the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Oct 2025
Regional CO2 and CH4 inversion system using WRF-Chem (v4.4)/DART (v9.8.0) and continuous high-precision observations over the Korean Peninsula
Abstract. We develop a high-resolution dual-species greenhouse gas (GHG) top-down inversion framework by integrating the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem v4.4) and the Data Assimilation Research Testbed (DART v9.8.0). This framework jointly performs the assimilation of near-surface CO2 and CH4 concentrations alongside standard meteorological data across the Korean Peninsula. To improve the simulation of GHG turbulent dispersion in the atmospheric boundary layer over complex terrain, we incorporate surface heterogeneity parameterizations (roughness sublayer and canopy height) into the model physics in the inversion system. The system assimilates continuous in situ observations from three World Meteorological Organization/Global Atmosphere Watch (WMO/GAW) stations and produces dynamically consistent updates of CO2 and CH4 emissions. Prior flux estimates include anthropogenic emissions (EDGAR v8.0), biogenic exchanges (the region-optimized VPRM), biomass burning (FINN v2.5 data), and oceanic CO2 exchanges (SeaFlux data). In a 2020 case study, the top-down estimates improve the agreement with ground observations, reducing root-mean-square errors by 30–60 % and correcting bias error of 1–10 ppm and 30–60 ppb for surface CO2 and CH4 concentrations at the high-precision surface observatory respectively. Independent aircraft profiles suggest consistency between the boundary and prior CH4 emissions. The posterior anthropogenic emissions show decreases over the Seoul Metropolitan Area and western coastal sources for CO2 and increases over agricultural areas for CH4, indicating potential areas that need to refine the global emission inventories. The posterior annual national total emissions for CO2 and CH4 fall within the ranges reported in the Republic of Korea’s Biennial Transparency Report of Korea). This case study demonstrates the utility of an observation-constrained top-down framework in supporting the Measurement-Monitoring-Reporting-Verification (MMRV) framework for national and sub-national assessments of GHG emissions and provide a scalable path toward multi-platform (satellite, aircraft, shipborne) integration.
Competing interests: One of coauthors is a topical editor of Geoscientific Model Development.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
(1942 KB) - Metadata XML
-
Supplement
(1718 KB) - BibTeX
- EndNote
Status: open (until 05 Jan 2026)
- RC1: 'Comment on egusphere-2025-4938', Anonymous Referee #1, 01 Dec 2025 reply
-
RC2: 'Comment on egusphere-2025-4938', Anonymous Referee #2, 12 Dec 2025
reply
The manuscript presents a dual state inversion framework developed at Yonsei University to estimate CO2 and CH4 emissions over Korea. The inversion is based on a coupling of DART and WRF-Chem, two widely used (and published) tools, so there is not a lot of technical novelty, but the setup itself is rather uncommon (at least in the GHG inversion field).
I guess the aim of publishing this in GMD is to have a published setup for referencing in future studies? This is totally fine, but then the authors need to do a little more than just presenting their coupling and one use case. They should clarify the interest (and drawbacks) of their dual-state setup, and justify the settings they used (uncertainties, correlations, resolution, choice of variables to be optimized, data selection, etc.). This should be demonstrated (e.g. through sensitivity experiments), not just stated. Currently the paper just shows the results of one single experiment, without really delving into what makes it a good experimental setup. That's a bit light. I also don't think that the comparison with the CAMS global GHG reanalysis is very insightful.
Overall though, what is written is well written, the topic is important and the setup is interesting, so the paper should eventually get published, once these comments have been addressed
Specific comments:
- The method description isn't sufficiently clear around what exactly is solved for by the data assimilation system. It seems to adjust the emissions (so it's an inversion?), but, according to Section 2.1, it adjusts meteorological and tracer fields (so it's an atmospheric state data assimilation?). Having some mathematical description of what happens (i.e. some equations) would help a lot. I assume it's an inversion, because emission adjustments are presented. I wonder if the tracer fields are directly modified by the assimilation as well?
- The dual-tracer, dual-state setup is rather uncommon: what benefit does it provide upon a more classical inversion, solving only for emissions? What are the costs? This should be demonstrated, not just stated (i.e. through sensitivity experiments, at least). Even for more classical aspects (choices of uncertainties, correlations, data filtering, treatment of the boundary condition), at least some idea of what are the most impactful settings (within that particular inversion framework) should be provided.
- I think there is some confusion between high resolution transport and high resolution inversion. At line 369, you claim that: "the 0.75° x 0.75° resolution of EGG4 is not enough to resolve the sharp urban-industrial emission heterogeneity and fine scale sources and sinks across Korea. They motivate the use of high resolution regional inversion". It's quite obvious that fine scale processes will be better represented in a higher resolution model. But that doesn't mean that you "resolve" (as in, robustly constrain) the emissions at that resolution. You have three observation points, distant from each other by a few hundreds of kms, so you can't resolve emissions pattern at much finer scale. Finer-scale components of the posterior are mainly just carried forward from the prior. So at line 375, the statement that "Local increments of anthropogenic CO$_2$ and CH$_4$ emissions (...) are evident" is not really true: the increments appear local only in absolute magnitude. If they were shown as proportion to the prior uncertainties, they would appear as smooth, wide scale patterns. This is in fact very visible in Figure 10.
- The comparison with the surface concentrations in EGG4 ... just shows that EGG4 had a lower resolution, nothing more (so something that was known beforehand). In particular, it says nothing about the emissions, which is not a variable that EGG4 solves for, but which are your real target. Besides this comparison, the presentation of the results focuses mainly on the emissions, except for Figure 6 which shows the uncertainty reduction in the concentration space. I am however wondering how much the direct adjustment of the tracers (if it is done) compares with the adjustment of the emissions, and with that of the boundary condition, in contributing to improving the fit to observations. And does it make sense to adjust all of them? This needs to be better presented and justified.
