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J. Hong's Lab
INVERSE-Korea (WMO IG3IS endorsed project)
INVERSE-Korea (WMO IG3IS endorsed project)
Monitoring sources and sink of greenhouse gases and estimating fossil fuel emission of greenhouse gases based on atmospheric measurements and data assimilation system from local to city and national scale.
Annual total emission of CO2 and CH4 in Korea (2019)
Overview
INVERSE-Korea is a leading scientific initiative endorsed by the World Meteorological Organization (WMO) as part of the Integrated Global Greenhouse Gas Information System (IG3IS). The project focuses on providing accurate, observation-based, and scientifically robust estimates of anthropogenic greenhouse gas (GHG) emissions, specifically carbon dioxide (CO₂), methane (CH₄), methane (SF6) across the Korean Peninsula.
Research Objectives
Development of top-down estimation framework for greenhouse gas (GHG) emission across scales ranging from 1 km to 25 km utilizing atmospheric inversion modeling system and high-precision GHG concentration data operated by the Korea Meteorological Administration
Monitoring of the local carbon budget in urban area using the eddy-covariance method
Tackling Complex Emission Landscapes
Tackling Complex Emission Landscapes
South Korea, characterized by dense urbanization, rapid industrial growth, and complex topography, is among the world's significant GHG emitters. High-density populations, substantial industrial activity, and energy-intensive economies, combined with seasonal variability driven by the East Asian monsoon, create intricate spatial and temporal emission patterns. Accurately quantifying these emissions is essential for effective climate policy and management.
Complementing Traditional Emission Inventories
Complementing Traditional Emission Inventories
Traditional bottom-up emission inventories, while valuable, face challenges in capturing rapid emission shifts and fine-scale spatial variations. INVERSE-Korea complements these inventories by employing advanced atmospheric inversion techniques that integrate real-time observations and sophisticated modeling to improve emission estimates at national and sub-national scales.
Greenhouse Gases Emission in Korea by Province (Inventory compiling)
Key Features and Capabilities
Key Features and Capabilities
1) High-Resolution and Multi-constituent Modeling of GHG Emission and Concentrations at National Scale
1) High-Resolution and Multi-constituent Modeling of GHG Emission and Concentrations at National Scale
Our system simultaneously assimilates meteorological and GHG data, producing hourly, 3D, high-resolution atmospheric analyses of CO₂ and CH₄ concentrations. This multi-species approach enhances consistency, reduces transport-related uncertainties, and improves source attribution.
2) Detailed Sectoral Insights
2) Detailed Sectoral Insights
INVERSE-Korea delivers detailed, sector-specific emissions insights, including:
Fossil fuel combustion and industrial processes
Agricultural emissions
Urban and metropolitan emissions patterns
Seasonal variations driven by the East Asian monsoon and energy demand cycles
3) Observation-Driven Accuracy
3) Observation-Driven Accuracy
Significant reductions in uncertainty are achieved around observational sites, with observed decreases of approximately 20–50% in emission estimate uncertainties, especially near major emission hotspots and well-instrumented areas.
Annual posterior fluxes and their differences relative to prior estimates in units of of kg m⁻² yr⁻¹.
Red (blue) shading on the map represents areas where posterior emissions increased (decreased) compared to the prior.
Sectoral emission comparison between a prior, posterior and Nationa Inventory Report (from BTU)
Observation: High-Precition GHG Concentration Measurements
Observation: High-Precition GHG Concentration Measurements
WMO GAW stations and Background colour indicates anthropogenic CO2 emission from the EDGAR.
Methodology: WRF-Chem/DART inversion system
Methodology: WRF-Chem/DART inversion system
INVERSE-Korea employs a state-of-the-art inversion system integrating the Weather Research and Forecasting model with Chemistry (WRF-Chem v4.4) and the Data Assimilation Research Testbed (DART) Ensemble Adjustment Kalman Filter. This framework:
Operates at a high spatial resolution (9 km), capturing fine-scale emission details across urban-industrial regions.
Assimilates continuous, high-precision in-situ CO₂ and CH₄ observations from WMO Global Atmosphere Watch (GAW) stations (AMY, GSN, ULD).
Incorporates meteorological data from the ERA5 reanalysis and boundary conditions from ECMWF CAMS EGG4.
Dynamically couples meteorology and GHG tracer transport, enabling physically consistent updates and accurate vertical transport representation.
Provides robust estimates of monthly and annual emissions, helping to identify biases and uncertainties in existing inventories.
Size of a circle indicates the model bias of 10 m wind speed. Model bias decreased significantly after our new method (b) compared to the default parameterizations (a).
Improvement of PBL structure in the atmospheric model
Improvement of PBL structure in the atmospheric model
Our system provides improved simulation of PBL structures by incorporating parameterization for the influences of complex terrain .
Lee, J. and J. Hong (2016) Implementation of space borne LiDAR-retrieved forest canopy height in the WRF model, Journal of Geophysical Research: Atmosphere, 121, https://doi:10.1002/2015JD024299.
Lee et al. (2020) Implementation of a roughness sublayer parameterization in the Weather Research and Forecasting model (WRF version 3.7.1) and its validation for regional climate simulations, Geoscientific Model Development, 13, 521-536.
Lee, et al. (2019) Ceilometer monitoring of boundary layer height in Seoul and its application to evaluate the dilution effect on air pollution, Bound.-Layer Meteorol., 171, 435-455.
Monitoring of Carbon Sources and Sink by the Eddy-Covariance Method (Neighborhood scale)
Monitoring of Carbon Sources and Sink by the Eddy-Covariance Method (Neighborhood scale)
Eddy covariance observations across contrasting urban landscapes in Seoul provide critical insights into the spatio-temporal variability of urban carbon fluxes, while also serving as a robust validation dataset to benchmark and enhance the performance of our inversion system.
Monthly variation of CO2 source and sink in urban forest
Monthly variation of CO2 source and sink in urban forest
Lee et al. (2021) Traces of urban forest in temperature and CO2 signals in the East Asian monsoon region, Atmospheric Chemistry and Physics, 21, 17833-17853, https://doi.org/10.5194/acp-21-17833-2021
Applications and Impact
Applications and Impact
1) Complementing the inventory emission data
1) Complementing the inventory emission data
Better understanding of the inventory emission data and reduce uncertainties in the inventory based emission data especially for non-CO2 greenhouse gases
2) Enhancing Urban Emission Understanding
2) Enhancing Urban Emission Understanding
We focus specifically on unraveling urban-scale emissions, integrating data from urban flux observations (eddy covariance measurements in Seoul metropolitan area) and future observations from tall towers such as the 555 m Lotte World Tower, significantly improving urban emission characterization.
3) Informing Policy and Climate Action
3) Informing Policy and Climate Action
Our data supports national Measurement, Reporting, and Verification (MRV) systems, providing independent validation of national and local emission inventories, essential for transparent climate reporting and policy-making.
Collaborative Network
Collaborative Network
INVERSE-Korea is led by the Ecosystem-Atmosphere Process Laboratory at Yonsei University, in close collaboration with:
National Institute of Meteorological Sciences (NIMS), Korea
Korea Environment Institute (KEI)
Emory University, Atlanta, USA
Pohang University of Science and Technology
Our multidisciplinary, international team ensures cutting-edge scientific rigor, extensive data integration, and practical policy relevance.
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