연세대학교 대기예측성 및 자료동화 연구실

Research

Researches in below categories are all relevant, and fundamentally related to predictability, numerical weather prediction, numerical modeling, data assimilation, etc. For convenience, researches are classified into more closely related topics.

Data assimilation

Variatioanl and ensemble-based approach
Diagnosis and forecast of meteorological variables (numerical weather prediction: NWP) and chemical species (e.g., Carbon Tracker)
Satellite and radar data assimilation (e.g., AMSU-A, Himawari AMVs, radar radial velocity, radar reflectivity, etc.)
Data assimilation for weather forecasts in midlatitude and polar regions

Yang, E.-G., and H. M. Kim, 2021: A comparison of variational, ensemble-based, and hybrid data assimilation methods over East Asia for two one-month periods. Atmospheric Research, 249, 105257, https://doi.org/10.1016/j.atmosres.2020.105257.

Kim, S.-M., and H. M. Kim, 2018: Effect of observation error variance adjustment on numerical weather prediction using forecast sensitivity to error covariance parameters. Tellus A, 70, 1-16, doi:10.1080/16000870.2018.1492839.

Kim, D.-H., and H. M. Kim, 2018: Effect of assimilating Himawari-8 atmospheric motion vectors on forecast errors over East Asia. Journal of Atmospheric and Oceanic Technology, 35, 1737-1752, doi:10.1175/JTECH-D-17-0093.1.

Noh, K., H. M. Kim, and D.-H. Kim, 2018: Development of the three-dimensional variational data assimilation system for the Republic of Korea Air Force operational numerical weather prediction system. Journal of the Korea Institute of Military Science and Technology, 21(3), 403-412, doi:10.9766/KIMST.2018.21.3.403. (in Korean with English abstract)

Park, J. I., and H. M. Kim, 2010: Typhoon Wukong (200610) prediction based on the Ensemble Kalman Filter and ensemble sensitivity analysis. Atmosphere, 20(3), 287-306. (in Korean with English abstract)

Adaptive observation

Specification of observation site and mobile observation location for better forecasts
Adjoint sensitivity and singular vector analysis for high impact weather phenomena (e.g., Typhoon, Heavy rain, Asian dust, etc.)

Kim, H. M., J. K. Kay, E.-G. Yang, S. Kim, M. Lee, 2013: Statistical adjoint sensitivity distributions for meteorological forecast errors of Asian dust transport events in Korea. Tellus B, 65, 20554, https://doi.org/10.3402/tellusb.v65i0.20554.

Park, S.-Y., H. M. Kim, T.-Y. Lee, and M. Morgan, 2013: Statistical distributions of singular vectors for tropical cyclones affecting Korea over a 10-year period. Meteorology and Atmospheric Physics, 120, 107-122, doi: 10.1007/s00703-013-0247-7.

Jung, B.-J., H. M. Kim, F. Zhang, and C.-C. Wu, 2012: Effect of targeted dropsonde observations and best track data on the track forecasts of Typhoon Sinlaku (2008) using an Ensemble Kalman Filter. Tellus A, 64, 14984, doi: 10.3402/tellusa.v64i0.14984.

Kim, H. M., S.-M. Kim, and B.-J. Jung, 2011: Real-time adaptive observation guidance using singular vectors for typhoon Jangmi (200815) in T-PARC 2008. Weather and Forecasting, 26, 634-649, doi: 10.1175/WAF-D-10-05013.1.

Kim, S.-M., H. M. Kim, S.-W. Joo, H.-C. Shin, and D. Won, 2011: Development of tools for calculating forecast sensitivities to the initial condition in the Korea Meteorological Administration (KMA) Unified Model (UM). Atmosphere, 21(2), 163-172. (in Korean with English abstract)

Jung, B.-J. and H. M. Kim, 2009: Moist-adjoint based forecast sensitivities for a heavy snowfall event over the Korean peninsula on 4-5 March 2004. Journal of Geophysical Research, 114, D15104, DOI:10.1029/2008JD011370.

Kim, H. M., and B.-J. Jung, 2009: Influence of moist physics and norms on singular vectors for a tropical cyclone. Monthly Weather Review, 137, 525-543.

