GEOS-Chem v11-02 can archive diagnostics to disk in one of two formats:
Binary punch file format. These diagnostics will be turned on by default, in order to provide backwards compatibility with older versions. For a complete listing of available diagnostics, please see our List of diagnostics archived to bpch format wiki page.
COARDS-compliant netCDF file format. These diagnostics, which use the de-facto standard for atmospheric and climate model output, will eventually replace the binary punch diagnostics. For a complete listing of all diagnostics in netCDF format, please see our List of diagnostics archived to netCDF format wiki page.
NOTE: At this time not all diagnostics (such as the satellite timeseries diagnostics ND51) have been implemented in netCDF format. These diagnostics will still be available in bpch format in GEOS-Chem v11-02.
To activate the netCDF diagnostics, compile your code with e.g.
make -j8 NC_DIAG=y
For many years, GEOS-Chem users have relied on the GAMAP package to visualize and analyze GEOS-Chem diagnostic output. While GAMAP is still used today (e.g. to create plots from each GEOS-Chem 1-month and 1-year benchmark), we have started to migrate away from GAMAP for the following reasons:
GAMAP requires the Interactive Data Language (IDL), which is proprietary software. The cost of an IDL site license is now prohibitive for many GEOS-Chem user groups.
GAMAP was developed many years ago for the binary punch file format. Its capacities for netCDF file I/O are less developed.
GAMAP is not able to read or regrid data on cubed-sphere grids. It is therefore incompatible with the diagnostic output produced by GEOS-Chem with the High Performance option (aka GCHP).
For these reasons, we have begun to use open-source data analysis packages written in the Python language. These packages, which are free to use, are mature and have well-established user communities. Being open-source, these Python packages can be implemented on cloud-computing environments (such as the Amazon EC2 cloud) very easily.
The most popular of these packages are:
We recommend that you take the excellent interactive tutorial created by Jiawei Zhuang, which will walk you through the steps of analyzing and plotting GEOS-Chem diagnostic data in Python.