Achievements

Achievements in FY2026

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Achievements in FY2025

  1. Almeida, A. P., H. M. J. Barbosa, S. R. Garcia, D. J. Gagne, K. Zhou, T. Kubota, T. Ushio, S. Otsuka, S. Pfreundschuh, and A. J. P. Calheiros, 2026: A regional benchmark for deep learning–based hourly precipitation nowcasting in Latin America. IEEE Access, 14, 38306-38331. doi:10.1109/ACCESS.2026.3670767
  2. Amemiya, A., and T. Miyoshi, 2026: Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30 s radar observation with ensemble Kalman filter: Idealized experiments of deep convection. Nonlin. Processes Geophys., 33, 1–16. doi:10.5194/npg-33-1-2026
  3. Chang, C., B. Mu, W. Han, Y. Ham, G. J. Zhang, A. Damiani, F. Ling, and T. Miyoshi, 2025: AI weather and climate prediction and applications. Bull. Amer. Meteor. Soc., 106, E2571–E2578. doi:10.1175/BAMS-D-25-0260.1
  4. Guerrieri, J. M., M. Pulido, T. Miyoshi, A. Amemiya, and J. J. Ruiz, 2026: Localization in the mapping particle filter. Nonlin. Processes Geophys., 33, 33–49. doi:10.5194/npg-33-33-2026
  5. Hascoet, T., V. Pellet, S. Oishi, and T. Miyoshi, 2026: Differentiable river routing for end-to-end learning of hydrological processes. J. Geophys. Res.: Machine Learning and Computation, 3, e2025JH000760. doi:10.1029/2025JH000760
  6. Huo, Z., Y. Liu, J. Taylor, Y. Zhou, A. Amemiya, H. Fan, and T. Miyoshi, 2025: Incremental analysis updates in a convective-scale ensemble Kalman filter using minute-by-minute phased array radar observations. J. Adv. Model. Earth Syst., 17, e2024MS004802. doi:10.1029/2024MS004802
  7. Konduru, R. T., R. Bale, M. Tsubokura, and T. Miyoshi, 2025: Transforming urban wind engineering by taming extreme weather strong winds over urban skylines with ultra-high-resolution simulations on supercomputer Fugaku. Proc. Supercomputing Asia Conf. (SCA ’25), 79–87. doi:10.1145/3718350.3718353
  8. Kotsuki, S., K. Shiraishi, and A. Okazaki, 2025: Ensemble data assimilation to diagnose AI-based weather prediction models: A case with ClimaX version 0.3.1. Geosci. Model Dev., 18, 7215–7225. doi:10.5194/gmd-18-7215-2025
  9. Matsugishi, S., Y.-W. Chen, K. Terasaki, H. Yashiro, S. Kotsuki, K. Kanemaru, K. Yamamoto, M. Satoh, T. Kubota, and T. Miyoshi, 2025: Intercomparison of NICAM–LETKF JAXA research analysis (NEXRA) version 2 and 3. SOLA, doi:10.2151/sola.2025-035
  10. Matsugishi, S., Y.-W. Chen, K. Terasaki, K. Kanemaru, S. Kotsuki, H. Yashiro, K. Yamamoto, M. Satoh, T. Kubota, and T. Miyoshi, 2025: NICAM–LETKF JAXA research analysis (NEXRA) version 2.0. Geosci. Data J., 12, e70011. doi:10.1002/gdj3.70011
  11. Miyoshi, T., 2026: A duality principle for chaotic systems: From data assimilation to efficient control. Nonlinear Dyn., 114, 105. doi:10.1007/s11071-025-12021-2
  12. Mulia, I., U. Shimada, N. Ueda, T. Miyoshi, and M. Maulana, 2025: Multi-horizon prediction of tropical cyclone intensity and its interpretability with temporal fusion transformer. Sci. Rep., 15. doi:10.1038/s41598-025-15522-7
  13. Ohishi, S., Y. Kobayashi, and T. Miyoshi, 2025: Including cross correlation between forecast and observation errors in an ensemble Kalman filter. Mon. Wea. Rev., 153, 1035–1043. doi:10.1175/MWR-D-25-0016.1
  14. Ohishi, S., T. Miyoshi, and M. Kachi, 2025: Deterministic and ensemble forecasts of the Kuroshio south of Japan. Ocean Dyn., 75, 92. doi:10.1007/s10236-025-01736-w
  15. Satoh, M., T. Kawabata, T. Miyakawa, M. Nakano, H. Yashiro, T. Miyoshi, L. Duc, P.-Y. Wu, T. Oizumi, Y. Maejima, J. Taylor, R. Yoshimura, K. Terasaki, Y. Yamada, R. Masunaga, T. Kawasaki, and M. Tanoue, 2025: Achievements in atmospheric sciences by the large-ensemble and high-resolution forecasting studies using the supercomputer Fugaku. Prog. Earth Planet. Sci., 12, 64. doi:10.1186/s40645-025-00730-6

