List of Publications
Updated on 09/28/2021

Peer-reviewed papers

    - in press -
  1. Dillon, M. E., P. Maldonado, P. Corrales, Y. García Skabar, J. Ruiz, M. Sacco, F. Cutraro, L. Mingari, C. Matsudo, L. Vidal, M. Rugna, M. P. Hobouchian, P. Salio, S. Nesbitt, C. Saulo, E. Kalnay, T. Miyoshi, 2021: A rapid refresh ensemble based data assimilation and forecast system for the RELAMPAGO field campaign. Atmos. Res., in press. doi:10.1016/j.atmosres.2021.105858
  2. Honda, T., Y. Sato, and T. Miyoshi, 2021: Potential impacts of lightning flash observations on numerical weather prediction with explicit lightning processes. J. Geophys. Res. Atmos., 126, e2021JD034611. doi:10.1029/2021JD034611
  3. Ruiz, J., G.-Y. Lien, K. Kondo, S. Otsuka, and T. Miyoshi, 2021: Reduced non-Gaussianity by 30-second rapid update in convective-scale numerical weather prediction, Nonlin. Processes Geophys., in press.
  4. Okazaki, A., T. Miyoshi, K. Yoshimura, S. J. Greybush, and F. Zhang, 2021: Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE study. J. Geophys. Res. Atmos., 126, doi:10.1029/2020JD034214.
  5. Taylor, J., A. Okazaki, T. Honda, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, T. Miyoshi, 2021: Oversampling Reflectivity Observations from a Geostationary Precipitation Radar Satellite: Impact on Typhoon Forecasts within a Perfect Model OSSE Framework. J. Adv. Modeling Earth Systems, in press. doi:10.1029/2020MS002332
  6. Yamazaki, A., T. Miyoshi, J. Inoue, T. Enomoto, and N. Komori 2021: EFSO at different geographical locations verified with observing-system experiments. Weather and Forecasting, in press.
  7. Taylor, J., T. Honda, A. Amemiya, Y. Maejima and T. Miyoshi, 2021: Predictability of the July 2020 Heavy Rainfall with the SCALE-LETKF. SOLA, in press. doi:10.2151/sola.2021-008

    - 2021 -
  8. Arakida, H., S. Kotsuki, S. Otsuka, Y. Sawada, and T. Miyoshi, 2021: Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia. Prog. Earth Planet. Sci., 8, 52. doi:10.1186/s40645-021-00443-6
  9. Honda, T. and T. Miyoshi, 2021: Predictability of the July 2018 Heavy Rain Event in Japan Associated with Typhoon Prapiroon and Southern Convective Disturbances. SOLA, 17, 113−119. doi:10.2151/sola.2021-018

    - 2020 -
  10. Yashiro, H., K. Terasaki, Y. Kawai, S. Kudo, T. Miyoshi, T. Imamura, K. Minami, H. Inoue, T. Nishiki, T. Saji, M. Satoh, and H. Tomita, 2020: A 1024-member ensemble data assimilation with 3.5-km mesh global weather simulations. SC '20: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 1, 1-10. doi:10.5555/3433701.3433703
  11. Tandeo, P., P. Ailliot, M. Bocquet, A. Carrassi, T. Miyoshi, M. Pulido, Y. Zhen, 2020: A Review of Innovation-Based Methods to Jointly Estimate Model and Observation Error Covariance Matrices in Ensemble Data Assimilation. Mon. Wea. Rev., 148, 3973-3994. doi:10.1175/MWR-D-19-0240.1
  12. Kotsuki, S., A. Pensoneault, A. Okazaki and T. Miyoshi, 2020: Weight Structure of the Local Ensemble Transform Kalman Filter: A Case with an Intermediate AGCM. Quart. J. Roy. Meteorol. Soc., 146, 3399-3415. doi:10.1002/qj.3852
  13. Miyoshi, T., S. Kotsuki, K. Terasaki, S. Otsuka, G.-Y. Lien, H. Yashiro, H. Tomita, M. Satoh, and E. Kalnay, 2020: Precipitation Ensemble Data Assimilation in NWP Models. In: Levizzani V., Kidd C., Kirschbaum D., Kummerow C., Nakamura K., Turk F. (eds) Satellite Precipitation Measurement. Advances in Global Change Research, 69, Springer, 983-991. doi:10.1007/978-3-030-35798-6_25
  14. Necker, T., S. Geiss, M. Weissmann, J. Ruiz, T. Miyoshi, G.-Y. Lien, 2020: A convective-scale 1000-member ensemble simulation and potential applications. Quart. J. Roy. Meteor. Soc., 146, 1423-1442. doi:10.1002/qj.3744
  15. Necker, T., M. Weissmann, Y. Ruckstuhl, J. Anderson, and T. Miyoshi, 2020: Sampling error correction evaluated using a convective-scale 1000-member ensemble. Mon. Wea. Rev., 148, 1229–1249. doi:10.1175/MWR-D-19-0154.1
  16. Maejima, Y. and T. Miyoshi, 2020: Impact of the window length of four-dimensional local ensemble transform Kalman filter: a case of convective rain event. SOLA, 16, 37-42. doi:10.2151/sola.2020-007
  17. Amemiya, A., T. Honda, and T. Miyoshi, 2020: Improving the observation operator for the Phased Array Weather Radar in the SCALE-LETKF system. SOLA, 16, 6-11. doi:10.2151/sola.2020-002
  18. Kotsuki S., Y. Sato, and T. Miyoshi, 2020: Data Assimilation for Climate Research: Model Parameter Estimation of Large Scale Condensation Scheme. J. Geophys. Res., 125, e2019JD031304. doi:10.1029/2019JD031304

