Hyun Mee Kim

The effect of assimilated surface CO₂ concentration observations on the analysis and forecast errors of model CO₂ concentrations was evaluated using the data assimilation (DA)-forecast system combining the Weather Research and Forecasting model coupled with Chemistry and modified Data Assimilation Research Testbed. To investigate the impact of surface CO₂ observations, four observing system simulation experiments (OSSEs) were conducted in July 2019. The impact of CO₂ observations on the CO₂ concentration analysis was calculated using self-sensitivity. Average self-sensitivity of four OSSEs was 21.0 %, implying that surface CO₂ observations provided average 21.0 % information to the CO₂ concentration analysis. Self-sensitivity was highly correlated with the root mean square error of the analysis and hourly variability in the surface CO₂ observations at each observation site. The impact of CO₂ observations on reducing forecast errors, calculated using nonlinear forecast error reduction (NER), showed that NER with DA was reduced by average 17.0 % compared with that without DA. Linear forecast error reduction, calculated using the ensemble forecast sensitivity to observation (EFSO) impact, showed that the EFSO impact was greater at surface CO₂ observation sites with higher self-sensitivity and active vegetation types. Average fraction of beneficial observations for all experiments was 68.9 % (66.3 %) for 6 h (12 h) forecasts, implying that more than half of the assimilated CO₂ observations contributed to reducing forecast errors. The assessment of observation impact on the CO₂ concentration analysis and forecast can be useful for monitoring and estimating atmospheric CO₂ concentrations, optimizing surface CO₂ fluxes, and designing atmospheric CO₂ observation networks.

The effect of assimilated surface CO₂ concentration observations on the analysis and forecast errors of model CO₂ concentrations was evaluated using the data assimilation (DA)-forecast system combining the Weather Research and Forecasting model coupled with Chemistry and modified Data Assimilation Research Testbed. To investigate the impact of surface CO₂ observations, four observing system simulation experiments (OSSEs) were conducted in July 2019. The impact of CO₂ observations on the CO₂ concentration analysis was calculated using self-sensitivity. Average self-sensitivity of four OSSEs was 21.0 %, implying that surface CO₂ observations provided average 21.0 % information to the CO₂ concentration analysis. Self-sensitivity was highly correlated with the root mean square error of the analysis and hourly variability in the surface CO₂ observations at each observation site. The impact of CO₂ observations on reducing forecast errors, calculated using nonlinear forecast error reduction (NER), showed that NER with DA was reduced by average 17.0 % compared with that without DA. Linear forecast error reduction, calculated using the ensemble forecast sensitivity to observation (EFSO) impact, showed that the EFSO impact was greater at surface CO₂ observation sites with higher self-sensitivity and active vegetation types. Average fraction of beneficial observations for all experiments was 68.9 % (66.3 %) for 6 h (12 h) forecasts, implying that more than half of the assimilated CO₂ observations contributed to reducing forecast errors. The assessment of observation impact on the CO₂ concentration analysis and forecast can be useful for monitoring and estimating atmospheric CO₂ concentrations, optimizing surface CO₂ fluxes, and designing atmospheric CO₂ observation networks.

The effect of assimilated surface CO₂ concentration observations on the analysis and forecast errors of model CO₂ concentrations was evaluated using the data assimilation (DA)-forecast system combining the Weather Research and Forecasting model coupled with Chemistry and modified Data Assimilation Research Testbed. To investigate the impact of surface CO₂ observations, four observing system simulation experiments (OSSEs) were conducted in July 2019. The impact of CO₂ observations on the CO₂ concentration analysis was calculated using self-sensitivity. Average self-sensitivity of four OSSEs was 21.0 %, implying that surface CO₂ observations provided average 21.0 % information to the CO₂ concentration analysis. Self-sensitivity was highly correlated with the root mean square error of the analysis and hourly variability in the surface CO₂ observations at each observation site. The impact of CO₂ observations on reducing forecast errors, calculated using nonlinear forecast error reduction (NER), showed that NER with DA was reduced by average 17.0 % compared with that without DA. Linear forecast error reduction, calculated using the ensemble forecast sensitivity to observation (EFSO) impact, showed that the EFSO impact was greater at surface CO₂ observation sites with higher self-sensitivity and active vegetation types. Average fraction of beneficial observations for all experiments was 68.9 % (66.3 %) for 6 h (12 h) forecasts, implying that more than half of the assimilated CO₂ observations contributed to reducing forecast errors. The assessment of observation impact on the CO₂ concentration analysis and forecast can be useful for monitoring and estimating atmospheric CO₂ concentrations, optimizing surface CO₂ fluxes, and designing atmospheric CO₂ observation networks.

The effect of assimilated surface CO₂ concentration observations on the analysis and forecast errors of model CO₂ concentrations was evaluated using the data assimilation (DA)-forecast system combining the Weather Research and Forecasting model coupled with Chemistry and modified Data Assimilation Research Testbed. To investigate the impact of surface CO₂ observations, four observing system simulation experiments (OSSEs) were conducted in July 2019. The impact of CO₂ observations on the CO₂ concentration analysis was calculated using self-sensitivity. Average self-sensitivity of four OSSEs was 21.0 %, implying that surface CO₂ observations provided average 21.0 % information to the CO₂ concentration analysis. Self-sensitivity was highly correlated with the root mean square error of the analysis and hourly variability in the surface CO₂ observations at each observation site. The impact of CO₂ observations on reducing forecast errors, calculated using nonlinear forecast error reduction (NER), showed that NER with DA was reduced by average 17.0 % compared with that without DA. Linear forecast error reduction, calculated using the ensemble forecast sensitivity to observation (EFSO) impact, showed that the EFSO impact was greater at surface CO₂ observation sites with higher self-sensitivity and active vegetation types. Average fraction of beneficial observations for all experiments was 68.9 % (66.3 %) for 6 h (12 h) forecasts, implying that more than half of the assimilated CO₂ observations contributed to reducing forecast errors. The assessment of observation impact on the CO₂ concentration analysis and forecast can be useful for monitoring and estimating atmospheric CO₂ concentrations, optimizing surface CO₂ fluxes, and designing atmospheric CO₂ observation networks.

