In:
Geoscientific Model Development, Copernicus GmbH, Vol. 16, No. 16 ( 2023-08-24), p. 4793-4810
Abstract:
Abstract. We present a versatile, powerful, and user-friendly chemical data assimilation toolkit for simultaneously optimizing emissions and concentrations of
chemical species based on atmospheric observations from satellites or suborbital platforms. The CHemistry and Emissions REanalysis Interface with
Observations (CHEEREIO) exploits the GEOS-Chem chemical transport model and a localized ensemble transform Kalman filter algorithm (LETKF) to
determine the Bayesian optimal (posterior) emissions and/or concentrations of a set of species based on observations and prior information using an
easy-to-modify configuration file with minimal changes to the GEOS-Chem or LETKF code base. The LETKF algorithm readily allows for nonlinear
chemistry and produces flow-dependent posterior error covariances from the ensemble simulation spread. The object-oriented Python-based design of
CHEEREIO allows users to easily add new observation operators such as for satellites. CHEEREIO takes advantage of the Harmonized Emissions Component (HEMCO) modular structure of
input data management in GEOS-Chem to update emissions from the assimilation process independently from the GEOS-Chem code. It can seamlessly
support GEOS-Chem version updates and is adaptable to other chemical transport models with similar modular input data structure. A post-processing
suite combines ensemble output into consolidated NetCDF files and supports a wide variety of diagnostic data and visualizations. We demonstrate
CHEEREIO's capabilities with an out-of-the-box application, assimilating global methane emissions and concentrations at weekly temporal resolution
and 2∘ × 2.5∘ spatial resolution for 2019 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. CHEEREIO achieves a 50-fold improvement in
computational performance compared to the equivalent analytical inversion of TROPOMI observations.
Type of Medium:
Online Resource
ISSN:
1991-9603
DOI:
10.5194/gmd-16-4793-2023
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2023
detail.hit.zdb_id:
2456725-5