    - 2019 -
  19. Awazu, T., S. Otsuka, and T. Miyoshi, 2019: Verification of precipitation forecast by pattern recognition. J. Meteor. Soc. Japan, 97, 1173-1189. doi:10.2151/jmsj.2019-066
  20. Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi, 2019: GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation. J. Meteor. Soc. Japan, 97, 1099-1117. doi:10.2151/jmsj.2019-061
  21. Greybush, S. J., E. Kalnay, R. J. Wilson, R. N. Hoffman, T. Nehrkorn, M. Leidner, J. Eluszkiewicz, H. E. Gillespie, M. Wespetal, Y. Zhao, M. Hoffman, P. Dudas, T. McConnochie, A. Kleinboehl, D. Kass, D. McCleese, and T. Miyoshi, 2019: The Ensemble Mars Atmosphere Reanalysis System (EMARS) Dataset Version 1.0. Geoscience Data Journal, 6, 137-150. doi:10.1002/gdj3.77
  22. Kondo, K., and T. Miyoshi, 2019: Non-Gaussian statistics in global atmospheric dynamics: a study with a 10240-member ensemble Kalman filter using an intermediate AGCM. Nonlinear Processes in Geophys., 26, 211-225. doi:10.5194/npg-26-211-2019
  23. Kotsuki, S., K. Kurosawa, and T. Miyoshi, 2019: On the Properties of Ensemble Forecast Sensitivity to Observations. Quart. J. Roy. Meteorol. Soc., 145, 1897-1914. doi:10.1002/qj.3534
  24. Okazaki, A., T. Honda, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, and T. Miyoshi, 2019: Simulating precipitation radar observations from a geostationary satellite, Atmos. Meas. Tech., 12, 3985-3996. doi:10.5194/amt-12-3985-2019
  25. Kotsuki, S., K. Kurosawa, S. Otsuka, K. Terasaki and T. Miyoshi, 2019: Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights. Weather and Forecasting, 34, 701-714. doi:10.1175/WAF-D-18-0164.1
  26. Sawada, Y., K. Okamoto, M. Kunii, and T. Miyoshi, 2019: Assimilating every-10-minute Himawari-8 infrared radiances to improve convective predictability. J. Geophys. Res. Atmos., 124, 2546-2561. doi:10.1029/2018JD029643
  27. Terasaki, K., S. Kotsuki, and T. Miyoshi, 2019: Multi-year analysis using the NICAM-LETKF data assimilation system. SOLA, 15, 41-46. doi:10.2151/sola.2019-009
  28. Maejima, Y., T. Miyoshi, M. Kunii, H. Seko and K. Sato, 2019: Impact of Dense and Frequent Surface Observations on 1-minute-update Severe Rainstorm Prediction: A Simulation Study. J. Meteor. Soc. Japan, 97, 253-273. doi:10.2151/jmsj.2019-014
  29. Honda, T., S. Takino, and T. Miyoshi, 2019: Improving a precipitation forecast by assimilating all-sky Himawari-8 satellite radiances: A case of Typhoon Malakas (2016). SOLA, 15, 7-11. doi:10.2151/sola.2019-002
  30. Kotsuki, S., K. Terasaki, K. Kanemaru, M. Satoh, T. Kubota and T. Miyoshi, 2019: Predictability of Record-Breaking Rainfall in Japan in July 2018: Ensemble Forecast Experiments with the Near-real-time Global Atmospheric Data Assimilation System NEXRA. SOLA, 15A, 1-7. doi:10.2151/sola.15A-001

    - 2018 -
  31. Hatfield, S., P. Duben, M. Chantry, K. Kondo, T. Miyoshi and T. Palmer, 2018: Choosing the optimal numerical precision for data assimilation in the presence of model error. J. Adv. Modeling Earth Systems, 10, 2177-2191. doi:10.1029/2018MS001341
  32. Komori, N., T. Enomoto, T. Miyoshi, A. Yamazaki, A. Kuwano-Yoshida and B. Taguchi, 2018: Ensemble-based Atmospheric Reanalysis using a Global Coupled Atmosphere-Ocean GCM. Mon. Wea. Rev., 146, 3311-3323. doi:10.1175/MWR-D-17-0361.1
  33. Kotsuki, S., K. Terasaki, H. Yashiro, H. Tomita, M. Satoh and T. Miyoshi, 2018: Online Model Parameter Estimation with Ensemble Data Assimilation in the Real Global Atmosphere: A Case with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) and the Global Satellite Mapping of Precipitation Data. J. Geophys. Res., 123, 7375-7392. doi:10.1029/2017JD028092
  34. Lien, G.-Y., D. Hotta, E. Kalnay, T. Miyoshi, and T.-C. Chen, 2018: Accelerating assimilation development for new observing systems using EFSO. Nonlinear Processes in Geophys., 25, 129-143. doi:10.5194/npg-25-129-2018
  35. Honda, T., S. Kotsuki, G.-Y. Lien, Y. Maejima, K. Okamoto and T. Miyoshi*, 2018: Assimilation of Himawari-8 All-Sky Radiances Every 10 Minutes: Impact on Precipitation and Flood Risk Prediction. J. Geophys. Res., 123, 965-976. doi:10.1002/2017JD027096
  36. Sawada, Y., T. Nakaegawa and T. Miyoshi*, 2018: Hydrometeorology as an inversion problem: Can river discharge observations improve the atmosphere by ensemble data assimilation?. J. Geophys. Res., 123, 848-860. doi:10.1002/2017JD027531
  37. Honda, T., T. Miyoshi, G.-Y. Lien, S. Nishizawa, R. Yoshida, S. A. Adachi, K. Terasaki, K. Okamoto, H. Tomita and K. Bessho, 2018: Assimilating All-Sky Himawari-8 Satellite Infrared Radiances: A Case of Typhoon Soudelor (2015). Mon. Wea. Rev., 146, 213-229. doi:10.1175/MWR-D-16-0357.1