Achievements in FY2024

  1. Furukawa, K., H. Sakamoto, M. Ohhigashi, S. Shima, T. Sluka, and T. Miyoshi, 2024: Particle filter data assimilation for ubiquitous unstable trajectories of two-dimensional three-state cellular automata, Nonlinear Dyn., 112, 21409-21424. doi:10.1007/s11071-024-09803-5
  2. Li, L., J. Li, and T. Miyoshi, 2025: Chaos suppression through Chaos enhancement, Nonlinear Dyn., 113, 3791-3800. doi:10.1007/s11071-024-10426-z
  3. Ohishi, S., T. Miyoshi, and M. Kachi, 2024: Impact of atmospheric forcing on SST in the LETKF-based ocean research analysis (LORA), Ocean Modelling, 189, 102357. doi:10.1016/j.ocemod.2024.102357
  4. Ohishi, S., T. Miyoshi, T. Ando, T. Higashiuwatoko, E. Yoshizawa, H. Murakami, and M. Kachi, 2024: LETKF-based Ocean Research Analysis (LORA) version 1.0, Geoscience Data Journal, 11, 995–1006. doi:10.1002/gdj3.271

Achievements in FY2023

  1. Kurosawa, K., S. Kotsuki, and T. Miyoshi, 2023: Comparative Study of Strongly and Weakly Coupled Data Assimilation with a Global Land-Atmosphere Coupled Model. Nonlin. Processes Geophys., 30, 457-479. doi: 10.5194/npg-30-457-2023
  2. Muto, Y., K. Kanemaru, and S. Kotsuki, 2023: Correcting GSMaP through histogram matching against satellite-borne radar-based precipitation. SOLA, 19, 217-224. doi:10.2151/sola.2023-028
  3. Oishi, K. and S. Kotsuki, 2023: Applying the Sinkhorn Algorithm for Resampling of Local Particle Filter. SOLA, 19, 185-193. doi:10.2151/sola.2023-024
  4. Ohishi, S., T. Miyoshi, and M. Kachi, 2023: LORA: A local ensemble transform Kalman filter-based ocean research analysis, Ocn. Dyn., 73, 117–143. doi:10.1007/s10236-023-01541-3
  5. Terasaki, K. and T. Miyoshi 2024: Including the horizontal observation error correlation in the ensemble Kalman filter: idealized experiments with NICAM-LETKF. Mon. Wea. Rev., 152, 277-293. doi:10.1175/MWR-D-23-0053.1

Achievements in FY2022

  1. Kotsuki, S., T. Miyoshi, K. Kondo, and R. Potthast, 2022: A Local Particle Filter and Its Gaussian Mixture Extension Implemented with Minor Modifications to the LETKF. Geosci. Model Dev., 15, 8325-8348. doi:10.5194/gmd-2022-69
  2. Ohishi, S., T. Hihara, H. Aiki, J. Ishizaka, Y. Miyazawa, M. Kachi, and T. Miyoshi, 2022: An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0, Geosci. Model Dev., 15, 8395–8410. doi:10.5194/gmd-15-8395-2022
  3. Jianyu, L., K. Terasaki, and M. Takemasa, 2023: A Machine Learning Approach to the Observation Operator for Satellite Radiance Data Assimilation. J. Meteorol. Soc. Japan, 101, 79-95. doi:10.2151/jmsj.2023-005
  4. Ohishi, S., T. Miyoshi, and M. Kachi, 2022: An ensemble Kalman filter-based ocean data assimilation system improved by adaptive observation error inflation (AOEI). Geosci. Model Dev., 15, 9057–9073. doi:10.5194/gmd-15-9057-2022
  5. Momoi, M., S. Kotsuki, R. Kikuchi, S. Watanabe, M. Yamada, and S. Abe, 2023: Emulating rainfall-runoff-inundation model using deep neural network with dimensionality reduction. Artificial Intelligence for the Earth Systems, 2 1-25. doi:10.1175/AIES-D-22-0036.1
  6. Kotsuki, S., K. Terasaki, M. Satoh, and T. Miyoshi, 2023: Ensemble-based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation with the Nonhydrostatic Icosahedral Atmospheric Model (NICAM). J. Geophys. Res. Atmos., 128, 5, e2022JD037447. doi:10.1029/2022JD037447
  7. Ohishi, S., T. Miyoshi, and M. Kachi, 2023: LORA: A local ensemble transform Kalman filter-based ocean research analysis, Ocn. Dyn., . doi:10.1007/s10236-023-01541-3

