KONDO, Keiichi

[Japanese]
Job title Senior Researcher / The 3rd Lab. / Department of Observation and Data Assimilation Research
E-mail keiichi.kondo[at]mri-jma.go.jp
Personal
Expertise Data assimilation, Satellite Remote Sensing
Degree Doctor
Publications
  • Maki, T., K. Kondo, K. Ishijima, T.T. Sekiyama, K Tsuboi, T Nakamura, 2023: Independent Bias Correction Method for Satellite Observation Data Introduced to CO2 Flux Inversion. SOLA, 19, 157-164.
  • Kotsuki, Shunji, 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.
  • Tanaka, H.L., H. Nakamichi, K. Kondo, S. Akami, and M. Iguchi, 2022: Applying the Particle Filter to the Volcanic Ash Tracking PUFF Model for Assimilating Multi-Parameter Radar Observation. J. Disaster Res., 17, 791-804.
  • Ruiz, F., G. Lien, K. Kondo, S, Otsuka, and T. Miyoshi, 2021: Reduced non-Gaussianity by 30 s rapid update in convective-scale numerical weather prediction. Nonlin. Processes Geophys., 28, 615-626.
  • 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. Nonlin. Processes Geophys., 26, 211-225.
  • Hatfield, S., P. Duben, M. Chantry, K. Kondo, T. Miyoshi, 2018: Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error. J. Adv. Modeling Earth Systems, 10, 2177-2191.
  • 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.
  • Miyoshi, T., K. Kondo, and K. Terasaki, 2015: Numerical Weather Prediction with Big Ensemble Data Assimilation. Computer, 48, 15-21.
  • Kondo, K., and T. Miyoshi, 2016: Impact of removing covariance localization in an ensemble Kalman filter: experiments with 10240 members using an intermediate AGCM. Mon. Wea. Rev., 144, 4849-4865.
  • Miyoshi, T., K. Kondo, and T. Imamura, 2014: 10240-member ensemble Kalman filtering with an intermediate AGCM. Geophys. Res. Lett., 41, 5264-5271.
  • 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, 174–178.
  • Miyoshi, T., and K. Kondo, 2013: A multi-scale localization approach to an ensemble Kalman filter. SOLA, 9, 170-173.
  • Kondo, K., and H. L. Tanaka, 2009: Applying the local ensemble transform Kalman filter to the nonhydrostatic icosahedral atmospheric model (NICAM). SOLA, 5, 121-124.
  • Kondo, K., and H. L. Tanaka, 2009: Comparison of the extended Kalman filter and the ensemble Kalman filter using the barotropic general circulation model. J. Meteor. Soc. Japan, 87, 347-359.
Project