    - 2017 -
  38. Navarro, T., F. Forget, E. Millour, S. J. Greybush, E. Kalnay and T. Miyoshi, 2017: The challenge of atmospheric data assimilation on Mars. Earth and Space Sci., 4, 690-722. doi:10.1002/2017EA000274
  39. Hotta, D., E. Kalnay, Y. Ota and T. Miyoshi, 2017: EFSR: Ensemble Forecast Sensitivity to Observation Error Covariance. Mon. Wea. Rev., 145, 5015-5031. doi:10.1175/MWR-D-17-0122.1
  40. Kotsuki, S., S. J. Greybush and T. Miyoshi, 2017: Can we optimize the assimilation order in the serial ensemble Kalman filter? A study with the Lorenz-96 model. Mon. Wea. Rev., 145, 4977-4995. doi:10.1175/MWR-D-17-0094.1
  41. Terasaki, K. and T. Miyoshi, 2017: Assimilating AMSU-A Radiances with the NICAM-LETKF. J. Meteorol. Soc. Japan, 95, 433-446. doi:10.2151/jmsj.2017-028
  42. Maejima, Y., M. Kunii and T. Miyoshi, 2017: 30-second-update 100-m-mesh data assimilation experiments: a sudden local rain case in Kobe on September 11, 2014. SOLA, 13, 174-180. doi:10.2151/sola.2017-032
  43. Arakida, H., T. Miyoshi*, T. Ise, S. Shima and S. Kotsuki, 2017: Non-Gaussian data assimilation of satellite-based Leaf Area Index observations with an individual-based dynamic global vegetation model. Nonlinear Processes in Geophys., 24, 553-567. doi:10.5194/npg-24-553-2017
  44. Yang, S.-C., S.-H. Chen, K. Kondo, T. Miyoshi, Y.-C. Liou, Y.-L. Teng and H.-L. Chang, 2017: Multi-localization data assimilation for predicting heavy precipitation associated with a multi-scale weather system. J. Adv. Modeling Earth Systems, 9, 1684-1702. doi:10.1002/2017MS001009
  45. Hotta, D., T.-C. Chen, E. Kalnay, Y. Ota and T. Miyoshi, 2017: Proactive QC: a fully flow-dependent quality control scheme based on EFSO. Mon. Wea. Rev., 145, 3331–3354. doi:10.1175/MWR-D-16-0290.1
  46. J. Liao, B. Gerofi, G.-Y. Lien, T. Miyoshi, S. Nishizawa, H. Tomita, W. Liao, A. Choudhary and Y. Ishikawa, 2017: A Flexible I/O Arbitration Framework for netCDF based Big Data Processing Workflows on High-End Supercomputers, Concurrency and Computation: Practice and Experience, 29, e4161. doi:10.1002/cpe.4161
  47. Kotsuki, S., Y. Ota and T. Miyoshi, 2017: Adaptive covariance relaxation methods for ensemble data assimilation: Experiments in the real atmosphere. Quart. J. Roy. Meteorol. Soc., 143, 2001-2015. doi:10.1002/qj.3060
  48. Yamazaki, A., T. Enomoto, T. Miyoshi, A. Kuwano-Yoshida and N. Komori, 2017: Using observations near the poles in the AFES-LETKF data assimilation system. SOLA, 13, 41-46. doi:10.2151/sola.2017-008
  49. Kotsuki, S., T. Miyoshi*, K. Terasaki, G.-Y. Lien and E. Kalnay, 2017: Assimilating the Global Satellite Mapping of Precipitation Data with the Nonhydrostatic Icosahedral Atmospheric Model NICAM. J. Geophys. Res., 122, 631-650. doi:10.1002/2016JD025355
  50. Lien, G.-Y., T. Miyoshi, S. Nishizawa, R. Yoshida, H. Yashiro, S. A. Adachi, T. Yamaura and H. Tomita, 2017: The near-real-time SCALE-LETKF system: A case of the September 2015 Kanto-Tohoku heavy rainfall. SOLA, 13, 1-6. doi:10.2151/sola.2017-001

    - 2016 -
  51. Motesharrei, S., J. Rivas, E. Kalnay, G. R. Asrar, A. J. Busalacchi, R. F. Cahalan, M. A. Cane, R. R. Colwell, K. Feng, R. S. Franklin, K. Hubacek, F. Miralles-Wilhelm, T. Miyoshi, M. Ruth, R. Sagdeev, A. Shirmohammadi, J. Shukla, J. Srebric, V. M. Yakovenko and N. Zeng, 2016: Modeling Sustainability: Population, Inequality, Consumption, and Bidirectional Coupling of the Earth and Human Systems. National Sci. Rev., 3, 470-494. doi:10.1093/nsr/nww081
  52. Kondo, K. and T. Miyoshi, 2016: Impact of removing covariance localization in an ensemble Kalman filter: experiments with 10,240 members using an intermediate AGCM. Mon. Wea. Rev., 144, 4849-4865. doi:10.1175/MWR-D-15-0388.1
  53. Penny, S. G. and T. Miyoshi, 2016: A local particle filter for high-dimensional geophysical systems. Nonlin. Processes Geophys., 23, 391-405. doi:10.5194/npg-23-391-2016
  54. Miyoshi, T., G.-Y. Lien, S. Satoh, T. Ushio, K. Bessho, H. Tomita, S. Nishizawa, R. Yoshida, S. A. Adachi, J. Liao, B. Gerofi, Y. Ishikawa, M. Kunii, J. Ruiz, Y. Maejima, S. Otsuka, M. Otsuka, K. Okamoto and H. Seko, 2016: "Big Data Assimilation" toward Post-peta-scale Severe Weather Prediction: An Overview and Progress. Proc. of the IEEE, 104, 2155-2179. doi:10.1109/JPROC.2016.2602560
  55. Miyoshi, T., M. Kunii, J. Ruiz, G.-Y. Lien, S. Satoh, T. Ushio, K. Bessho, H. Seko, H. Tomita and Y. Ishikawa, 2016: "Big Data Assimilation" Revolutionizing Severe Weather Prediction. Bull. Amer. Meteor. Soc., 97, 1347-1354. doi:10.1175/BAMS-D-15-00144.1
  56. Liao, J., B. Gerofi, G.-Y. Lien, S. Nishizawa, T. Miyoshi, H. Tomita and Y. Ishikawa, 2016: Toward a General I/O Arbitration Framework for netCDF based Big Data Processing. The 22nd International European Conference on Parallel and Distributed Computing (Euro-par 2016), LNCS 9833, 293-305. doi:10.1007/978-3-319-43659-3_22
  57. Otsuka, S., S. Kotsuki and T. Miyoshi, 2016: Nowcasting with data assimilation: a case of Global Satellite Mapping of Precipitation. Weather and Forecasting, 31, 1409-1416. doi:10.1175/WAF-D-16-0039.1
  58. Yashiro, H., K. Terasaki, T. Miyoshi and H. Tomita, 2016: Performance evaluation of throughput-aware framework for ensemble data assimilation: The case of NICAM-LETKF. Geosci. Model Devel., 9, 2293-2300. doi:10.5194/gmd-9-2293-2016
  59. Sluka, T., S. Penny, E. Kalnay and T. Miyoshi, 2016: Assimilating Atmospheric Observations into the Ocean Using Strongly Coupled Ensemble Data Assimilation. Geophys. Res. Lett., 43, 752-759. doi:10.1002/2015GL067238
  60. Hattori, M., J. Matsumoto, S. Ogino, T. Enomoto and T. Miyoshi, 2016: The Impact of Additional Radiosonde Observations on the Analysis of Disturbances in the South China Sea during VPREX2010. SOLA, 12, 75-79. doi:10.2151/sola.2016-018
  61. Otsuka, S., G. Tuerhong, R. Kikuchi, Y. Kitano, Y. Taniguchi, J. J. Ruiz, S. Satoh, T. Ushio and T. Miyoshi, 2016: Precipitation Nowcasting with Three-Dimensional Space–Time Extrapolation of Dense and Frequent Phased-Array Weather Radar Observations. Weather and Forecasting, 31, 329-340. doi:10.1175/WAF-D-15-0063.1
  62. Dillon, M. E., Y. G. Skabar, J. Ruiz, E. Kalnay, E. A. Collini, P. Echevarría, M. Saucedo, T. Miyoshi and M. Kunii, 2016: Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics. Weather and Forecasting, 31, 217-236. doi:10.1175/WAF-D-14-00157.1
  63. Lien, G.-Y., E. Kalnay, T. Miyoshi and G. J. Huffman, 2016: Statistical properties of global precipitation in the NCEP GFS model and TMPA observations for data assimilation. Mon. Wea. Rev., 144, 663-679. doi:10.1175/MWR-D-15-0150.1
  64. Lien, G.-Y., T. Miyoshi and E. Kalnay, 2016: Assimilation of TRMM Multisatellite Precipitation Analysis with a low-resolution NCEP Global Forecasting System. Mon. Wea. Rev., 144, 643-661. doi:10.1175/MWR-D-15-0149.1

