Title:	Inverse modeling of greenhouse gases
DNr:	NAISS 2025/22-1417
Project Type:	NAISS Small Compute
Principal Investigator:	Hans Chen <hans.chen@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2025-11-01 – 2026-11-01
Classification:	10508
Keywords:

Abstract

This project advances the state-of-the-art in regional greenhouse gas (GHG) flux estimation through innovative inverse modeling techniques. By leveraging high-performance computing, we will develop and implement sophisticated data assimilation methods that integrate prior GHG flux estimates, atmospheric transport modeling, in situ measurements, and cutting-edge satellite observations. At the core of this research is the TRACE Regional Atmosphere–Carbon Ensemble (TRACE) system, a data assimilation framework developed by the PI (Hans Chen). TRACE specifically addresses the challenge of constraining regional carbon dioxide fluxes at high spatial and temporal resolutions. Our primary innovation lies in developing novel methodologies to assimilate high-resolution satellite measurements. This represents a significant advancement over conventional atmospheric inversion techniques, which struggle with satellite data due to fundamental assumptions in their formulations. TRACE consists of two major components: atmospheric transport modeling using the Weather Research and Forecasting Model (WRF), a full-physics mesoscale atmospheric model, and an advanced data assimilation system based on the PSU WRF EnKF framework. The data assimilation system employs ensemble-based algorithms to propagate probability density functions of the system’s state through time. The current WRF setup operates at a 27 km horizontal resolution, which the option of a higher 9 km resolution to better leverage the detail in high-resolution satellite imagery. In this project, we will focus on three tasks: 1. Test atmospheric inversions by assimilating synthetic CO2M satellite observations over Western Europe. 2. Quantify the added value of using hourly emissions in atmospheric inversions versus annual-average emissions. 3. Assess the impact of systematic and random errors in satellite observations on inverse flux estimates.