To improve atmospheric CO₂ concentration simulations in East Asia, a data assimilation (DA)–forecast system that assimilates surface CO₂ concentrations was developed by modifying the Data Assimilation Research Testbed (DART) and combining it with the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem). To verify the stability of the DA–forecast system, observing system simulation experiments (OSSEs) were conducted during January and July 2019, and the DA effects according to surface CO₂ observation networks were investigated. A nature run (NR; considered as the true state), a control run (CR) without DA, and assimilation runs (ARs) with DA were conducted. The stability of the DA–forecast system was investigated in AR with evenly distributed CO₂ observations to avoid the influence of observation distribution. During January and July 2019, the ratios of the root‐mean‐squared error (RMSE) to the ensemble total spread of simulated CO₂ concentrations were 1.00 and 0.97, respectively. By assimilating surface CO₂ concentrations, the bias and RMSE of simulated CO₂ concentrations were reduced by 1.23 ppm (50%) and 1.24 ppm (39%) in January and 1.41 ppm (66%) and 1.84 ppm (47%) in July, implying that the DA was performed stably in the DA–forecast system. To investigate the DA effects according to surface CO₂ observation network, four AR experiments were conducted. Based on NR, in January (July), the RMSE of CR was 4.13 (5.02) ppm and the RMSEs of four ARs ranged from 3.40 (4.24) to 3.76 (4.44) ppm. The RMSE was smaller for ARs using evenly distributed observation sites, whereas the RMSEs in regions without observations were relatively high in the ARs using concentrated observation sites. The effects of the surface CO₂ concentration DA using the developed system differed depending on observation networks, implying the necessity of an appropriate surface CO₂ observation network to improve the CO₂ concentration simulations in East Asia.

In the Korean Peninsula, intricate meteorological conditions influence fine particulate matter (PM) concentrations, making the improvement of both air-quality and meteorological forecasts crucial for better PM predictions. Data assimilation (DA) can help enhancing air-quality forecasts by reducing initial condition uncertainties of air-quality and meteorology. This study investigates the impacts of chemical and meteorological DA on air-quality and meteorological forecasts during a high PM event in the Korean Peninsula. Observational verification showed that the combined application of chemical and meteorological DA yielded the greatest improvements in air-quality and meteorological forecasts. While chemical DA primarily enhanced air-quality predictions, meteorological DA was essential for improving meteorological forecasts. The study also assessed the effects of chemical and meteorological DA on air-quality and meteorological forecasts in both DA cycling and non-cycling processes, with respect to the forecasts without DA. Based on the root-mean-square differences between forecasts with and without DA, the impacts of chemical and meteorological DA on air-quality forecasts were found to be similar in cycling and non-cycling processes. In the simultaneous chemical–meteorological DA experiment, the effects of the chemical DA and meteorological DA complemented each other. In the cycling DA process, chemical DA influenced meteorological forecasts, and meteorological DA affected air-quality forecasts due to cumulative DA effects. Chemical DA improved the absolute levels of PM in forecasts, while meteorological DA enhanced the spatiotemporal accuracy of PM distribution by refining transport processes. Consequently, simultaneous chemical–meteorological DA proved to be the most effective approach for changing air-quality and meteorological forecasts, and could offer substantial improvements in air-quality and meteorological forecasts in the Korean Peninsula. AcknowledgementsThis study was supported by the National Research Foundation of Korea (2021R1A2C1012572) funded by the South Korean government (Ministry of Science and ICT) and the Yonsei Signature Research Cluster Program of 2024 (2024-22-0162).

In this study, a high-resolution CO2 data assimilation (DA)-forecast system was developed to improve atmospheric CO2 concentration simulations in East Asia. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was used for simulating regional CO2 concentrations and the ensemble adjusted Kalman filter (EAKF) in modified Data Assimilation Research Testbed (DART) was used for assimilating CO2 concentration observations. To evaluate the performance of the developed DA-forecast system, observing system simulation experiment (OSSE) was performed in January and July 2019. Four experiments, which assimilated pseudo surface CO2 observations from four observation site networks, were conducted to avoid the influence of observation site distributions. In January and July 2019, the ratios of the root mean square error (RMSE) to the ensemble total spread for surface CO2 concentrations were 1.00 and 0.97, respectively. By assimilating surface CO2 concentrations, the bias and RMSE of simulated CO2 concentrations reduced by 1.23 ppm and 1.24 ppm in January and 1.41 ppm and 1.84 ppm in July, implying the stability of the developed DA-forecast system. Among four experiments, the experiment with an evenly distributed observation site network showed the smallest RMSE for surface CO2 concentration. The RMSE of the experiment with the existing CO2 observation network was greater than that with the evenly distributed observation network, but was smaller than that without DA. While the DA using the evenly distributed observation network showed the best performance for simulating CO2 concentrations in East Asia, the DA using the existing surface CO2 observation network also improved CO2 simulation performance compared to the experiment without DA.AcknowledgmentsThis study was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (Ministry of Science and ICT) (Grant 2021R1A2C1012572) and the Yonsei Signature Research Cluster Program of 2024 (2024-22-0162).