    - 2015 -
  65. Seko, H., M. Kunii, S. Yokota, T. Tsuyuki and T. Miyoshi, 2015: Ensemble experiments using a nested LETKF system to reproduce intense vortices associated with tornadoes of 6 May 2012 in Japan. Progress in Earth and Planetary Science, 2:42, doi:10.1186/s40645-015-0072-3.
  66. Miyoshi, T., K. Kondo and K. Terasaki, 2015: Big Ensemble Data Assimilation in Numerical Weather Prediction. Computer, 48, 15-21. doi:10.1109/MC.2015.332
  67. Otsuka, S. and T. Miyoshi, 2015: A Bayesian Optimization Approach to Multi-Model Ensemble Kalman Filter with a Low-Order Model. Mon. Wea. Rev., 143, 2001-2012. doi:10.1175/MWR-D-14-00148.1
  68. Ruiz, J. J., T. Miyoshi*, S. Satoh and T. Ushio, 2015: A Quality Control Algorithm for the Osaka Phased Array Weather Radar. SOLA, 11, 48-52. doi:10.2151/sola.2015-011
  69. Kotsuki, S., H. Takenaka, K. Tanaka, A. Higuchi and T. Miyoshi, 2015: 1-km-resolution land surface analysis over Japan: Impact of satellite-derived solar radiation. Hydrological Res. Lett., 9, 14-19. doi:10.3178/hrl.9.14
  70. Sawada, M., T. Sakai, T. Iwasaki, H. Seko, K. Saito and T. Miyoshi, 2015: Assimilating high-resolution winds from a Doppler lidar using an ensemble Kalman filter with lateral boundary adjustment. Tellus, 67A, 23473. doi:10.3402/tellusa.v67.23473
  71. Terasaki, K., M. Sawada and T. Miyoshi, 2015: Local Ensemble Transform Kalman Filter Experiments with the Nonhydrostatic Icosahedral Atmospheric Model NICAM. SOLA, 11, 23-26. doi:10.2151/sola.2015-006

    - 2014 -
  72. Terasaki, K. and T. Miyoshi, 2014: Data Assimilation with Error-correlated and Non-orthogonal Observations: Experiments with the Lorenz-96 Model. SOLA, 10, 210-213. doi:10.2151/sola.2014-044
  73. Kotsuki, S., K. Terasaki and T. Miyoshi, 2014: GPM/DPR Precipitation Compared with a 3.5-km-resolution NICAM Simulation. SOLA, 10, 204-209. doi:10.2151/sola.2014-043
  74. Matsuoka, S., H. Sato, O. Tatebe, F. Takatsu, M. A. Jabri, M. Koibuchi, I. Fujiwara, S. Suzuki, M. Kakuta, T. Ishida, Y. Akiyama, T. Suzumura, K. Ueno, H. Kanezashi and T. Miyoshi, 2014: Extreme Big Data (EBD): Next Generation Big Data Infrastructure Technologies Towards Yottabyte/Year. Supercomputing Frontiers and Innovations, 1, No.2, 89-107. doi:10.14529/jsfi140206
  75. Satoh, M., H. Tomita, H. Yashiro, H. Miura, C. Kodama, T. Seiki, A. Noda, Y. Yamada, D. Goto, M. Sawada, T. Miyoshi, Y. Niwa, M. Hara, T. Ohno, S. Iga, T. Arakawa, T. Inoue and H. Kubokawa, 2014: The non-hydrostatic icosahedral atmospheric model: description and development. Progress in Earth and Planetary Science, 1:18, doi:10.1186/s40645-014-0018-1.
  76. Yoden, S., K. Ishioka, D. Durran, T. Enomoto, Y. Hayashi, T. Miyoshi and M. Yamada, 2014: Theoretical Aspects of Variability and Predictability in Weather and Climate Systems. Bull. Amer. Meteor. Soc., 95, 1101-1104. doi:10.1175/BAMS-D-14-00009.1
  77. Miyoshi, T., K. Kondo and T. Imamura, 2014: The 10240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, 5264-5271, doi:10.1002/2014GL060863.
  78. Yoshimura, K., T. Miyoshi and M. Kanamitsu, 2014: Observation System Simulation Experiments Using Water Vapor Isotope Information. J. Geophys. Res., 119, doi:10.1002/2014JD021662.
  79. Cecelski, S. F., D.-L. Zhang and T. Miyoshi, 2014: Genesis of Hurricane Julia (2010) within an African Easterly Wave: Developing and Non-Developing Members from WRF-LETKF Ensemble Forecasts. J. Atmos. Sci., 71, 2763-2781. doi:10.1175/JAS-D-13-0187.1

    - 2013 -
  80. Penny, S. G., E. Kalnay, J. A. Carton, B. R. Hunt, K. Ide, T. Miyoshi and G. A. Chepurin, 2013: The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model. Nonlin. Processes Geophys., 20, 1031-1046. doi:10.5194/npg-20-1031-2013
  81. Kondo, K., T. Miyoshi and H. L. Tanaka, 2013: Parameter sensitivities of the dual-localization approach in the local ensemble transform Kalman filter. SOLA, 9, 174-177. doi:10.2151/sola.2013-039
  82. Miyoshi, T. and K. Kondo, 2013: A multi-scale localization approach to an ensemble Kalman filter. SOLA, 9, 170-173. doi:10.2151/sola.2013-038
  83. Yang, S.-C., K.-J. Lin, T. Miyoshi and E. Kalnay, 2013: Improving the spin-up of regional EnKF for typhoon assimilation and forecasting with Typhoon Sinlaku (2008). Tellus, 65A, 20804. doi:10.3402/tellusa.v65i0.20804
  84. Ruiz, J. J., M. Pulido and T. Miyoshi, 2013: Estimating model parameters with ensemble-based data assimilation: Parameter covariance treatment. J. Meteorol. Soc. Japan, 91, 453-469. doi:10.2151/jmsj.2013-403
  85. Ota, Y., J. C. Derber, E. Kalnay and T. Miyoshi, 2013: Ensemble-based observation impact estimates using the NCEP GFS. Tellus, 65A, 20038. doi:10.3402/tellusa.v65i0.20038
  86. Lien, G.-Y., E. Kalnay and T. Miyoshi, 2013: Effective Assimilation of Global Precipitation: Simulation Experiments. Tellus, 65A, 19915. doi:10.3402/tellusa.v65i0.19915
  87. Enomoto, T., T. Miyoshi, Q. Moteki, J. Inoue, M. Hattori, A. Kuwano-Yoshida, N. Komori and S. Yamane, 2013: Observing-system research and ensemble data assimilation at JAMSTEC. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II), ed. by S. K. Park and L. Xu, chap. 21, 509-526, Springer, doi:10.1007/978-3-642-35088-7_21.
  88. Seko, H., T. Tsuyuki, K. Saito and T. Miyoshi, 2013: Development of a Two-way Nested LETKF System for Cloud-resolving Model. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II), ed. by S. K. Park and L. Xu, chap. 20, 489-508, Springer, doi:10.1007/978-3-642-35088-7_20.
  89. Ruiz, J. J., M. Pulido and T. Miyoshi, 2013: Estimating model parameters with ensemble-based data assimilation: A review. J. Meteorol. Soc. Japan, 91, 79-99. doi:10.2151/jmsj.2013-201
  90. Miyoshi, T., E. Kalnay and H. Li, 2013: Estimating and including observation-error correlations in data assimilation. Inverse Problems in Science and Engineering, 21, 387-398. doi:10.1080/17415977.2012.712527