Kim, H. M., and B.-J. Jung, 2009: Singular vector structure and evolution of a recurving tropical cyclone. Monthly Weather Review, 137, 505-524.

Kim, H. M., B.-J. Jung, Y. -H. Kim, and H.-S. Lee, 2008: Adaptive observation guidance applied to Typhoon Rusa: Implications for THORPEX-PARC 2008. Asia-Pacific Journal of Atmospheric Sciences, 44, 297-312.

Kim, H. M., and B.-J. Jung, 2006: Adjoint-based forecast sensitivities of Typhoon Rusa. Geophysical Research Letters, 33, L21813, DOI:10.1029/2006GL027289.

Observation impact

Forecast sensitivity observation impact (FSOI) using both adjoint and ensemble-based approach
Observing system simulation experiment (OSSE) and observing system experiment (OSE)

Kim, D.-H., and H. M. Kim, 2023: Evaluation of observation impact on the meteorological forecasts associated with heat wave in 2018 over East Asia using observing system experiments. Heliyon, 9, e23064, https://doi.org/10.1016/j.heliyon.2023.e23064.

Kim, H. M., and D.-H. Kim, 2021: Effect of boundary conditions on adjoint-based forecast sensitivity observation impact in a regional model. Journal of Atmospheric and Oceanic Technology, 38, 1233-1247, doi:10.1175/JTECH-D-20-0040.1.

Kim, S.-M., and H. M. Kim, 2019: Forecast sensitivity observation impact in the 4DVAR and Hybrid-4DVAR data assimilation systems. Journal of Atmospheric and Oceanic Technology, 36, 1563-1575, doi:10.1175/JTECH-D-18-0240.1.

Kim, S.-M., and H. M. Kim, 2017: Adjoint-based observation impact of Advanced Microwave Sounding Unit-A (AMSU-A) on the short-range forecast in East Asia. Atmosphere, 27(1), 93-104, doi:10.14191/Atmos.2017.27.1.093. (in Korean with English abstract)

Kim, M., H. M. Kim, J. Kim, S.-M. Kim, C. Velden, and B. Hoover, 2017: Effect of enhanced satellite-derived atmospheric motion vectors on numerical weather prediction in East Asia using an adjoint-based observation impact method. Weather and Forecasting, 32, 579-594, doi:10.1175/WAF-D-16-0061.1.

Kim, S.-M., and H. M. Kim, 2014: Sampling error of observation impact statistics. Tellus A, 66, 25435, http://dx.doi.org/10.3402/tellusa.v66.25435.

Jung, B.-J., H. M. Kim, T. Auligne, X. Zhang, X. Zhang, and X.-Y. Huang, 2013: Adjoint-derived observation impact using WRF in the western North Pacific. Monthly Weather Review, 141, 4080-4097.

Kim, S., H. M. Kim, E.-J. Kim, and H.-C. Shin, 2013: Forecast sensitivity to observations for high-impact weather events in the Korean Peninsula. Atmosphere, 23(2), 171-186. (in Korean with English abstract)

Jung, B.-J., H. M. Kim, Y.-H. Kim, E.-H. Jeon, and K.-H. Kim, 2010: Observation system experiments for Typhoon Jangmi (200815) observed during T-PARC. Asia-Pacific Journal of Atmospheric Sciences, 46, 305-316.

Ensemble forecast and impact forecast

Probabilistic weather forecast
Connecting meteorological information with disaster information

On, N., H. M. Kim, and S. Kim, 2018: Effects of resolution, cumulus parameterization scheme, and probability forecasting on precipitation forecasts in a high-resolution limited-area ensemble prediction system. Asia-Pacific Journal of Atmospheric Sciences, 54, 623-637, doi:10.1007/s13143-018-0081-4.

Kim, S., and H. M. Kim, 2017: Effect of considering sub-grid scale uncertainties on the forecasts of a high-resolution limited area ensemble prediction system. Pure and Applied Geophysics, 174(5), 2021-2037, doi: 10.1007/s00024-017-1513-2.

