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Ji-Sun Kang
Korea Institute of Science and Technology Information, Daejeon 34141, Korea

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Journal article
Published: 11 May 2021 in Future Internet
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Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

ACS Style

Jiseong Son; Chul-Su Lim; Hyoung-Seop Shim; Ji-Sun Kang. Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data. Future Internet 2021, 13, 124 .

AMA Style

Jiseong Son, Chul-Su Lim, Hyoung-Seop Shim, Ji-Sun Kang. Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data. Future Internet. 2021; 13 (5):124.

Chicago/Turabian Style

Jiseong Son; Chul-Su Lim; Hyoung-Seop Shim; Ji-Sun Kang. 2021. "Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data." Future Internet 13, no. 5: 124.

Research article
Published: 13 November 2018 in Quarterly Journal of the Royal Meteorological Society
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We test an ensemble data assimilation system using the 4‐D Local Ensemble Transform Kalman Filter (4D‐LEKTF) for a global Numerical Weather Prediction (NWP) model with unstructured grids on the cubed sphere. It is challenging to selectively represent structures of dynamically growing errors in background states under system uncertainties such as sampling and model errors. We compute Ensemble Singular Vectors (ESVs) in an attempt to capture fast growing errors on the subspace spanned by ensemble perturbations, and use them as additive inflation to enlarge the covariance in the area where errors are flow‐dependently growing. The performance of the 4D‐LETKF system with ESVs is evaluated in real data assimilation, as well as Observing System Simulation Experiments (OSSEs). We find that leading ESVs help to capture fast growing errors effectively, especially when model errors are present, and that the use of ESVs as additive inflation significantly improves the performance of the 4D‐LETKF.

ACS Style

Seoleun Shin; Ji-Sun Kang; Shu-Chih Yang; Eugenia Kalnay. Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model. Quarterly Journal of the Royal Meteorological Society 2018, 145, 258 -272.

AMA Style

Seoleun Shin, Ji-Sun Kang, Shu-Chih Yang, Eugenia Kalnay. Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model. Quarterly Journal of the Royal Meteorological Society. 2018; 145 (718):258-272.

Chicago/Turabian Style

Seoleun Shin; Ji-Sun Kang; Shu-Chih Yang; Eugenia Kalnay. 2018. "Ensemble singular vectors as additive inflation in the Local Ensemble Transform Kalman Filter (LETKF) framework with a global NWP model." Quarterly Journal of the Royal Meteorological Society 145, no. 718: 258-272.

Article
Published: 01 June 2018 in Asia-Pacific Journal of Atmospheric Sciences
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An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.

ACS Style

Seoleun Shin; Jeon-Ho Kang; Hyoung-Wook Chun; Sihye Lee; Kwangjae Sung; Kyoungmi Cho; Youngsoon Jo; Jung-Eun Kim; In-Hyuk Kwon; Sujeong Lim; Ji-Sun Kang. Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere. Asia-Pacific Journal of Atmospheric Sciences 2018, 54, 351 -360.

AMA Style

Seoleun Shin, Jeon-Ho Kang, Hyoung-Wook Chun, Sihye Lee, Kwangjae Sung, Kyoungmi Cho, Youngsoon Jo, Jung-Eun Kim, In-Hyuk Kwon, Sujeong Lim, Ji-Sun Kang. Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere. Asia-Pacific Journal of Atmospheric Sciences. 2018; 54 (1):351-360.

Chicago/Turabian Style

Seoleun Shin; Jeon-Ho Kang; Hyoung-Wook Chun; Sihye Lee; Kwangjae Sung; Kyoungmi Cho; Youngsoon Jo; Jung-Eun Kim; In-Hyuk Kwon; Sujeong Lim; Ji-Sun Kang. 2018. "Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere." Asia-Pacific Journal of Atmospheric Sciences 54, no. 1: 351-360.

Journal article
Published: 02 April 2016 in Pure and Applied Geophysics
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We develop an ensemble data assimilation system using the four-dimensional local ensemble transform kalman filter (LEKTF) for a global hydrostatic numerical weather prediction (NWP) model formulated on the cubed sphere. Forecast-analysis cycles run stably and thus provide newly updated initial states for the model to produce ensemble forecasts every 6 h. Performance of LETKF implemented to the global NWP model is verified using the ECMWF reanalysis data and conventional observations. Global mean values of bias and root mean square difference are significantly reduced by the data assimilation. Besides, statistics of forecast and analysis converge well as the forecast-analysis cycles are repeated. These results suggest that the combined system of LETKF and the global NWP formulated on the cubed sphere shows a promising performance for operational uses.