    - 2012 -
  91. Kunii, M. and T. Miyoshi, 2012: Including uncertainties of sea surface temperature in an ensemble Kalman filter: a case study of Typhoon Sinlaku (2008). Weather and Forecasting, 27, 1586-1597. doi:10.1175/WAF-D-11-00136.1
  92. Kang, J.-S., E. Kalnay, T. Miyoshi, J. Liu and I. Fung, 2012: Estimation of surface carbon fluxes with an advanced data assimilation methodology. J. Geophys. Res., 117, D24101. doi:10.1029/2012JD018259
  93. Greybush, S. J., R. J. Wilson, R. N. Hoffman, M. J. Hoffman, T. Miyoshi, K. Ide, T. McConnochie and E. Kalnay, 2012: Ensemble Kalman Filter Data Assimilation of Thermal Emission Spectrometer (TES) Temperature Retrievals into a Mars GCM. J. Geophys. Res., 117, E11008. doi:10.1029/2012JE004097
  94. Hoffman, M. J., T. Miyoshi, T. Haine, K. Ide, C. W. Brown and R. Murtugudde, 2012: An Advanced Data Assimilation System for the Chesapeake Bay: Performance Evaluation. J. Atmos. Oceanic Tech., 29, 1542-1557. doi:10.1175/JTECH-D-11-00126.1
  95. Kalnay, E., Y. Ota, T. Miyoshi and J. Liu, 2012: A Simpler Formulation of Forecast Sensitivity to Observations: Application to Ensemble Kalman Filters. Tellus, 64A, 18462. doi:10.3402/tellusa.v64i0.18462
  96. Miyoshi, T. and M. Kunii, 2012: Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus, 64A, 18408. doi:10.3402/tellusa.v64i0.18408
  97. Yang, S.-C., E. Kalnay and T. Miyoshi, 2012: Accelerating the EnKF Spinup for Typhoon Assimilation and Prediction. Weather and Forecasting, 27, 878-897. doi:10.1175/WAF-D-11-00153.1
  98. Kunii, M., T. Miyoshi and E. Kalnay, 2012: Estimating the Impact of Real Observations in Regional Numerical Weather Prediction Using an Ensemble Kalman Filter. Mon. Wea. Rev., 140, 1975-1987. doi:10.1175/MWR-D-11-00205.1
  99. Miyoshi, T. and M. Kunii, 2012: The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations. Pure and Appl. Geophys., 169, 321-333. doi:10.1007/s00024-011-0373-4
  100. Saito, K., H. Seko, M. Kunii and T. Miyoshi, 2012: Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction. Tellus, 64A, 11594. doi:10.3402/tellusa.v64i0.11594

    - 2011 -
  101. Kang, J.-S., E. Kalnay, J. Liu, I. Fung, T. Miyoshi and K. Ide, 2011: "Variable localization" in an Ensemble Kalman Filter: application to the carbon cycle data assimilation. J. Geophys. Res., 116, D09110. doi:10.1029/2010JD014673
  102. Miyoshi, T., 2011: The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1519-1535. doi:10.1175/2010MWR3570.1
  103. Moteki, Q., K. Yoneyama, R. Shirooka, H. Kubota, K. Yasunaga, J. Suzuki, A. Seiki, N. Sato, T. Enomoto, T. Miyoshi and S. Yamane, 2011: The influence of observations propagated by convectively coupled equatorial waves. Quart. J. Roy. Meteor. Soc., 137, 641-655. doi:10.1002/qj.779
  104. Seko, H., T. Miyoshi, Y. Shoji and K. Saito, 2011: Data Assimilation Experiments of Precipitable Water Vapor using the LETKF System: Intense Rainfall Event over Japan 28 July 2008. Tellus, 63A, 402-414. doi:10.1111/j.1600-0870.2010.00508.x
  105. Greybush, S. J., E. Kalnay, T. Miyoshi, K. Ide and B. R. Hunt, 2011: Balance and Ensemble Kalman Filter Localization Techniques. Mon. Wea. Rev., 139, 511-522. doi:10.1175/2010MWR3328.1
  106. Sekiyama, T. T., M. Deushi and T. Miyoshi, 2011: Operation-Oriented Ensemble Data Assimilation of Total Column Ozone. SOLA, 7, 41-44. doi:10.2151/sola.2011-011

    - 2010 -
  107. Miyoshi, T., T. Komori, H. Yonehara, R. Sakai and M. Yamaguchi, 2010: Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts. Weather and Forecasting, 25, 1568-1573. doi:10.1175/2010WAF2222392.1
  108. Hoffman, M. J., S. J. Greybush, R. J. Wilson, G. Gyarmati, R. N. Hoffman, E. Kalnay, K. Ide, E. J. Kostelich, T. Miyoshi and I. Szunyogh, 2010: An Ensemble Kalman Filter Data Assimilation System for the Martian Atmosphere: Implementation and Simulation Experiments. Icarus, 209, 470-481. doi:10.1016/j.icarus.2010.03.034
  109. Schutgens, N. A. J., T. Miyoshi, T. Takemura and T. Nakajima, 2010: Sensitivity tests for an ensemble Kalman filter for aerosol assimilation. Atmos. Chem. Phys., 10, 6583-6600. doi:10.5194/acp-10-6583-2010 (Discussion Paper in ACPD)
  110. Miyoshi, T., Y. Sato and T. Kadowaki, 2010: Ensemble Kalman filter and 4D-Var inter-comparison with the Japanese operational global analysis and prediction system. Mon. Wea. Rev., 138, 2846-2866. doi:10.1175/2010MWR3209.1
  111. Enomoto, T., M. Hattori, T. Miyoshi and S. Yamane, 2010: Precursory signals in analysis ensemble spread. Geophys. Res. Lett., 37, L08804. doi:10.1029/2010GL042723
  112. Schutgens, N. A. J., T. Miyoshi, T. Takemura and T. Nakajima, 2010: Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model. Atmos. Chem. Phys., 10, 2561-2576. doi:10.5194/acp-10-2561-2010 (Discussion Paper in ACPD)
  113. Sekiyama, T. T., T. Y. Tanaka, A. Shimizu and T. Miyoshi, 2010: Data assimilation of CALIPSO aerosol observations. Atmos. Chem. Phys., 10, 39-49. doi:10.5194/acp-10-39-2010 (Discussion Paper in ACPD)

