- 著者
- Koji Terasaki Shunji Kotsuki Takemasa Miyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.15, pp.41-46, 2019 (Released:2019-02-28)
- 参考文献数
- 29
- 被引用文献数
- 7
This study investigates the long-term stability of the global atmospheric data assimilation system, incorporating the Local Ensemble Transform Kalman Filter (LETKF) with the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The NICAM-LETKF system assimilates conventional observations, advanced microwave sounding unit–A (AMSU-A) radiances, and global satellite mapping of precipitation (GSMaP) data. The long-term stability of the data assimilation system can be investigated only by running an expensive long-term experiment. This study successfully performed a data assimilation experiment with more than 2 years of data, using the relaxation to prior spread (RTPS) method for covariance inflation. Analysis fields indicate a stable physical performance compared with the ERA-interim data for the entire experimental period.
- 著者
- Koji Terasaki Takemasa Miyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.18A, no.Special_Edition, pp.8-14, 2022 (Released:2022-04-02)
- 参考文献数
- 22
- 被引用文献数
- 5
This study investigated the predictability and causes of the heavy rainfall event that brought severe disasters in Kyushu in July 2020 with a global numerical weather prediction system composed of the NICAM (non-hydrostatic icosahedral atmospheric model) and the LETKF (local ensemble transform Kalman filter). We performed ensemble data assimilation and forecast experiments using the NICAM-LETKF system with 1,024 members and 56-km horizontal resolution on the supercomputer Fugaku. The results showed that 1,024-member ensemble forecasts captured the probability of heavy rainfall in Kyushu about five days before it happens, although a 10-day-lead forecast is difficult. Ensemble-based lag-correlation analyses with the 1024-member ensemble showed very small sampling errors in the correlation patterns and showed that the moist air inflow in the lower troposphere associated with a low-pressure anomaly over the Baiu front was related to this heavy rainfall in Kyushu.
- 著者
- Koji Terasaki Takemasa Miyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.18A-002, (Released:2022-02-08)
- 被引用文献数
- 5
This study investigated the predictability and causes of the heavy rainfall event that brought severe disasters in Kyushu in July 2020 with a global numerical weather prediction system composed of the NICAM (non-hydrostatic icosahedral atmospheric model) and the LETKF (local ensemble transform Kalman filter). We performed ensemble data assimilation and forecast experiments using the NICAM-LETKF system with 1,024 members and 56-km horizontal resolution on the supercomputer Fugaku. The results showed that 1,024-member ensemble forecasts captured the probability of heavy rainfall in Kyushu about five days before it happens, although a 10-day-lead forecast is difficult. Ensemble-based lag-correlation analyses with the 1024-member ensemble showed very small sampling errors in the correlation patterns and showed that the moist air inflow in the lower troposphere associated with a low-pressure anomaly over the Baiu front was related to this heavy rainfall in Kyushu.
- 著者
- Koji Terasaki Shunji Kotsuki Takemasa Miyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- pp.2019-009, (Released:2019-01-31)
- 被引用文献数
- 7
This study investigates the long-term stability of the global atmospheric data assimilation system, incorporating the Local Ensemble Transform Kalman Filter (LETKF) with the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The NICAM-LETKF system assimilates conventional observations, advanced microwave sounding unit–A (AMSU-A) radiances, and global satellite mapping of precipitation (GSMaP) data. The long-term stability of the data assimilation system can be investigated only by running an expensive long-term experiment. This study successfully performed a data assimilation experiment with more than 2 years of data, using the relaxation to prior spread (RTPS) method for covariance inflation. Analysis fields indicate a stable physical performance compared with the ERA-interim data for the entire experimental period.
- 著者
- Shunji Kotsuki Koji Terasaki Kaya Kanemaru Masaki Satoh Takuji Kubota Takemasa Miyoshi
- 出版者
- Meteorological Society of Japan
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.15A, pp.1-7, 2019 (Released:2019-01-26)
- 参考文献数
- 25
- 被引用文献数
- 26
This paper is the first publication presenting the predictability of the record-breaking rainfall in Japan in July 2018 (RJJ18), the severest flood-related disaster since 1982. Of the three successive precipitation stages in RJJ18, this study investigates synoptic-scale predictability of the third-stage precipitation using the near-real-time global atmospheric data assimilation system named NEXRA. With NEXRA, intense precipitation in western Japan on July 6 was well predicted 3 days in advance. Comparing forecasts at different initial times revealed that the predictability of the intense rains was tied to the generation of a low-pressure system in the middle of the frontal system over the Sea of Japan. Observation impact estimates showed that radiosondes in Kyusyu and off the east coast of China significantly reduced the forecast errors. Since the forecast errors grew more rapidly during RJJ18, data assimilation played a crucial role in improving the predictability.
- 著者
- Koji Terasaki Masahiro Sawada Takemasa Miyoshi
- 出版者
- (公社)日本気象学会
- 雑誌
- SOLA (ISSN:13496476)
- 巻号頁・発行日
- vol.11, pp.23-26, 2015 (Released:2015-02-26)
- 参考文献数
- 14
- 被引用文献数
- 4 31
The Local Ensemble Transform Kalman Filter (LETKF) is implemented with the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) to assimilate the real-world observation data. First, the NICAM-LETKF system was developed using grid conversions between the NICAM's icosahedral grid and LETKF's uniform longitude-latitude grid to take advantage of the existing codes of Miyoshi. The grid conversions require additional computations and may cause additional interpolation error. Therefore, the LETKF code is modified, so that the LETKF reads and writes the NICAM's icosahedral grid data directly. We call this new version ICO-LETKF. In this study, the two systems are tested and compared using real conventional observations. The results show that the ICO-LETKF successfully accelerates the computations and improves the analyses.