ACS Style

Seoleun Shin; Ji-Sun Kang; Youngsoon Jo. The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere. Pure and Applied Geophysics 2016, 173, 2555 -2570.

AMA Style

Seoleun Shin, Ji-Sun Kang, Youngsoon Jo. The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere. Pure and Applied Geophysics. 2016; 173 (7):2555-2570.

Chicago/Turabian Style

Seoleun Shin; Ji-Sun Kang; Youngsoon Jo. 2016. "The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere." Pure and Applied Geophysics 173, no. 7: 2555-2570.

Journal article
Published: 30 September 2015 in Atmosphere
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ACS Style

Youngsoon Jo; Ji-Sun Kang; Hataek Kwon. Optimization of the Vertical Localization Scale for GPS-RO Data Assimilation within KIAPS-LETKF System. Atmosphere 2015, 25, 529 -541.

AMA Style

Youngsoon Jo, Ji-Sun Kang, Hataek Kwon. Optimization of the Vertical Localization Scale for GPS-RO Data Assimilation within KIAPS-LETKF System. Atmosphere. 2015; 25 (3):529-541.

Chicago/Turabian Style

Youngsoon Jo; Ji-Sun Kang; Hataek Kwon. 2015. "Optimization of the Vertical Localization Scale for GPS-RO Data Assimilation within KIAPS-LETKF System." Atmosphere 25, no. 3: 529-541.

Technical report
Published: 23 July 2014 in Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"
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We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-scale resolution. The main properties of the LETKF-C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advancesmore » include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-2, and upper troposphere AIRS retrievals). After a spin-up of about one month, the LETKF-C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available.« less

ACS Style

Eugenia Kalnay; Ji-Sun Kang; Inez Fung. Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter". Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter" 2014, 1 .

AMA Style

Eugenia Kalnay, Ji-Sun Kang, Inez Fung. Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter". Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter". 2014; ():1.

Chicago/Turabian Style

Eugenia Kalnay; Ji-Sun Kang; Inez Fung. 2014. "Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter"." Final report on "Carbon Data Assimilation with a Coupled Ensemble Kalman Filter" , no. : 1.

Journal article
Published: 19 December 2012 in Journal of Geophysical Research
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ACS Style

Ji-Sun Kang; Eugenia Kalnay; Takemasa Miyoshi; Junjie Liu; Inez Fung. Estimation of surface carbon fluxes with an advanced data assimilation methodology. Journal of Geophysical Research 2012, 117, 1 .

AMA Style

Ji-Sun Kang, Eugenia Kalnay, Takemasa Miyoshi, Junjie Liu, Inez Fung. Estimation of surface carbon fluxes with an advanced data assimilation methodology. Journal of Geophysical Research. 2012; 117 (D24):1.

Chicago/Turabian Style

Ji-Sun Kang; Eugenia Kalnay; Takemasa Miyoshi; Junjie Liu; Inez Fung. 2012. "Estimation of surface carbon fluxes with an advanced data assimilation methodology." Journal of Geophysical Research 117, no. D24: 1.

Journal article
Published: 09 March 2012 in Journal of Geophysical Research
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[1] This study is our first step toward the generation of 6 hourly 3‐D CO2 fields that can be used to validate CO2 forecast models by combining CO2 observations from multiple sources using ensemble Kalman filtering. We discuss a procedure to assimilate Atmospheric Infrared Sounder (AIRS) column‐averaged dry‐air mole fraction of CO2 (Xco2) in conjunction with meteorological observations with the coupled Local Ensemble Transform Kalman Filter (LETKF)‐Community Atmospheric Model version 3.5. We examine the impact of assimilating AIRS Xco2 observations on CO2 fields by comparing the results from the AIRS‐run, which assimilates both AIRS Xco2 and meteorological observations, to those from the meteor‐run, which only assimilates meteorological observations. We find that assimilating AIRS Xco2 results in a surface CO2 seasonal cycle and the N‐S surface gradient closer to the observations. When taking account of the CO2 uncertainty estimation from the LETKF, the CO2 analysis brackets the observed seasonal cycle. Verification against independent aircraft observations shows that assimilating AIRS Xco2 improves the accuracy of the CO2 vertical profiles by about 0.5–2 ppm depending on location and altitude. The results show that the CO2 analysis ensemble spread at AIRS Xco2 space is between 0.5 and 2 ppm, and the CO2 analysis ensemble spread around the peak level of the averaging kernels is between 1 and 2 ppm. This uncertainty estimation is consistent with the magnitude of the CO2 analysis error verified against AIRS Xco2 observations and the independent aircraft CO2 vertical profiles.