    - 2009 -
  114. Liu, J., E. Kalnay, T. Miyoshi and C. Cardinali, 2009: Analysis sensitivity calculation in an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 1842-1851. doi:10.1002/qj.511
  115. Li, H., E. Kalnay, T. Miyoshi and C. M. Danforth, 2009: Accounting for Model Errors in Ensemble Data Assimilation. Mon. Wea. Rev., 137, 3407-3419. doi:10.1175/2009MWR2766.1
  116. Inoue, J., T. Enomoto, T. Miyoshi and S. Yamane, 2009: Impact of observations from Arctic drifting buoys on the reanalysis of surface fields. Geophys. Res. Lett., 36, L08501. doi:10.1029/2009GL037380
  117. Li, H., E. Kalnay and T. Miyoshi, 2009: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter. Quart. J. Roy. Meteor. Soc., 135, 523-533. doi:10.1002/qj.371
  118. Yang, S-C., M. Corazza, A. Carrassi, E. Kalnay and T. Miyoshi, 2009: Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model. Mon. Wea. Rev., 137, 693-709. doi:10.1175/2008MWR2396.1

    - 2008 -
  119. Miyoshi, T. and T. Kadowaki, 2008: Accounting for Flow-dependence in the Background Error Variance within the JMA Global Four-dimensional Variational Data Assimilation System. SOLA, 4, 37-40. doi:10.2151/sola.2008-010

    - 2007 -
  120. Miyoshi, T. and S. Yamane, 2007: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. Mon. Wea. Rev., 135, 3841-3861. doi:10.1175/2007MWR1873.1
  121. Miyoshi, T., S. Yamane and T. Enomoto, 2007: Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF). SOLA, 3, 89-92. doi:10.2151/sola.2007-023
  122. Kalnay, E., H. Li, T. Miyoshi, S-C. Yang and J. Ballabrera, 2007: 4-D-Var or Ensemble Kalman Filter?. Tellus, 59A, 758-773. doi:10.1111/j.1600-0870.2007.00261.x
  123. Kalnay, E., H. Li, T. Miyoshi, S-C. Yang and J. Ballabrera, 2007: Response to the discussion on "4-D-Var or EnKF?" by Nils Gustafsson. Tellus, 59A, 778-780. doi:10.1111/j.1600-0870.2007.00263.x
  124. Tsuyuki, T. and T. Miyoshi, 2007: Recent Progress of Data Assimilation Methods in Meteorology. J. Meteor. Soc. Japan, 85(2B), 331-361. doi:10.2151/jmsj.85B.331
  125. Miyoshi, T., S. Yamane and T. Enomoto, 2007: The AFES-LETKF Experimental Ensemble Reanalysis: ALERA. SOLA, 3, 45-48. doi:10.2151/sola.2007-012
  126. Miyoshi, T. and Y. Sato, 2007: Assimilating Satellite Radiances with a Local Ensemble Transform Kalman Filter (LETKF) Applied to the JMA Global Model (GSM). SOLA, 3, 37-40. doi:10.2151/sola.2007-010
  127. Danforth, C. M., E. Kalnay and T. Miyoshi, 2007: Estimating and Correcting Global Weather Model Error. Mon. Wea. Rev., 135, 281-299. doi:10.1175/MWR3289.1

    - 2006 -
  128. Miyoshi, T. and K. Aranami, 2006: Applying a Four-dimensional Local Ensemble Transform Kalman Filter (4D-LETKF) to the JMA Nonhydrostatic Model (NHM). SOLA, 2, 128-131. doi:10.2151/sola.2006-033

    - 2005 -
  129. Miyoshi, T., 2005: Ensemble Kalman filter experiments with a primitive-equation global model. Ph.D. dissertation, University of Maryland, College Park, 197pp. Available PDF HERE

