Welcome to CHEEREIO!

Welcome to the CHEEREIO website and documentation! The GEOS-Chem CHEmistry and Emissions REanalysis Interface with Observations (CHEEREIO) is a tool that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. Other scientists have assembled estimates of emissions of various pollutants from around the world, but our emissions estimates are very uncertain. CHEEREIO uses a model called GEOS-Chem to simulate what the atmosphere would look like if those emissions estimates were correct, and then compares those estimates to the real atmosphere as observed by satellites or equipment on the Earth’s surface. CHEEREIO uses the difference between the model simulation and the real world to update our maps of emissions.

More formally, CHEEREIO is a set of Python and shell scripts that support data assimilation and emissions inversions for arbitrary runs of the GEOS-Chem chemical transport model via an ensemble approach (i.e. without the model adjoint).

This site provides a manual for the installation, use, and modification of CHEEREIO for a variety of scientific applications. You can download CHEEREIO from the Github page and install by following the instructions on the Installing CHEEREIO page later in the documentation.

CHEEREIO follows four design principles:

  1. Easy to customize: Assimilate anything, in any GEOS-Chem configuration or simulation.

  2. Easy to maintain: Science automatically aligned with latest model version.

  3. Easy to deploy: One configuration file controls installation and settings

  4. Easy to link observations: Object-oriented observation operator implementation in Python allows the user to rapidly add new kinds of data with minimal programming required.

This manual assumes that you are familiar with setting up and running the GEOS-Chem chemical transport model. For more information and background, please see the GEOS-Chem Wiki. Although this manual offers an intuitive explanation of CHEEREIO’s data assimilation algorithm in the How CHEEREIO works section, you should probably consider the original algorithm paper Hunt. et. al., [2007] as a prerequisite to using CHEEREIO. Don’t worry, it’s quite readable!

I am more than happy to collaborate with you, however you choose to use CHEEREIO: just send an email to me at pendergrass [AT] g [DOT] harvard [DOT] edu. Questions of any kind are also more than welcome. This can be on anything from interpreting results to getting the ensemble installed on your machine. However, these questions along with any bugs should be reported by opening an issue on Github, as this will allow all users to see the solution.

The assimilation toolkit