Publications

Books

  1. Saito, K., Y. Shoji, S. Origuchi and L. Duc, 2017: GPS PWV assimilation with the JMA nonhydrostatic 4DVAR and cloud resolving ensemble forecast for the 2008 August Tokyo metropolitan area local heavy rainfalls. Data Assimilation for Atmospheric, Oceanic and Hydrological Applications, 3, 383-404. doi: 10.1007/978-3-319-43415-5_17.

  2. Dang, Q.A., M. Ehrhardt, G.L. Tran, and D. Le, 2007: On the numerical solution of some problems of environmental pollution, Chapter 8 in: C.B. Bodine (ed.), Air Pollution Research Advances, Nova Science Publishers, Inc., Hauppauge, NY 11788, pp. 171-200.

Peer-reviewed Journals

  1. Duc, L., T. Kawabata, K. Saito, and T. Oizumi, 2021: Forecasts of the July 2020 Kyushu heavy rain using a 1000-member ensemble Kalman filter. SOLA, 17, doi:10.2151/sola.2021-007

  2. Oizumi, T., K. Saito, L. Duc, and J. Ito, 2020: Ultra-high-resolution numerical weather prediction with a large domain using the K Computer. Part 2: The Case of the Hiroshima Heavy Rainfall Event on August 2014 and Dependency of Simulated Convective Cells on Model Resolutions. J. Meteor. Soc. Japan, 98, 1163–1182, doi:10.2151/jmsj.2020-060

  3. Duc, L., K. Saito, and D. Hotta, 2020: Analysis and design of covariance inflation methods using inflation functions. Part 1: Theoretical framework. Quart. J. Roy. Meteor. Soc., 146, 3638–3660, doi: 10.1002/qj.3864

  4. Kobayashi, K., L. Duc, T. Oizumi, and K. Saito, 2020: Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls. Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls. Nat. Hazards Earth Syst. Sci., 20, 755–770

  5. Duc, L., K. Saito, and D. Hotta, 2020: An explanation for the diagonally predominant property of the positive symmetric ensemble transform matrix. J. Meteor. Soc. Japan, 98, 455–462, doi: 10.2151/jmsj.2020-022

  6. Duc, L. and K. Saito, 2018: Verification in the presence of observation errors: Bayesian point of view. Quart. J. Roy. Meteor. Soc., 144, 1063–1090, doi:10.1002/qj.3275

  7. Oizumi, T., K. Saito, J. Ito, T. Kuroda, and L. Duc, 2018: Ultra-high-resolution numerical weather prediction with a large domain using the K Computer. Part 1: A case study of the Izu Oshima heavy rainfall event on October 15-16, 2013. J. Meteor. Soc. Japan, 96, 25–54, doi:10.2151/jmsj.2018-006

  8. Duc, L. and K. Saito, 2017: On cost functions in the hybrid variational-ensemble method. Mon. Wea. Rev., 145, 2071-2082, doi:10.1175/MWR-D-16-0325.1

  9. Ito, K., M. Kunii, T. Kawabata, K. Saito, K. Aonashi and L. Duc, 2016: Mesoscale hybrid data assimilation system based on JMA nonhydrostatic model. Mon. Wea. Rev., 144, 3417-3439, doi:10.1175/MWR-D-16-0014.1

  10. Kieu, X., H. Vu, T. Nguyen, D. Le, L. Nguyen, I. Takayabu, H. Sasaki, and A. Kitoh, 2015: Rainfall and tropical cyclone activity over Vietnam simulated and projected by the non-hydrostatic regional climate model – NHRCM. J. Meteor. Soc. Japan, doi: 10.2151/jmsj.2015-057

  11. Duc, L., T. Kuroda, K. Saito and T. Fujita, 2015: Ensemble Kalman Filter data assimilation and storm surge experiments of tropical cyclone Nargis. Tellus A, 67, doi:10.3402/tellusa.v67.25941

  12. Duc, L., K. Saito and H. Seko, 2013: Spatial-temporal fractions verification for high resolution ensemble forecasts. Tellus A, 65, doi:10.3402/tellusa.v65i0.18171

  13. Dang, Q.A., M. Ehrhardt, G.L. Tran, and D. Le, 2012: Mathematical modeling and numerical algorithms for simulation of oil pollution. Environmental Modeling and Assessment, 17, 275-288

  14. Saito, K., T. Tsuyuki, H. Seko, F. Kimura, T. Tokioka, T. Kuroda, L. Duc, K. Ito, T. Oizumi, G. Chen, J. Ito, and SPIRE Field 3 Mesoscale NWP group, 2013: Super high-resolution mesoscale weather prediction. J. Phys. Conf. Ser., 454, (012073), doi:10.1088/1742-6596/454/1/012073

  15. Lang, N.D., T.G. Lich, and L. Duc, 2006: Two approximation methods of spatial derivatives on unstructured triangular meshes and their application in computing two dimensional flows, Vietnam Journal of Mechanics, Vol. 28, 230-240

  16. Duc, L., L.C. Thanh, and K.T. Xin, 2005: On the high resolution regional weather forecast model (HRM) and forecasting tropical cyclone motion over the South China Sea, Vietnam Journal of Mechanics, Vol. 27, 193-203

Technical Reports

  1. Saito, K., S. Origuchi, L. Duc, and K. Kobayashi, 2013: Mesoscale ensemble prediction of the 2010 Niigata-Fukushima heavy rainfall, (in Japanese), Japan Meteorological Agency, 134, pp.170-184