Journal papers in Japanese

  1. 三好建正, 2021: スパコンが拓く次世代天気予報の新展開~観測値と予測値の相乗効果でゲリラ豪雨も予測可能に~. MDB 技術予測レポート, in press.
  2. 三好建正, 2021: ビッグデータとスーパーコンピュータによる豪雨予測~世界最先端「ビッグデータ同化」の気象予測研究~. 難病看護学会誌, 25(No.3), in press.
  3. 佐藤 正樹, 川畑 拓矢, 宮川 知己, 八代 尚, 三好 建正, 2021: 「富岳」による新時代の大アンサンブル気象・大気環境予測. 繊維学会誌, 77(No.2), 54-58.
  4. 三好建正, 2020: ゲリラ豪雨予報のリアルタイム実証実験. スーパーコンピューティングニュース, 22(No.5), 14-17. 2020年9月 (PDF)
  5. 三好建正, 2020: フェイズドアレー気象レーダを用いた超高速降水予報. 電子情報通信学会誌, 103(No.9), 924-930. 2020年9月号
  6. 三好建正, 2020: ビッグデータ同化:気象学における先端データ同化研究. 計測と制御, 59, 541-545. 2020年8月号
  7. 三好建正, 2019: 次世代スーパーコンピューターとビッグデータが拓く未来の気象予測. 天気, 66, 675-679. (PDF)
  8. 三好建正, 2019: 気象予測研究の最前線. FBNews, No.506, 2019年2月, 1-5. (PDF)
  9. 三好建正, 2019: データ同化研究:ゲリラ豪雨予測からその先へ. ながれ, 38(No.1), 3-7. (PDF)
  10. 三好建正, 2019: 気象学におけるデータ同化研究の最前線. 計算工学, 24(no.1), 5-8. 2019年1月
  11. 三好建正, 2018: スーパーコンピュータ「京」と最新鋭気象レーダを活用したゲリラ豪雨予測. 水環境学会誌, 41(No.2), 57-60.
  12. 三好建正, 2018: 発想のたまご「発想が生まれるとき」. 水文・水資源学会誌, 31(No.1), 44-45.
  13. 三好建正, 2017: 「ゲリラ豪雨」を予測する~天気予報研究の最前線. 科学EYES, 神奈川県川崎図書館, 58(No.2), 1-8. 2017年3月
  14. 三好建正, 2017: アンサンブルカルマンフィルタの基礎と応用 「京」を使った最先端研究の紹介. 号外海洋, 海洋出版株式会社, 59, 28-36. 2017年3月
  15. 三好建正, 2017: 「京」を最新鋭気象レーダを活用したゲリラ豪雨予測手法. 技術総合誌[オーム], 104(第3号), 67-69. 2017年3月
  16. 三好建正, 2015: データ同化の進展と拡がり. Japan Geoscience Letters, 11(No.4), 1-3. (PDF)
  17. 牛山朋來・佐山敬洋・岩見洋一・三好建正, 2014: 2011年台風12号・15号を対象としたアンサンブル降雨流出予測実験. 河川技術論文集, 第20巻, 455-460. 2014年6月
  18. 三好建正・大塚成徳・小守信正・露木義・榎本剛, 2013: 「AICS国際データ同化ワークショップ」開催報告. 天気, 60, 731-735.
  19. 牛山朋來・佐山敬洋・藤岡奨・建部祐哉・深見和彦・三好建正, 2013: アンサンブルカルマンフィルターを用いた2011年台風12号・15号の降雨流出予測実験, 河川技術論文集, 18, 319-324.
  20. 芳村圭・三好建正・金光正郎, 2013: アンサンブルカルマンフィルタを用いた水同位体比データ同化に向けた理想化実験. 土木学会論文集B1(水工学), 69, 1795-1800.
  21. 大野木和敏・原田やよい・古林慎哉・釜堀弘隆・小林ちあき・遠藤洋和・石橋俊之・久保田雅久・芳村圭・三好建正・小守信正・大島和裕, 2012: 第4回WCRP再解析国際会議報告. 天気, 59, 1007-1016. Available online HERE
  22. 三好建正, 2011: アメリカ大学院留学のススメ. 天気, 58, 1091-1095. Available online HERE
  23. 里村雄彦・竹見哲也・野田暁・三好建正・富田浩之・斉藤和雄・日下博幸・重尚一, 2011: 第1回非静力学数値モデルに関する国際ワークショップの報告. 天気, 58, 249-256. Available online HERE
  24. 三好建正, 2010: 雲のデータ同化. 科学, 80, 927-928. Available online HERE
  25. 三好建正, 2010: データ同化への誘い. 天気, 57, 208-211. Available online HERE
  26. 三好建正, 2009: 「1+1>2」―2008年度山本・正野論文賞受賞記念講演―. 天気, 56, 315-323. Available online HERE
  27. 三好建正, 2009: 「4次元変分法とアンサンブル・カルマンフィルタの相互比較に関するワークショップ」及び「データ同化集中コース」参加報告. 天気, 56, 75-82. Available online HERE
  28. 三好建正, 2008: 「第3回アンサンブルデータ同化に関するワークショップ」参加報告. 天気, 55, 591-598. Available online HERE
  29. 余田成男・中澤哲夫・山口宗彦・竹内義明・木本昌秀・榎本剛・岩崎俊樹・向川均・松枝未遠・茂木耕作・三好建正・新野宏・斉藤和雄・瀬古弘・小司禎教, 2008: 日本における顕著現象の予測可能性研究. 天気, 55, 117-126. Available online HERE
  30. 三好建正・本田有機, 2007: 気象学におけるデータ同化. 天気, 54, 287-290. Available online HERE
  31. 秋山博子・有本昌弘・植村立・大石龍太・財城真寿美・佐藤友徳・大楽浩司・田口正和・東塚知己・豊田隆寛・長島佳菜・長野宇規・西澤誠也・西田哲・堀正岳・三好建正・安中さやか・山口耕生・山田和芳・吉川知里・渡邉英嗣, 2006: 日欧先端科学セミナー「気候変動」参加報告. 天気, 53, 913-918. Available online HERE
  32. 三好建正, 2005: アンサンブル・カルマンフィルタ―データ同化とアンサンブル予報の接点―. 天気, 52, 93-104. Available online HERE
  33. 三好建正, 2003: 「Atmospheric Modeling, Data Assimilation and Predictability」 E. Kalnay 著. 天気, 50, 929. Available online HERE

Book chapters

  1. Miyoshi, T., 2010: NHM-LETKF. Tech. Rep. MRI, 62, 159-163.
  2. 三好建正, 2008: カルマンフィルタ. 気象学におけるデータ同化, 気象研究ノート, 露木義・川畑拓也編, 第3章, 69-95.
  3. 三好建正, 2006: アンサンブル・カルマンフィルタ~データ同化との融合~. 数値予報課報告・別冊, 気象庁予報部, 第6章, 52, 80-99. Available PDF HERE
  4. 三好建正, 2003: 3次元変分法(JNoVA0)の開発. 数値予報課報告・別冊, 気象庁予報部, 第6.4節, 49, 148-155. Available PDF HERE

Non-refereed papers

  1. Miyoshi, T., Y. Sato, T. Kadowaki, M. Kazumori, R. Sakai, and M. Yamaguchi, 2008: Developments of a local ensemble transform Kalman filter with JMA global model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, in press. Available PDF HERE
  2. Miyoshi, T. and Y. Sato, 2007: Applying a local ensemble transform Kalman filter to the JMA global model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 36, 1-09. Available PDF HERE
  3. Miyoshi, T. and K. Aranami, 2007: Applying a local ensemble transform Kalman filter to the JMA nonhydrostatic model. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 36, 1-11. Available PDF HERE
  4. Miyoshi, T. and S. Yamane, 2006: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. CAS/JSC WGNE Research Activities in Atmospheric and Oceanic Modelling, 35, 1-19. Available PDF HERE
  5. Miyoshi, T., 2004: Classical Methods Tour of Advanced Data Assimilation using Lorenz '96 Model. Final Report of METO658E, 17pp. Available PDF HERE

Proceedings (Japanese)

