| 31 Dec 2025
Near real-time inversion of high-resolution anthropogenic carbon emissions in the Pearl River Delta region based on the four-dimensional local ensemble transform Kalman filter
Abstract. For climate mitigation, it is necessary to address the dynamic updating and assessment of CO2 emissions at regional scales. This study developed a kilometer-scale carbon assimilation system (the Guangzhou Regional Atmospheric Composition and Environment Forecasting System–Greenhouse Gas–Data Assimilation, GRACES-GHG-DA) by coupling the weather research and forecasting–greenhouse gas (WRF-GHG) model with the four-dimensional local ensemble transform Kalman filter (4D-LETKF). GRACES-GHG-DA constructs a near-real-time 4-km anthropogenic emission inventory, constrained by simulated CO2 observation data from seven high-precision greenhouse gas monitoring stations in the Pearl River Delta (PRD) region, to analyze spatiotemporal emission distributions and their relationship with ambient CO2 concentrations. The results indicate that: (1) GRACES-GHG-DA accurately downscales CO2 concentrations from a resolution of 36 to 4 km, with the finer resolution better capturing meso- and micro-scale variations (hourly and monthly mean biases of −0.77 and −0.51 ppm, respectively). (2) In 2022, the inverted annual anthropogenic CO2 flux in core PRD areas exceeded 7500 g C m−2 a−1, contrasting with values below 1000 g C m−2 a−1 in peripheral regions. Compared to the inversion estimates, statistical inventories (EDGAR, ODIAC, GCP, and MEIC) underestimated total emissions by 14.71% on average. (3) Seasonal anthropogenic emissions were 24.03, 29.86, 30.61, and 27.26 Tg C for spring, summer, autumn, and winter, respectively, showing a unimodal diurnal pattern largely influenced by fossil-fuel electricity generation.(4) Anthropogenic emissions are not the dominant factor governing atmospheric CO2 concentrations in the PRD; vegetation carbon uptake/release, boundary layer evolution, and regional transport also play critical roles.
This paper presents a sophisticated approach to carbon cycle science by developing the GRACES-GHG-DA system. It bridges the gap between mesoscale meteorological modeling (WRF-GHG) and advanced data assimilation (4D-LETKF) to achieve kilometer-scale, hourly updates for anthropogenic CO₂ emissions in one of the world's most complex urban clusters: the Pearl River Delta (PRD). The novelty of this research lies in its spatiotemporal granularity and the application of 4D-LETKF for near real-time inversion. While global and regional models often struggle with the "representation error" of urban environments, this study successfully downscales to a 4-km resolution, allowing for the identification of meso- and micro-scale variations that coarser models (36 km) overlook.
The use of 4D-LETKF represents a meaningful advancement over more commonly applied 3D EnKF or EnSRF approaches, especially in the assimilation of asynchronous hourly observations. However, the manuscript would benefit from a clearer articulation of its novelty relative to previous WRF-GHG-based regional inversion studies. While the technical improvements are evident from the results, explicitly highlighting how the near–real-time, hourly-updated anthropogenic emission inversion and the kilometer-scale resolution extend beyond prior work would strengthen the paper’s positioning.
The comparison between the top-down inversion and bottom-up inventories is one of the manuscript’s strengths. The spatial patterns of disagreement are well analyzed, particularly in the PRD urban core. However, the discussion could be deepened by offering more interpretation of why certain inventories, especially MEIC, show large underestimations in core cities. Even a qualitative sectoral explanation would help contextualize these differences and enhance the relevance for inventory developers and policymakers.
To strengthen the discussion on urban emission heterogeneity and the challenges of capturing mobile source contributions (specifically mentioned in Section 3.3 regarding traffic emissions), I strongly suggest that the authors cite the following paper:
Comprehensive spatiotemporal analysis of long-term mobile monitoring for traffic-related particles in a complex urban environment. > DOI: 10.1016/j.apr.2025.102870