Citation: https://doi.org/10.5194/egusphere-2025-4938-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 231 | 67 | 14 | 312 | 23 | 13 | 12 |
- HTML: 231
- PDF: 67
- XML: 14
- Total: 312
- Supplement: 23
- BibTeX: 13
- EndNote: 12
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This manuscript combines WRF-Chem with the DART system to perform a top-down inversion of CO2 and CH4 using atmospheric observations, constraining concentrations and fluxes at a 9-km resolution. Given the growing need to improve national-scale greenhouse gas estimates within the MMRV framework, the topic is timely and of scientific relevance. However, for the reasons outlined below, I believe the manuscript in its current form is not yet suitable for publication in Geoscientific Model Development.
1. Suitability for GMD
The manuscript does not clearly articulate the novelty of the WRF-Chem/DART modeling framework or how it differs from existing inversion systems. Details necessary for reproducibility—an essential requirement for GMD—are limited. The numerical settings and methodological choices require clearer justification to meet the journal’s standards for model development papers.
2. Insufficient OSSE-based accuracy evaluation
Although the study focuses on developing an inversion framework, it lacks prior validation of its performance. An OSSE experiment using synthetic observations would allow the authors to quantitatively assess the accuracy and robustness of the system before applying it to real observations.
3. Limited description of DART and EAKF methodology
The manuscript provides only a brief description of the DART system and the Ensemble Adjustment Kalman Filter. Further explanation of the theoretical basis, implementation steps, and internal processing is needed to help readers understand how the assimilation framework functions.
4. Unclear scope of assimilated variables
Lines 133–134 suggest that both greenhouse gases and meteorological fields are assimilated, but the specific meteorological variables (e.g., temperature, wind components, humidity, surface variables) are not identified. Clarifying the full set of assimilated variables is essential.
5. Insufficient justification for uncertainty settings
The rationale for applying 5% uncertainty to initial/lateral boundary conditions and 30% to anthropogenic emissions is not provided. Given that CO2 and CH4 exhibit different source characteristics and variability, using identical uncertainty ratios is difficult to justify. Supporting evidence from previous studies or sensitivity analyses is needed. Moreover, observational and model uncertainties are not adequately discussed.
6. Lack of meteorological field evaluation
The manuscript does not evaluate the WRF-Chem meteorological fields (e.g., wind speed and direction), which directly influence transport and mixing. A comparison of modeled meteorology with observational data is necessary to quantify biases and assess their effects on the inversion.
7. Unclear treatment of initial and boundary conditions & need for sensitivity analysis
It is not specified whether WRF-Chem/DART updates initial and boundary conditions in the single-domain configuration. And additional justification is needed to demonstrate that a single-domain setup is sufficient for obtaining reliable posterior estimates. Given that regional inversions are highly sensitive to boundary conditions—and considering the known negative CH4 bias in EGG4—additional sensitivity tests using alternative boundary conditions would help evaluate the robustness of the posterior estimates.
8. Limited ground observation network & questionable ability to constrain emissions
The number and spatial distribution of ground sites appear insufficient to constrain national-scale emissions. The sites used are located near the national borders and represent background conditions, making it uncertain whether they provide meaningful constraints for South Korea’s total emissions. Figures 6 and 7 also indicate limited uncertainty reduction in several high-uncertainty regions. Inclusion of satellite observations would likely enhance spatial representativeness.
9. Filtering of WMO/GAW background-site observations
WMO/GAW stations typically apply data filtering to remove local source influences. It is unclear whether filtered or unfiltered measurements were used in this study, and this point should be clarified. If filtered data were used, the reduced local variability may limit the ability of the assimilation system to effectively constrain regional emissions.
10. Missing wetland emissions for CH4 and lack of chemistry description
Wetland CH4 emissions—one of the dominant natural CH4 sources—are not included, and the potential impact of this omission is not discussed. In addition, CH4 oxidation by OH radicals should be addressed, but the manuscript does not describe how WRF-Chem handles this chemistry.
11. Insufficient discussion of results
The evaluation of top-down estimates lacks depth. For example, the reasons behind the strong improvements at AMY and GSN, the pronounced summer behavior, and the underestimation of CH4 in vertical profiles are not explained. In particular, the manuscript mentions "underlying mechanisms for the maritime CH4" but does not clarify what those mechanisms are or how they influence the vertical structure. Similar gaps appear in the CO2 increment patterns across different regions; for instance, negative increments in SMA and MWI and positive increments in SCI and SEI are reported without any discussion of potential drivers. Without addressing these points, it is difficult to assess the reliability of the results.
12. Limitations of bottom-up inventory comparisons
When comparing total anthropogenic emissions with bottom-up inventories, sectoral coverage should be consistent across datasets. If they are not harmonized, the manuscript must explain the sectoral differences for proper interpretation. The discussion of why posterior emissions align with ROK-BTR but differ substantially from ODIAC is also insufficient. Lines 399–400 are unclear and require revision.
13. Additional issues
Lack of contextual clarity for EGG4 CO2 and CH4 errors: The discussion of EGG4 biases in Lines 185–190 does not clearly connect with the surrounding text. The purpose and relevance of this information within the flow of the manuscript are unclear.
Input data preprocessing: The manuscript does not describe how datasets with differing spatial and temporal resolutions were regridded and harmonized.
VPRM parameters: The text states that parameters were adopted but does not specify which ones or why.
Figure 7: Prior flux exists over the ocean, yet its uncertainty is zero; the rationale should be explained. The reduction of uncertainty over North Korea and China also requires discussion.