  2. Saito, K., T. Kuroda, S. Hayashi, H. Seko, M. Kunii, Y. Shoji, M. Ueno, T. Kawabata, S. Yoden, S. Ostuka, N.J. Trilaksono, T.Y. Koh, S. Koseki, L. Duc, K.T. Xin, W.K. Wong, and K.C. Gouda, 2011: International Research for Prevention and Mitigation of Meteorological Disasters in Southeast Asia, Tech. Rep. MRI, 65, 198pp

Conferences

  1. Duc L., K. Saito, and D. Hotta, 2019: Analysis and design of covariance inflation methods from a functional viewpoint, The 7th International Symposium on Data Assimilation, Kobe, Japan

  2. Duc, L., K. Saito, and D. Hotta, 2019: The diagonally predominant property of the positive symmetric ensemble transform matrix and its application in ensemble forecast. American Meteorological Society annual meeting, Phoenix, USA

  3. Duc L., K. Saito, and D. Hotta, 2018: Development and validation of a diagonal ensemble transform Kalman filter, The 5th International Workshop on Nonhydrostatic Models, Tokyo, Japan

  4. Duc L., and K. Saito, 2018: A 4D-EnVAR data assimilation system without vertical localization. Asia Oceania Geosciences Society meeting, Hawaii, USA

  5. Duc L., K. Saito, and D. Hotta, 2018: Application of diagonal ensemble transform matrices into ensemble forecast, Meteorological Society of Japan Spring meeting, Tsukuba, Japan

  6. Duc L., and K. Saito, 2018: V​e​r​i​f​i​c​a​t​i​o​n​ ​a​n​d​ ​d​a​t​a​ ​a​s​s​i​m​i​l​a​t​i​o​n​:​ ​t​w​o​ ​s​i​d​e​s​ ​o​f​ ​a​ ​c​o​i​n, The 8th annual Japanese data assimilation workshop, Hokkaido, Japan

  7. Duc L., and K. Saito, 2017: An explanation for the diagonally predominant property of ensemble transform matrices, Meteorological Society of Japan Autumn meeting, Hokkaido, Japan

  8. Duc L., and K. Saito, 2017: An EnVAR system with its own analysis perturbations using the block GMRES algorithm. The 14th Asia Oceania Geosciences Society annual meeting, Singapore

  9. Duc, L. and K. Saito, 2017: C​o​m​p​a​r​i​s​o​n​ ​b​e​t​w​e​e​n​ ​o​b​s​e​r​v​a​t​i​o​n​ ​s​p​a​c​e​ ​l​o​c​a​l​i​z​a​t​i​o​n​ ​a​n​d​ ​m​o​d​e​l​ ​s​p​a​c​e​ ​l​o​c​a​l​i​z​a​t​i​o​n​ ​i​n​ ​a​n​ ​E​n​V​A​R​ ​s​y​s​t​e​m. Meteorological Society of Japan Autumn meeting, Tokyo, Japan

  10. Duc, L., T. Kawabata, and K. Saito, 2016: Data assimilation experiments on　the 26 August 2011 heavy rainfall event over Kanto. The 3rd TOMACS international workshop, Tokyo, Japan

  11. Duc, L., and K. Saito, 2016: Inter-comparison of the hybrid variational-ensemble methods. Japan Geoscience Union meeting, Chiba, Japan

  12. Duc L., and K. Saito, 2016: Development of a local Deterministic Ensemble Kalman Filter and comparison with LETKF, Meteorological Society of Japan Autumn meeting, Nagoya, Japan

  13. Duc L., and K. Saito, 2015: B-preconditioned conjugate gradient, an efficient method for Hybrid-4DVAR and Hybrid-4DEnVAR, Meteorological Society of Japan Autumn meeting, Kyoto, Japan

  14. Duc L., T. Kawabata, and K. Saito, 2015: Comparison of 4DVAR, Hybrid-4DVAR, and Hybrid-4DEnVAR at cloud resolving scales, The 12nd Asia Oceania Geosciences Society annual meeting, Singapore

  15. Duc L., T. Kawabata, and K. Saito, 2015: A hybrid-4DVAR system for the JMA non-hydrostatic regional model, Meteorological Society of Japan Spring meeting, Tsukuba, Japan

  16. Duc L., 2015: A practical quadratic ensemble filter, The 4th International Symposium on Data Assimilation, Kobe, Japan

  17. Duc L., T. Kawabata, and K. Saito, 2014: Towards a hybrid variational-ensemble data assimilation system at cloud resolving scales, Meteorological Society of Japan Autumn meeting, Fukuoka, Japan

  18. Duc L., and K. Saito, 2014: Ensemble forecast of storm surges induced by the typhoon Haiyan, The 11st Asia Oceania Geosciences Society annual meeting, Sapporo, Japan

  19. Duc L., K. Saito, and T. Fujita, 2013: Test of deterministic assimilation in NHM-LETKF, Meteorological Society of Japan Spring meeting, Tokyo, Japan

  20. Duc L., T. Kuroda, K. Saito, and T. Fujita, 2012: NHM-LETKF modifications in application for tropical cyclones, Meteorological Society of Japan Autumn meeting, Sapporo, Japan

  21. Duc L., T. Kuroda, K. Saito, and T. Fujita, 2012: Data assimilation with LETKF: the case of Niigata-Fukushima heavy rainfall event, The second International Workshop on Non-hydrostatic Numerical Models, Sendai, Japan

  22. Duc, L., 2008: Development of a short-range ensemble prediction system: preliminary results, Second International Workshop on Prevention and Mitigation of Meteorological Disasters in Southeast Asia, Bandung, Indonesia

  23. Duc, L., 2006: A 3DVAR data assimilation system for HRM, Second HRM Workshop, Madrid, Spain