Kim, S., H. M. Kim, J. K. Kay, and S.-W. Lee, 2015: Development and evaluation of high resolution limited area ensemble prediction system in KMA. Atmosphere, 25(1), 67-83. (in Korean with English abstract)

Kay, J. K., and H. M. Kim, 2014: Characteristics of initial perturbations in the ensemble prediction system of the Korea Meteorological Administration. Weather and Forecasting, 29, 563-581, doi: 10.1175/WAF-D-13-00097.1.

Kay, J. K., H. M. Kim, Y.-Y. Park, and J. Son, 2013: Effect of doubling ensemble size on the performance of ensemble prediction in warm season using MOGREPS implemented in KMA. Advances in Atmospheric Sciences, 30(5), 1287-1302, doi:10.1007/s00376-012-2083-y.

Regional reanalysis

Development of East Asia Regional Reanalysis (EARR)
Regional Reanalysis over the Tibetan Plateau
AsiaPEX (Asian Precipitation Experiment)

Terao T., S. Kanae, H. Fujinami, S. Das, A. P. Dimri, S. Dutta, K. Fujita, A. Fukushima, K.-J. Ha, M. Hirose, J. Hong, H. Kamimera, R. B. Kayastha, M. Kiguchi, K. Kikuchi, H. M. Kim, A. Kitoh, H. Kubota, W. Ma, Y. Ma, M. Mujumdar, M. I. Nodzu, T. Sato, Z. Su, S. Sugimoto, H. G. Takahashi, Y. Takaya, S. Wang, K. Yang, S. Yokoi, and J. Matsumoto, 2023: AsiaPEX: Challenges and Prospects in Asian Precipitation Research. Bulletin of the American Meteorological Society, accepted.

Yang, E.-G., H. M. Kim, and D.-H. Kim, 2022: Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis. Earth System Science Data, 14, 2109-2127, https://doi.org/10.5194/essd-14-2109-2022.

(Data publication) Yang, E.-G., and H. M. Kim, 2021: East Asia Regional Reanalysis 6 hourly data on pressure levels from 2010 to 2019. https://doi.org/10.7910/DVN/7P8MZT, Harvard Dataverse, V1.

(Data publication) Yang, E.-G., and H. M. Kim, 2021: East Asia Regional Reanalysis 6 hourly data on single levels from 2010 to 2019. https://doi.org/10.7910/DVN/TTML1J, Harvard Dataverse, V1.

(Data publication) Yang, E.-G., and H. M. Kim, 2021: East Asia Regional Reanalysis 6 hourly precipitation data from 2010 to 2019. https://doi.org/10.7910/DVN/Q07VRC, Harvard Dataverse, V1.

He, J., F. Zhang, X. Chen, X. Bao, D. Chen, H. M. Kim, H.-W. Lai, L. R. Leung, X. Ma, Z. Meng, T. Ou, Z. Xiao, E.-G. Yang, and K. Yang, 2019: Development and evaluation of an ensemble-based data assimilation system for regional reanalysis over the Tibetan Plateau and surrounging regions. Journal of Advances in Modeling Earth Systems, 11, 2503-2522, doi:10.1029/2019MS001665.

Yang, E.-G., and H. M. Kim, 2019: Evaluation of short-range precipitation reforecasts from East Asia Regional Reanalysis. Journal of Hydrometeorology, 20, 319-337, doi:10.1175/JHM-D-18-0068.1.

Yang, E.-G., and H. M. Kim, 2017: Evaluation of a regional reanalysis and ERA-Interim over East Asia using in situ observations during 2013-14. Journal of Applied Meteorology and Climatology, 56, 2821-2844, doi: 10.1175/JAMC-D-16-0227.1.

Observation network design

Network design for Asian dust forecast
Network design for surface carbon flux estimation
Network design for NWP in polar region (e.g., Arctic)

Kim, D.-H., and H. M. Kim, 2024: Design of buoy observation network over the Arctic Ocean. Cold Regions Science and Technology, 218, 104087, https://doi.org/10.1016/j.coldregions.2023.104087.

Park, J., and H. M. Kim, 2020: Design and evaluation of CO2 observation network to optimize surface CO2 fluxes in Asia using observation system simulation experiments. Atmospheric Chemistry and Physics, 20, 5175-5195, doi:10.5194/acp-20-5175-2020.