  1. 三好建正・佐藤芳昭・門脇隆志, 2008: アンサンブルカルマンフィルタの誤差共分散膨張法の比較. 日本気象学会春季大会予稿集. Available PDF HERE
  2. 三好建正, 2007: 気象庁におけるアンサンブル・カルマンフィルタ開発. 平成19年度「異常気象と長期変動」研究集会報告. Available PDF HERE
  3. 三好建正・門脇隆志・佐藤芳昭, 2007: LETKFにおける衛星放射輝度観測データの最適バイアス補正法. 日本気象学会秋季大会予稿集, B215. Available PDF HERE
  4. 三好建正, 2007: 気象庁でのLETKF開発の近況. 第9回非静力学モデルに関するワークショップ講演予稿集. Available PDF HERE
  5. 三好建正・佐藤芳昭, 2007: 気象庁全球モデルを使ったLETKF実験. 日本気象学会春季大会予稿集, A207. Available PDF HERE
  6. 三好建正・山根省三・榎本剛, 2007: Local patchを使わない新しいLETKFの実装. 日本気象学会春季大会予稿集, P226. Available PDF HERE
  7. 三好建正・荒波恒平, 2006: 気象庁非静力学モデルを使った4次元アンサンブル・カルマンフィルタ. 第8回非静力学モデルに関するワークショップ講演予稿集, 19-20. Available PDF HERE
  8. 三好建正・山根省三・榎本剛, 2006: アンサンブル・カルマンフィルタによる実験的再解析. 平成18年度「異常気象と長期変動」研究集会報告, 6-12. Available PDF HERE
  9. 榎本剛・山根省三・三好建正, 2006:実験的再解析に向けたAFESの改良. 日本気象学会秋季大会予稿集, A204.
  10. 三好建正・山根省三・榎本剛, 2006: AFES-LETKFによる2005年5月以降の実験的再解析. 日本気象学会秋季大会予稿集, A205. Available PDF HERE
  11. 山根省三・三好建正・榎本剛・大淵済, 2006: 大気大循環モデルのアンサンブル実験に見られる擾乱の発展について. 日本気象学会秋季大会予稿集, A206.
  12. 三好建正・荒波恒平, 2006: 気象庁非静力学モデルを使った4次元アンサンブル・カルマンフィルタ. 日本気象学会秋季大会予稿集, A208. Available PDF HERE
  13. 三好建正・山根省三, 2006: 局所アンサンブル変換カルマンフィルタが提案する次世代の再解析プロダクト. 日本気象学会春季大会予稿集, B463. Available PDF HERE
  14. 三好建正・山根省三, 2006: 地球シミュレータを使った局所アンサンブル変換カルマンフィルタの完全モデル実験. 日本気象学会春季大会予稿集, D307. Available PDF HERE
  15. 三好建正 and E. Kalnay, 2005: 全球プリミティブ方程式モデルを使ったアンサンブル・カルマンフィルタ実験. 日本気象学会秋季大会予稿集, D155. Available PDF HERE
  16. 三好建正 and E. Kalnay, 2005: Breeding法のアンサンブル摂動生成におけるStochastic seedingの効果. 日本気象学会秋季大会予稿集, D156. Available PDF HERE
  17. 三好建正, 2003: 3次元変分法(JNoVA0)の毎時解析による非静力学モデル(NHM)の予報例-2003年1月27日の成田空港付近の局所前線-. 日本気象学会春季大会予稿集, B403. Available PDF HERE
  18. 三好建正, 2002: 気象庁における非静力学モデル用変分法データ同化システム(JNoVA)の開発. 第4回非静力学モデルに関するワークショップ講演予稿集. Available PDF HERE

Proceedings (excluding Japanese)

  1. Miyoshi, T. and M. Kunii, 2010: Development of the local ensemble transform Kalman filter with the WRF model. Extended abstract of the 1st International Workshop on Nonhydrostatic Numerical Models, September 2010, 2pp.
  2. Miyoshi, T., E. Kalnay, and H. Li, 2009: Estimation of observation error correlation and the treatment in ensemble Kalman filter. Extended abstract of the 5th WMO Symposium on Data Assimilation, October 2009, 8pp.
  3. Miyoshi, T., K. Ide, J. McWilliams, Z. Li, Y. Uchiyama, and E. Kalnay, 2009: Ensemble Data Assimilation for Idealized California Current System with ROMS-LETKF. Extended abstract of the 5th WMO Symposium on Data Assimilation, October 2009, 8pp.
  4. Miyoshi, T., S. Yamane, Y. Sato, K. Aranami, and T. Enomoto, 2007: Recent development of global and regional local ensemble transform Kalman filters (LETKF) at JMA. Extended abstract of the AMS annual meeting, January 2007, 4.3. Available PDF HERE
  5. Li, H., E. Kalnay, T. Miyoshi, and C. M. Danforth, 2007: Ensemble Kalman filter in the presence of model errors. Extended abstract of the AMS annual meeting, January 2007, 4.8.
  6. Kalnay, E., H. Li, and T. Miyoshi, 2007: Adaptive estimation of background and observation errors within local ensemble transform kalman filter. Extended abstract of the AMS annual meeting, January 2007, 7.2A.
  7. Miyoshi, T., S. Yamane, and T. Enomoto, 2006: Experimental reanalysis using AFES-LETKF at a T159/L48 resolution. Extended abstract of the Second THORPEX International Science Symposium (STISS), WMO/TD No. 1355, 154-155.
  8. Enomoto, T., S. Yamane, and T. Miyoshi, 2006: Simulations of the intra-seasonal oscillation in the tropics with ensemble techniques. Extended abstract of the Second THORPEX International Science Symposium (STISS), WMO/TD No. 1355, 84-85.
  9. Miyoshi, T. and S. Yamane, 2006: Local ensemble transform Kalman filtering with an AGCM at a T159/L48 resolution. EGU General Assembly 2006, Geophysical Research Abstracts, 8, 2468.
  10. Liu, J., E. Kalnay, and T. Miyoshi, 2006: Adaptive Observation Strategies with the Local Ensemble Transform Kalman Filter. Extended abstract of the AMS annual meeting, January 2006, 2.7. Available PDF HERE
  11. Li, H., E. Kalnay, T. Miyoshi, and C. M. Danforth, 2006: Estimation of Model Errors in the Local Ensemble Transform Kalman Filter. Extended abstract of the AMS annual meeting, January 2006, 6.2. Available PDF HERE
  12. Yang, S.-C., M. Corazza, A. Carrassi, E. Kalnay, and T. Miyoshi, 2006: Comparison of Ensemble-based and Variational-based Data Assimilation Schemes in a Quasi-geostrophic Model. Extended abstract of the AMS annual meeting, January 2006, 6.11. Available PDF HERE
  13. Miyoshi, T., 2003: JNoVA Project - JMA Nonhydrostatic Model based Variational Data Assimilation System -. Proceeding of International Workshop on NWP Models for Heavy Precipitation in Asia and Pacific Areas, Feb. 2003, JMA.
  14. Miyoshi, T., 2003: 3D-Var Control Variables Designed for the JMA Nonhydrostatic Model. Abstracts for the International Workshop on GPS Meteorology Extra Session, Jan. 2003, Meteorological Research Institute, Tsukuba, Japan.