Use Case 9

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 Ozone and Cloud data (Atmosphere Mole Content of Ozone and Cloud Cover)
Geometric adjustments (coregistration)
Spatial (point, polygon) and temporal subsetting
Visualisation of time series
Correlation analysis, scatter-plot of correlation statistics, saving of image and correlation statistics on disk (format options)

Ingest data and create datasets



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from cate.core.ds import DATA_STORE_REGISTRY
import cate.ops as ops
from cate.util import ConsoleMonitor



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monitor = ConsoleMonitor()



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data_store = DATA_STORE_REGISTRY.get_data_store('esa_cci_odp')
local_store = DATA_STORE_REGISTRY.get_data_store('local')



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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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oz_remote_sources[0].make_local(local_name='ozone.mon',
                                time_range='2007-01-01, 2007-03-31',
                                monitor=monitor)



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cl_remote_sources = data_store.query('esacci.CLOUD.mon.L3C.CLD_PRODUCTS.multi-sensor.multi-platform.ATSR2-AATSR.2-0.r1')
cl_remote_sources



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cl_remote_sources[0].make_local(local_name='clouds1.mon',
                                time_range='2007-01-01, 2007-03-31',
                                monitor=monitor)



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local_store



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cc = ops.open_dataset('local.clouds1.mon')



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cc



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oz = ops.open_dataset('local.ozone.mon')



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oz

Filter datasets to select the desired variables



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cc_tot = ops.select_var(cc, 'cfc')
oz_tot = ops.select_var(oz, 'O3_du_tot')



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oz_tot

Plot the first time slice of the dataset



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%matplotlib inline
ops.plot_map(oz_tot, var='O3_du_tot', time='2007-01-01', file='/home/ccitbx/Desktop/fig1.png')



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ops.plot_map(cc_tot, var='cfc', time='2007-01-01', file='/home/ccitbx/Desktop/fig2.png')

Co-register datasets by resampling



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print(cc_tot['cfc'].shape)
print(oz_tot['O3_du_tot'].shape)



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cc_tot_res = ops.coregister(oz_tot, cc_tot)



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print(cc_tot_res['cfc'].shape)
print(oz_tot['O3_du_tot'].shape)



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ops.plot_map(cc_tot_res, var='cfc', time='2007-01', file='/home/ccitbx/Desktop/fig3.png')

Select the desired spatial region



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africa = '-20.0, -40.0, 60.0, 40.0'
# 'lon_min, lat_min, lon_max, lat_max'
cc_tot_africa = ops.subset_spatial(cc_tot_res, africa)
oz_tot_africa = ops.subset_spatial(oz_tot, africa)



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ops.plot_map(cc_tot_africa, var='cfc', time='2007-01-01', file='/home/ccitbx/Desktop/fig4.png')



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ops.plot_map(cc_tot_africa, var='cfc', time='2007-01-01', 
                 region=africa, file='/home/ccitbx/Desktop/fig5.png')

Select the desired temporal region



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cc_tot_janmar = ops.subset_temporal(cc_tot_africa, '2007-01-01, 2007-03-31')
oz_tot_janmar = ops.subset_temporal(oz_tot_africa, '2007-01-01, 2007-03-31')



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print(cc_tot_janmar.time)



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print(oz_tot_janmar.time)

Retrieve and plot timeseries



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oz_ts_point = ops.tseries_point(oz_tot_janmar, point='50, 50')
cc_ts_point = ops.tseries_point(cc_tot_janmar, point='50, 50')
oz_ts_mean = ops.tseries_mean(oz_tot_janmar, var='O3_du_tot')
cc_ts_mean = ops.tseries_mean(cc_tot_janmar, var='cfc')



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print(oz_ts_mean)



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print(cc_ts_mean)



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ops.plot(cc_ts_mean, 'cfc', file='/home/ccitbx/Desktop/fig6.png')



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ops.plot(oz_ts_mean, 'O3_du_tot', file='/home/ccitbx/Desktop/fig7.png')

Do correlation analysis



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correlation = ops.pearson_correlation_scalar(cc_ts_mean, oz_ts_mean, 'cfc', 'O3_du_tot')
correlation



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ops.write_text(correlation, file='/home/ccitbx/Desktop/corr.txt')



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correlation = ops.pearson_correlation(cc_tot_janmar, oz_tot_janmar, 'cfc', 'O3_du_tot')



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correlation



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ops.write_netcdf4(correlation, file='/home/ccitbx/Desktop/corr.nc')



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ops.plot_map(correlation, var='corr_coef', 
             region=africa, file='/home/ccitbx/Desktop/fig8.png')



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