Use Case 19

Problem Definition:

A climate scientist wishes to analyse potential correlations between Ozone and Cloud ECVs.

Required Toolbox Features:

Access to and ingestion of ESA CCI GHG data
Selection of required products/variables
Temporal and spatial subsetting
Generation of maps/animations depicting the evolution of GHG emissions

Ingest data and create the dataset



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import cate.ops as ops
from cate.core.ds import DATA_STORE_REGISTRY
from cate.util.monitor import ConsoleMonitor
from IPython.core.display import display, HTML

monitor = ConsoleMonitor()



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# As CCI GHG L3 level data is not found in the esa cci odp remote repository, we'll look at ozone
data_store = DATA_STORE_REGISTRY.get_data_store('esa_cci_odp')
oz_remote_sources = data_store.query('esacci.OZONE.mon.L3.NP.multi-sensor.multi-platform.MERGED.fv0002.r1')
oz_remote_sources



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# To reduce bandwith select the region of interest, time range and variables right away
europe = 'POLYGON((-12.05078125 73.54664369613808,33.65234375 73.54664369613808,33.65234375 35.65604583948963,-12.05078125 35.65604583948963,-12.05078125 73.54664369613808))'
oz_remote_sources[0].make_local(local_name='ozone-europe.mon.2007.2008',
                                time_range='2007-01-01, 2008-12-31',
                                var_names='O3_du_tot, O3_ndens',
                                region=europe,
                                monitor=monitor)



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oz = ops.open_dataset('local.ozone-europe.mon.2007.2008')



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oz



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display(HTML(ops.animate_map(oz, var='O3_du_tot', region=europe, true_range=True, monitor=monitor)))



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# Let's look at ozone density at different air pressures in April, 2017
display(HTML(ops.animate_map(oz,
                             var='O3_ndens',
                             monitor=monitor,
                             animate_dim='air_pressure',
                             region=europe,
                             true_range=True,
                             indexers={'time':'2007-04-01'})))