhere to an expected format and are expected to minimally contain a fixed set of inputs.

At the moment, the expected format for these files is created through standard methods from within the ACRG repository.

NOTE: Plots created within this tutorial may not show up on the online documentation version of this notebook.

0. Using the tutorial object store#

To avoid adding the example data we use in this tutorial to your normal object store, we need to tell OpenGHG to use a separate sandboxed object store that we’ll call the tutorial store. To do this we use the use_tutorial_store function from openghg.tutorial. This sets the OPENGHG_TUT_STORE environment variable for this session and won’t affect your use of OpenGHG outside of this tutorial.

from openghg.tutorial import use_tutorial_store


1. Loading data sources into the object store#

This tutorial will create a temporary object store for the duration of this tutorial.

See Adding_new_data/Adding_observation_data.ipynb for more details and advice on how to create a more permanent object store. Once a permanent object store is set up, these steps would only need to be performed once. Any added data can then be retrieved using searches.

For this, we will add observation, footprint and flux data to the object store. This data relates to Tacolneston (TAC) site within the DECC network and the area around Europe (EUROPE domain). Here we’ll use some helper functions fro the openghg.tutorial submodule.

from openghg.tutorial import populate_surface_data, populate_footprint_inert, populate_flux_ch4, populate_bc_ch4

2. Creating a model scenario#

With this ancillary data, we can start to make comparisons between model data, such as bottom-up inventories, and our observations. This analysis is based around a ModelScenario object which can be created to link together observation, footprint, emissions data and boundary conditions data.

Above we loaded observation data from the Tacolneston site into the object store. We also added an associated footprint (sensitivity map) and anthropogenic emissions maps both for a domain defined over Europe.

To access and link this data we can set up our ModelScenario instance using a similiar set of keywords. In this case we have also limited ourselves to a date range:

from openghg.analyse import ModelScenario

source_waste = "waste"
start_date = "2016-07-01"
end_date = "2016-08-01"

scenario = ModelScenario(site=site,

Using these keywords, this will search the object store and attempt to collect and attach observation, footprint, flux and boundary conditions data. This collected data will be attached to your created ModelScenario. For the observations this will be stored as the ModelScenario.obs attribute. This will be an ObsData object which contains metadata and data for your observations:


To access the undelying xarray Dataset containing the observation data use

ds =

The ModelScenario.footprint attribute contains the linked FootprintData (again, use .data to extract xarray Dataset):


And the ModelScenario.fluxes attribute can be used to access the FluxData. Note that for ModelScenario.fluxes this can contain multiple flux sources and so this is stored as a dictionary linked to the source name:


Finally, this will also search and attempt to add boundary conditions. The ModelScenario.bc attribute can be used to access the BoundaryConditionsData if present.


An interactive plot for the linked observation data can be plotted using the ModelScenario.plot_timeseries() method:


You can also set up your own searches and add this data directly.

from openghg.retrieve import get_obs_surface, get_footprint, get_flux, get_bc

# Extract obs results from object store
obs_results = get_obs_surface(site=site,

# Extract footprint results from object store
footprint_results = get_footprint(site=site,

# Extract flux results from object store
flux_results = get_flux(species=species,

# Extract specific boundary conditions from the object store
bc_results = get_bc(species=species,
scenario_direct = ModelScenario(obs=obs_results, footprint=footprint_results, flux=flux_results, bc=bc_results)

You can create your own input objects directly and add these in the same way. This allows you to bypass the object store for experimental examples. At the moment these inputs need to be ``ObsData``, ``FootprintData``, ``FluxData`` or ``BoundaryConditionsData`` objects (can be created using classes from openghg.dataobjects) but simpler inputs will be made available.

One benefit of this interface is to reduce searching the database if the same data needs to be used for multiple different scenarios.

3. Comparing data sources#

Once your ModelScenario has been created you can then start to use the linked data to compare outputs. For example we may want to calculate modelled observations at our site based on our linked footprint and emissions data:

modelled_observations = scenario.calc_modelled_obs()

This could then be plotted directly using the xarray plotting methods:

modelled_observations.plot()  # Can plot using xarray plotting methods

The modelled baseline, based on the linked boundary conditions, can also be calculated in a similar way:

modelled_baseline = scenario.calc_modelled_baseline()
modelled_baseline.plot()  # Can plot using xarray plotting methods

To compare the these modelled observations to the observations themselves, the ModelScenario.plot_comparison() method can be used. This will stack the modelled observations and the modelled baseline by default to allow comparison:


The ModelScenario.footprints_data_merge() method can also be used to created a combined output, with all aligned data stored directly within an xarray.Dataset:

combined_dataset = scenario.footprints_data_merge()

When the same calculation is being performed for multiple methods, the last calculation is cached to allow the outputs to be produced more efficiently. This can be disabled for large datasets by using cache=False.

For a ModelScenario object, different analyses can be performed on this linked data. For example if a daily average for the modelled observations was required, we could calculate this by setting our resample_to input to "1D" (matching available pandas time aliases):

modelled_observations_daily = scenario.calc_modelled_obs(resample_to="1D")

To allow comparisons with multiple flux sources, more than one flux source can be linked to your ModelScenario. This can be either be done upon creation or can be added using the add_flux() method. When calculating modelled observations, these flux sources will be aligned in time and stacked to create a total output:

scenario.add_flux(species=species, domain=domain, source="energyprod")

Output for individual sources can also be created by specifying the sources as an input:

# Included recalculate option to ensure this is updated from cached data.
modelled_obs_energyprod = scenario.calc_modelled_obs(sources="energyprod", recalculate=True)

Plotting functions to be added for 2D / 3D data

4. Cleanup#

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

from openghg.tutorial import clear_tutorial_store