Achievements in FY2021

  1. Taylor, J., A. Okazaki, T. Honda, S. Kotsuki, M. Yamaji, T. Kubota, R. Oki, T. Iguchi, and 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, 13, 7. doi:10.1029/2020MS002332
  2. Honda, T., Y. Sato, and T. Miyoshi, 2021: Potential impacts of lightning flash observations on numerical weather prediction with explicit lightning processes, Journal of Geophysical Research: Atmospheres, 126, e2021JD034611. doi:10.1029/2021JD034611
  3. Miyoshi, T., K. Terasaki, S. Kotsuki, S. Otsuka, Y. W. Chen, K. Kanemaru, K. Okamoto, K. Kondo, G. Y. Lien, H. Yashiro, H. Tomita, M. Sato, and E. Kalnay, 2022: Enhancing data assimilation of GPM observations. In: Silas M. (Eds) Precipitation Science, Measurement Remote Sensing, Microphysics, and Modeling. Elsevier, 787-804. doi: 10.1016/B978-0-12-822973-6.00020-2
  4. Terasaki, K., and T. Miyoshi, 2022: A 1024-Member NICAM-LETKF Experiment for the July 2020 Heavy Rainfall Event. 18A, 8-14. doi:10.2151/sola.18A-002
  5. Terasaki, K., and T. Miyoshi, 2022: Ensemble Kalman Filter Experiments at 112-km and 28-km Resolution for the Record-Breaking Rainfall Event in Japan in July 2018. In: Park S.K., Xu L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV). Springer, Cham. 525-542. doi:10.1007/978-3-030-77722-7_20
  6. Kotsuki, S., and H. C. Bishop, 2022: Implementing Hybrid Background Error Covariance into the LETKF with Attenuation-based Localization: Experiments with a Simplified AGCM. Mon. Wea. Rev., 150, 283-302. doi:10.1175/MWR-D-21-0174.1

Achievements in FY2020

  1. 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
  2. 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. Satellite Precipitation Measurement. Advances in Global Change Research, Springer, 69, 983-991. doi:10.1007/978-3-030-35798-6_25
  3. Kotsuki, S., A. Pensoneault, A. Okazaki, and T. Miyoshi, 2020: Weight Structure of the Local Ensemble Transform Kalman Filter: A case with an intermediate atmospheric general circulation model. Q. J. R. Meteorol. Soc., 146, 3399-3415. doi:10.1002/qj.3852

Achievements in FY2019

  1. Awazu, T., S. Otsuka, and T. Miyoshi, 2019: Verification of precipitation forecast by pattern recognition. J. Meteorol. Soc. Japan, 97, 1173-1189. doi:10.2151/jmsj.2019-066
  2. Otsuka, S., S. Kotsuki, M. Ohhigashi, and T. Miyoshi, 2019: GSMaP RIKEN Nowcast: Global precipitation nowcasting with data assimilation. J. Meteorol. Soc. Japan, 97, 1099-1117. doi:10.2151/jmsj2019-061
  3. 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
  4. 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
  5. 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
  6. 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

Achievements in FY2018

  1. 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. Atmos., 123, 7375-7392. doi:10.1029/2017JD028092

Achievements in FY2017

  1. Kotsuki, S., S. J. Greybush, 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
  2. Terasaki, K., and T. Miyoshi, 2017: Assimilating AMSU-A Radiances with the NICAM-LETKF. J. Meteorol. Soc. Japan, 96, 433-446. doi:10.2151/jmsj.2017-028
  3. Kotsuki, S., Y. Ota, 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
  4. 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. Atmos., 122, 631-650. doi:10.1002/2016JD025355

Achievements in FY2016

  1. 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 Dev., 9, 2293-2300. doi:gmd-9-2293-2016
  2. 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
  3. Lien, G.-Y., E. Kalnay, T. Miyoshi, 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-15-0150.11
  4. Lien, G.-Y., T. Miyoshi, and E. Kalnay, 2016: Assimilation of TRMM Multisatellite Precipitation Analysis with a Low-Resolution NCEP Global Forecast System. Mon. Wea. Rev., 144, 643-661. doi:10.1175/MWR-D-15-0149.1

Achievements in FY2015

  1. 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

Achievements in FY2014

  1. 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
  2. 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

Achievements in FY2013

  1. Lien, G.-Y., E. Kalnay, and T. Miyoshi, 2013: Effective Assimilation of Global Precipitation: Simulation Experiments. Tellus, 65A, 11915. doi:10.3402/tellusa.v65i0.19915
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