Yang, E.-G., H. M. Kim, J. Kim, and J. K. Kay, 2014: Effect of observation network design on meteorological forecasts of Asian dust events. Monthly Weather Review, 142, 4679-4695, doi:10.1175/MWR-D-14-00080.1.

Atmospheric predictability

Uncertainty quantification
Analysis and forecast error specification
Error growth

Hwang, S.-O., J. Park, and H. M. Kim, 2019: Effect of hydrometeor species on very-short-range simulations of precipitation using ERA5. Atmospheric Research, 218, 245-256, doi:10.1016/j.atmosres.2018.12.008.

Hong, S.-Y., H. M. Kim, J.-E. Kim, S.-O. Hwang, and H. Park, 2011: The impact of model uncertainties on analyzed data in a global data assimilation system. Terrestrial, Atmospheric and Oceanic Sciences, 22(1),41-47.

Kim, H. M., and R. J. Beare, 2011: Characteristics of adjoint sensitivity to potential vorticity. Meteorology and Atmospheric Physics, 111, 91-102, doi: 10.1007/s00703-010-0119-3.

Kim, H. M., M. Morgan, and R. E. Morss, 2004: Evolution of analysis error and adjoint-based sensitivities: Implications for adaptive observations. Journal of the Atmospheric Sciences, 61, 795-812.

Kim, H. M., 2003: A computation of adjoint-based sensitivities in a quasigeostrophic model. Korean Journal of the Atmospheric Sciences, 6(2), 71-83.

Kim, H. M. and M. Morgan, 2002: Dependence of singular vector structure and evolution on the choice of norm. Journal of the Atmospheric Sciences, 59, 3099-3116.

Large and synoptic scale dynamics

Potential vorticity analysis
Cyclogenesis

Kim, H. M., Y. H. Youn, and H. S. Chung, 2004: Potential vorticity thinking as an aid to understanding midlatitude weather systems. Journal of the Korean Meteorological Society, 40(6), 633-647.

Carbon and air pollution modeling

Diagnosis and forecast of chemical species (e.g., Carbon Tracker, WRF chem)
Data assimilation for surface carbon flux estimation and air pollutant forecast in midlatitude

Seo, M.-G., H. M. Kim, and D.-H. Kim, 2024: High-resolution atmospheric CO2 concentration data simulated in WRF-Chem over East Asia for 10 years. Geoscience Data Journal, https://doi.org/10.1002/gdj3.273.

Cho, Y., H. M. Kim, E.-G. Yang, Y. Lee, J.-B. Lee, and S. Ha, 2024: Effect of meteorological data assimilation on regional air quality forecasts over the Korean Peninsula. Journal of Meteorological Research, 38, 1-23, https://doi.org/10.1007/s13351-024-3152-8.

Ha, S., R. Kumar, G. G. Pfister, Y. Lee, D. Lee, H. M. Kim, and Y.-H. Ryu, 2024: Chemical data assimilation with aqueous chemistry in WRF-Chem coupled with WRFDA (V4.4.1). Journal of Advances in Modeling Earth Systems, 16, e2023MS003928, https://doi.org/10.1029/2023MS003928.

Seo, M.-G., H. M. Kim, and D.-H. Kim, 2024: Effect of atmospheric conditions and VPRM parameters on high-resolution regional CO2 simulations over East Asia. Theoretical and Applied Climatology, 155, 859-877, https://doi.org/10.1007/s00704-023-04663-2.

Seo, M.-G., and H. M. Kim, 2023: Effect of meteorological data assimilation using 3DVAR on high-resolution simulations of atmospheric CO2 concentrations in East Asia. Atmospheric Pollution Research, 14, 101759, https://doi.org/10.1016/j.apr.2023.101759.

Cho, M., and H. M. Kim, 2022: Effect of assimilating CO2 observations in the Korean Peninsula on the inverse modeling to estimate surface CO2 flux over Asia. PLoS ONE, 17, e0263925. https://doi.org/10.1371/journal.pone.0263925.

