Working with carbon dioxide

For carbon dioxide data, the natural diurnal cycle must be taken into account when comparing any expected measurements against our observations.

In order to compare our measurements to modelled data for carbon dioxide (\(\mathrm{CO_2}\)), we need high frequency inputs for both the footprint and flux files. For NAME footprints these should contain an additional “H_back” dimension to store hourly footprints overall an initial time period for each release time. For any natural flux data to compare against, this should have a frequency of less than 24 hours.

Operations around :math:`CO_2` and high resolution data may run slow at the moment, the plan is to profile this so we can optimise performance.

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

use_tutorial_store()

1. Loading data sources into the object store

For this tutorial we will again use a helper function from the openghg.tutorial submodule to add some data to your local object store. As we’re going to be comparing CO2 emissions with flux data here, we’ll add surface, flux and footprint data to the object store. Don’t worry if you get a message telling you the file has already been added, this might happen if you’ve been through one of the other tutorials previously.

from openghg.tutorial import populate_flux_data, populate_surface_data, populate_footprint_data

First we’ll add some surface measurment data

populate_surface_data()

Next we’ll add some flux data.

populate_flux_data()

And finally some footprint data

populate_footprint_data()

2. Creating a model scenario

To link together our observations to our ancillary data we can create a ModelScenario object, as shown in the previous tutorial 3_Comparing_with_emissions.ipynb, using suitable keywords to grab the data from the object store.

from openghg.analyse import ModelScenario

site = "TAC"
domain = "EUROPE"
species = "co2"
height = "185m"
source_natural = "natural"
start_date = "2017-07-01"
end_date = "2017-07-07"

scenario = ModelScenario(site=site,
                         inlet=height,
                         domain=domain,
                         species=species,
                         source=source_natural,
                         start_date=start_date,
                         end_date=end_date)

We can plot our observation timeseries using the ModelScenario.plot_timeseries() method as before:

scenario.plot_timeseries()

We can also check trace details of the extracted data by checking the available metadata. For instance for our footprint data we would expect this to have an associated species and for this to be labelled as “co2”:

footprint_metadata = scenario.footprint.metadata
footprint_species = footprint_metadata["species"]
print(f"Our linked footprint has an associated species of '{footprint_species}'")

3. Comparing data sources

Once the correct high frequency emissions and footprints have been linked for our carbon dioxide data, we can start to plot comparisons between the sources and our measurement data.

scenario.plot_comparison(baseline="percentile")

As in the previous tutorial, multiple fluxes can be linked to your ModelScenario object if required. This can include additional high frequency (<24 hourly) or low frequency flux data. In this case we have added monthly “fossil fuel” emissions:

source_fossil = "ff-edgar-bp"

scenario.add_flux(species=species,
                  source=source_fossil,
                  domain=domain)

fossil_flux = scenario.fluxes[source_fossil]
fossil_flux

If we plot the modelled measurement comparison, this will stack the natural and fossil fuel flux sources and combine with the footprint data in an appropriate way:

# scenario.plot_comparison(baseline="percentile", recalculate=True)

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

clear_tutorial_store()