ACS Style

Junjie Liu; Inez Fung; Eugenia Kalnay; Ji-Sun Kang; Edward T. Olsen; Luke Chen. Simultaneous assimilation of AIRS Xco2and meteorological observations in a carbon climate model with an ensemble Kalman filter. Journal of Geophysical Research 2012, 117, 1 .

AMA Style

Junjie Liu, Inez Fung, Eugenia Kalnay, Ji-Sun Kang, Edward T. Olsen, Luke Chen. Simultaneous assimilation of AIRS Xco2and meteorological observations in a carbon climate model with an ensemble Kalman filter. Journal of Geophysical Research. 2012; 117 (D5):1.

Chicago/Turabian Style

Junjie Liu; Inez Fung; Eugenia Kalnay; Ji-Sun Kang; Edward T. Olsen; Luke Chen. 2012. "Simultaneous assimilation of AIRS Xco2and meteorological observations in a carbon climate model with an ensemble Kalman filter." Journal of Geophysical Research 117, no. D5: 1.

Journal article
Published: 30 June 2011 in Geophysical Research Letters
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[1] Inference of surface CO2 fluxes from atmospheric CO2 observations requires information about large‐scale transport and turbulent mixing in the atmosphere, so transport errors and the statistics of the transport errors have significant impact on surface CO2 flux estimation. In this paper, we assimilate raw meteorological observations every 6 hours into a general circulation model with a prognostic carbon cycle (CAM3.5) using the Local Ensemble Transform Kalman Filter (LETKF) to produce an ensemble of meteorological analyses that represent the best approximation to the atmospheric circulation and its uncertainty. We quantify CO2 transport uncertainties resulting from the uncertainties in meteorological fields by running CO2 ensemble forecasts within the LETKF‐CAM3.5 system forced by prescribed surface fluxes. We show that CO2 transport uncertainties are largest over the tropical land and the areas with large fossil fuel emissions, and are between 1.2 and 3.5 ppm at the surface and between 0.8 and 1.8 ppm in the column‐integrated CO2 (with OCO‐2‐like averaging kernel) over these regions. We further show that the current practice of using a single meteorological field to transport CO2 has weaker vertical mixing and stronger CO2 vertical gradient when compared to the mean of the ensemble CO2 forecasts initialized by the ensemble meteorological fields, especially over land areas. The magnitude of the difference at the surface can be up to 1.5 ppm.

ACS Style

Junjie Liu; Inez Fung; Eugenia Kalnay; Ji-Sun Kang. CO2transport uncertainties from the uncertainties in meteorological fields. Geophysical Research Letters 2011, 38, 1 .

AMA Style

Junjie Liu, Inez Fung, Eugenia Kalnay, Ji-Sun Kang. CO2transport uncertainties from the uncertainties in meteorological fields. Geophysical Research Letters. 2011; 38 (12):1.

Chicago/Turabian Style

Junjie Liu; Inez Fung; Eugenia Kalnay; Ji-Sun Kang. 2011. "CO2transport uncertainties from the uncertainties in meteorological fields." Geophysical Research Letters 38, no. 12: 1.

Journal article
Published: 12 May 2011 in Journal of Geophysical Research
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ACS Style

Ji-Sun Kang; Eugenia Kalnay; Junjie Liu; Inez Fung; Takemasa Miyoshi; Kayo Ide. “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation. Journal of Geophysical Research 2011, 116, 1 .

AMA Style

Ji-Sun Kang, Eugenia Kalnay, Junjie Liu, Inez Fung, Takemasa Miyoshi, Kayo Ide. “Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation. Journal of Geophysical Research. 2011; 116 (D9):1.

Chicago/Turabian Style

Ji-Sun Kang; Eugenia Kalnay; Junjie Liu; Inez Fung; Takemasa Miyoshi; Kayo Ide. 2011. "“Variable localization” in an ensemble Kalman filter: Application to the carbon cycle data assimilation." Journal of Geophysical Research 116, no. D9: 1.