Overview of Capabilities

This page outlines the capabilities of GCPy with links to detailed function documentation.

Spatial Plotting

One hallmark of GCPy is easy-to-use spatial plotting of GEOS-Chem data. Available plotting falls into two layouts: single panel (one map of one variable from a dataset) and six panel (six maps comparing a variable between two datasets). The maps in these plots can display data at a single vertical level of your input dataset or in a zonal mean for all layers of the atmosphere.

Single Panel Plots

Single panel plots are generated through the plot.single_panel() function. plot.single_panel() uses Matplotlib and Cartopy plotting capabilities while handling certain behind the scenes operations that are necessary for plotting GEOS-Chem data, particularly for cubed-sphere and/or zonal mean data.

import xarray as xr
import gcpy.plot as gcplot
import matplotlib.pyplot as plt
ds = xr.open_dataset('GEOSChem.Restart.20160701_0000z.nc4')
#plot surface Ozone over the North Pacific
gcplot.single_panel(ds['SpeciesRst_O3'].isel(lev=0), title='Surface Ozone over the North Pacific', extent=[80, -90, -10, 60])
plt.show()
_images/single_panel_single_level.png 
#plot global zonal mean of Ozone
gcplot.single_panel(ds['SpeciesRst_O3'], plot_type='zonal_mean', title='Global Zonal Mean of Ozone')
plt.show()
_images/single_panel_zonal_mean.png

Click here for an example single panel plotting script. Click here for detailed documentation for single_panel().

Six Panel Comparison Plots

Six panel plots are used to compare results across two different model runs. Single level and zonal mean plotting options are both available. The two model runs do not need to be the same resolution or even the same grid type (GEOS-Chem Classic and GCHP output can be mixed at will).

import xarray as xr
import gcpy.plot as gcplot
import matplotlib.pyplot as plt
gcc_ds = xr.open_dataset('GEOSChem.SpeciesConc.20160701_0000z.nc4')
gchp_ds = xr.open_dataset('GCHP.SpeciesConc.20160716_1200z.nc4')
#Plot comparison of surface ozone over the North Pacific
gcplot.compare_single_level(gcc_ds, 'GEOS-Chem Classic', gchp_ds, 'GCHP', varlist=['SpeciesConc_O3'], extra_title_txt='Surface')
plt.show()
_images/six_panel_single_level.png 
#Plot comparison of global zonal mean ozone
gcplot.compare_zonal_mean(gcc_ds, 'GEOS-Chem Classic', gchp_ds, 'GCHP', varlist=['SpeciesConc_O3'])
plt.show()
_images/six_panel_zonal_mean.png

Click here for an example six panel plotting script. Click here for complete documentation for compare_single_level() and compare_zonal_mean().

Comprehensive Benchmark Plotting

The GEOS-Chem Support Team uses comprehensive plotting functions from benchmark.py to generate full plots of benchmark diagnostics. Functions like benchmark.make_benchmark_conc_plots by default create plots for every variable in a given collection (e.g. SpeciesConc) at multiple vertical levels (surface, 500hPa, zonal mean) and divide plots into separate folders based on category (e.g. Chlorine, Aerosols). The GCST uses full benchmark plotting / table scripts similar to this example to produce plots and tables for official model benchmarks. Full documentation for the benchmark plotting functions can be found here.

Table Creation

GCPy has several dedicated functions for tabling GEOS-Chem output data in text file format. These functions and their outputs are primarily used for model benchmarking purposes.

Budget Tables

Currently, budget tables can be created for “operations” (table shows change in mass after each category of model operation, as contained in the GEOS-Chem Budget diagnostics) or in overall averages for different aerosols or the Transport Tracers simulation.

Operations budget tables are created using the benchmark.make_benchmark_operations_budget function and appear as follows:

_images/budget_table.png

Full documentation for operations budget table creation can be found here.

Mass Tables

The benchmark.make_benchmark_mass_tables function uses species concentrations and info from meteorology files to generate the total mass of species in certain segments of the atmosphere (currently global or only the troposphere). An example table is shown below:

_images/mass_table.png

Full documentation for mass table creation can be found here.

Emissions Tables

The benchmark.make_benchmark_emis_tables function creates tables of total emissions categorized by species or by inventory. Examples of both emissions table types are shown below:

_images/emissions_totals.png _images/inventory_totals.png

Full documentation for emissions table creation can be found here.

Regridding

General Regridding Rules

GCPy supports regridding between all horizontal GEOS-Chem grid types, including latitude/longitude grids (the grid format of GEOS-Chem Classic), standard cubed-sphere (the standard grid format of GCHP), and stretched-grid (an optional grid format in GCHP). GCPy contains several horizontal regridding functions built off of xESMF. GCPy automatically handles most regridding needs when plotting GEOS-Chem data.

gcpy.file_regrid allows you to regrid NetCDF files between different grid types / resolutions and can be called from the command line or as a function.

The 72-level and 47-level vertical grids are pre-defined in GCPy. Other vertical grids can also be defined if you provide the A and B coefficients of the hybrid vertical grid.

When plotting data of differing grid types or horizontal resolutions using compare_single_level or compare_zonal_mean, you can specify a comparison resolution using the cmpres argument. This resolution will be used for the difference panels in each plot (the bottom four panels rather than the top two raw data panels). If you do not specify a comparison resolution, GCPy will automatically choose one.

For more extensive regridding information, visit the detailed regridding documentation.