Achievements in 2016

Peer-reviewed papers

  1. 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
  2. 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
  3. 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. J. Ruiz, Y. Maejima, S. Otsuka, M. Otsuka, K. Okamoto, and H. Seko, 2016: "Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress. Proceedings of the IEEE, vol. 104, no. 11, pp. 2155-2179. doi: 10.1109/JPROC.2016.2602560
  4. Sawada, Y. and T. Koike, 2016: Ecosystem resilience to the Millennium drought in southeast Australia (2001-2009). J. Geophys. Res. - Biogeosci., 121, 2312-2327. doi:10.1002/2016JG003356
  5. 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. Lecture Notes in Computer Science, 9833, 293-305. doi: 10.1007/978-3-319-43659-3_22
  6. 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
  7. Miyoshi, T., M. Kunii, J. 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
  8. Kobayashi, K., S. Otsuka, Apip, and K. Saito, 2016: Ensemble flood simulation for a small dam catchment in Japan using 10 and 2km resolution nonhydrostatic model rainfalls. Nat. Hazards Earth Syst. Sci., 16, 1821-1839. doi:10.5194/nhess-16-1821-2016
  9. Sawada, Y., and T. Koike, 2016: Towards ecohydrological drought monitoring and prediction using a land data assimilation system: a case study on the Horn of Africa drought (2010-2011). J. Geophys. Res. Atmos., 121, 8229-8242. doi:10.1002/2015JD024705
  10. Yashiro, H., K. Terasaki, T. Miyoshi, and H. Tomita, 2016: Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF. Geosci. Model Dev., 9, 2293-2300. doi:10.5194/gmd-9-2293-2016
  11. Schraff, C., H. Reich, A. Rhodin, A. Schomburg, K. Stephan, A. Periáñez, and R. Potthast, 2016: Kilometre-scale ensemble data assimilation for the COSMO model (KENDA). Q.J.R. Meteorol. Soc., 142: 1453-1472. doi: 10.1002/qj.2748
  12. Imamura, T., A. Watanabe and Y. Maejima, 2016: Convective generation and vertical propagation of fast gravity waves on Mars: One- and two-dimensional modeling. Icarus, 267, 51-63. doi:10.1016/j.icarus.2015.12.005
  13. 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
  14. Honda, T., and T. Kawano, 2016: A possible mechanism of tornadogenesis associated with the interaction between a supercell and an outflow boundary without horizontal shear. J. Atmos. Sic., 73, 1273-1292. doi:10.1175/JAS-D-14-0347.1
  15. 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
  16. 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
  17. Dillon, M. E., Y. G. Skabar, J. Ruiz, E. Kalnay, E. A. Collini, P. Echevarría, M. Saucedo, T. Miyoshi, and M. Kunii, 2015: 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
  18. 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
  19. 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
  20. Sueyoshi T., K. Saito, S. Miyazaki, J. Mori, T. Ise, H. Arakida, R. Suzuki, A. Sato, Y. Iijima, H. Yabuki, H. Ikawa, T. Ohta, A. Kotani, T. Hajima, H. Sato, T. Yamazaki, and A. Sugimoto, 2016: The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set. Earth Syst. Sci. Data, 8, 1-14. doi:10.5194/essd-8-1-2016

Invited Presentations

  1. Okazaki, A.: Basics of data assimilation and its application to paleoclimate. Kongju National University, Gongju, Korea, 20th October, 2016.
  2. Lien, G.-Y.: Assimilation of phased array radar data for short-range NWP using the SCALE-LETKF regional data assimilation system. Research Center for Environmental Changes, Academia Sinica, Taiwan, 11th October, 2016.
  3. Lien, G.-Y.: LETKF assimilation of dense radar data for short-range, fine-scale prediction of convective systems. Department of Atmospheric Sciences, National Taiwan University, Taiwan, 12th July 2016.
  4. Miyoshi, T.: Big Data, Supercomputing, and Data Assimilation. Data Assimilation Research Centre Meetings, Reading, UK, 11th July 2016.
  5. Miyoshi, T.: Big Data perspective on assimilating dense observations with spatially correlated errors. RMetS/NCAS Conference 2016 High Impact Weather and Climate, Manchester, UK, 6th July 2016.
  6. Miyoshi, T.: "Big Data Assimilation" revolutionaizing weather prediction. International HPC Summer School 2016, Ljubljana, Slovenia, 29th June 2016.
  7. [Keynote] Miyoshi, T.: "Big data assimilation" Revolutionizing weather prediction. Symposium on Advanced Assimilation and Uncertainly Quantification in BigData Research for Weather, Climate and Earth System Monitoring and Prediction, State College, Pennsylvania, USA, 24th May 2016.
  8. Kondo, K., T. Miyoshi: Non-Gaussian statistics and data assimilation in the global atmospheric dynamics with 10240-member ensemble Kalman filter. Japan Geoscience Union Meeting 2016, Chiba, 22th May 2016.
  9. Miyoshi, T.: "Big Data Assimilation" revolutionaizing weather prediction". JST-NSF Big Data Joint Workshop, Tokyo, 11th May 2016.
  10. Miyoshi, T.: Big Data Assimilation: Toward post­peta­scale supercomputing. Blueprints for Next-Generation Data Assimilation Systems, Boulder, 10th March 2016.
  11. Miyoshi, T., K. Kondo, K. Terasaki, M. Kunii, J. Ruiz, G.-Y. Lien, S. Satoh, T. Ushio, H. Tomita, Y. Ishikawa, K. Bessho and H. Seko: "Big data assimilation" revolutionizing numerical weather prediction. Third International Workshop on Tokyo Metropolitan Area Convection Study for Extreme Weather Resilient Cities (WMO/WWRP, TOMACS/RDP), Tokyo, 4th February 2016.
  12. Miyoshi, T.: Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements. JAXA Joint PI meeting of Global Environment Observation Mission FY2015 (PMM panel session), Tokyo, 22th January 2016.
  13. Miyoshi, T.: "Big Data Assimilation" Revolutionizing Numerical Weather Prediction". International Workshop on the Variations of East Asian Monsoon, Guangzhou, China,10th January 2016.

Honors and Awards

  1. The Most Accessed Paper Award 2016 (by Japan Geoscience Union), 22nd May 2016.
    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, 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
  2. Miyoshi, T.: Meteorological Society of Japan Award, 19th May 2016.
  3. Lien, G.-Y: 7th RIKEN Research Incentive Award, "Development of Precipitation Data Assimilation Method using Ensemble Kalman Filter". 30th March 2016.

Working with us

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Data Assimilation Research Team

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RIKEN Center for Computational Science (R-CCS) Data Assimilation Research Team

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