Kim, H., H. M. Kim, M. K. Cho, J. Park, and D.-H. Kim, 2018: Development of the aircraft CO2 measurement data assimilation system to improve the estimation of surface CO2 fluxes using an inverse modeling system. Atmosphere, 28(2), 1-9, doi:10.14191/Atmos.2018.28.2.113. (in Korean with English abstract)

Kim, H., H. M. Kim, J. Kim, and C.-H. Cho, 2018: Effect of data assimilation parameters on the optimized surface CO2 flux in Asia. Asia-Pacific Journal of Atmospheric Sciences, 54, 1-17, doi:10.1007/s13143-017-0049-9.

Kim, J., H. M. Kim, C.-H. Cho, K.-O. Boo, A. R. Jacobson, M. Sasakawa, T. Machida, M. Arshinov, and N. Fedoseev, 2017: Impact of Siberian observations on the optimization of surface CO₂ flux. Atmospheric Chemistry and Physics, 17, 2881-2899, doi:10.5194/acp-17-2881-2017.

Kim, H. J., H. M. Kim, J. Kim, and C.-H. Cho, 2016: A Comparison of the Atmospheric CO₂ Concentrations Obtained by an Inverse Modeling System and Passenger Aircraft Based Measurement. Atmosphere, 26(3), 387-400. (in Korean with English abstract)

Kim, J., H. M. Kim, and C.-H. Cho, 2014: Influence of CO2 observations on the optimized CO2 flux in an ensemble Kalman filter. Atmospheric Chemistry and Physics, 14, 13515-13530, doi:10.5194/acp-14-13515-2014.

Kim, J., H. M. Kim, and C.-H. Cho, 2014: The effect of optimization and the nesting domain on carbon flux analyses in Asia using a carbon tracking system based on the ensemble Kalman filter. Asia-Pacific Journal of Atmospheric Sciences, 50, 327-344, https://doi.org/10.1007/s13143-014-0020-y.

Kim, J., H. M. Kim, and C.-H. Cho, 2012: Application of Carbon Tracking System based on ensemble Kalman filter on the diagnosis of Carbon Cycle in Asia. Atmosphere, 22(4), 415-427. (in Korean with English abstract)

Kim, H. M., and J. K. Kay, 2010: Forecast sensitivity analysis of an Asian dust event occurred on 6-8 May 2007 in Korea. Atmosphere, 20(4), 399-414. (in Korean with English abstract)

Kim, H. M., J. K. Kay, and B.-J. Jung, 2008: Application of adjoint-based forecast sensitivities to Asian dust transport events in Korea. Water, Air, and Soil Pollution, 195, 335-343, DOI:10.1007/s11270-008-9750-8.

Arctic weather and climate, Deep learning

Diagnosis and forecast of meteorological variables (e.g., Polar WRF)
Data assimilation for weather forecasts in polar region
Deep learning for weather and climate forecasts in polar region

Kim, D.-H., and H. M. Kim, 2024: Adjoint-based observation impact on meteorological forecast errors in the Arctic. Quarterly Journal of the Royal Meteorological Society, accepted.

Kim, D.-H., and H. M. Kim, 2024: Effect of microphysics scheme and data assimilation on hydrometeor and radiative flux simulations in the Arctic. Royal Society Open Science, 11, 240594, https://doi.org/10.1098/rsos.240594.

Kim, D.-H., and H. M. Kim, 2022: Deep learning for downward longwave radiative flux forecasts in the Arctic. Expert Systems With Applications. 210, 118547, https://doi.org/10.1016/j.eswa.2022.118547.

Kim, D.-H., and H. M. Kim, 2022: Effect of data assimilation in the Polar WRF with 3DVAR on the prediction of radiation, heat flux, cloud, and near surface atmospheric variables over Svalbard. Atmospheric Research, 272, 106155, https://doi.org/10.1016/j.atmosres.2022.106155.

Kim, D.-H., H. M. Kim, and J. Hong, 2019: Evaluation of wind forecasts over Svalbard using the high-resolution Polar WRF with 3DVAR. Arctic, Antarctic, and Alpine Research, 51, 471-489, doi:10.1080/15230430.2019.1676939.