학술발표
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지식과 아이디어의 교류로 수치예보의 새로운 가능성을 모색합니다.
[세미나] 전문가 초청 세미나_서은교 교수
- 작성자
- 마스터관리자
- 작성일
- 조회수
- 45
- 일시: 2025년 2월 24일(월) 14:00~
- 장소: 사업단 7층 세미나실
- 연사: 서은교 교수(부경대학교)
- 제목: Land data assimilation into the JULES land surface model using the Local Ensemble Transform Kalman Filter
- Abstract:
This study develops a land data assimilation system with a local ensemble transform Kalman filter (LETKF) into JULES land surface model. Recently, remote sensing soil moisture retrievals with high-temporal and -spatial resolution have been increased. For instance, global near-surface soil moisture retrievals are available by SMAP and SMOS L-band passive, AMSR2 C-band passive, and ASCAT C-band active remote sensing retrievals in real-time. Additionally, snow water equivalent (SWE) and snow cover are available from AMSR2 and IMS (Interactive Multisensor Snow and Ice Mapping System) satellite, respectively. Therefore, this study performs the land data assimilation experiments using these satellite retrievals with ensemble-based data assimilation system. The result is evaluated by ground based in situ soil moisture measurements and "Assimilation Gain" enables us to explain which components affect soil moisture skill improvement in each experiment. The assimilation of multi-sensor retrievals, which extend the temporal and spatial coverage of soil moisture, exhibits better performance compared with the single sensor experiments and the snow assimilation also shows the beneficial impacts of assimilating satellite-based snow retrievals.
- 장소: 사업단 7층 세미나실
- 연사: 서은교 교수(부경대학교)
- 제목: Land data assimilation into the JULES land surface model using the Local Ensemble Transform Kalman Filter
- Abstract:
This study develops a land data assimilation system with a local ensemble transform Kalman filter (LETKF) into JULES land surface model. Recently, remote sensing soil moisture retrievals with high-temporal and -spatial resolution have been increased. For instance, global near-surface soil moisture retrievals are available by SMAP and SMOS L-band passive, AMSR2 C-band passive, and ASCAT C-band active remote sensing retrievals in real-time. Additionally, snow water equivalent (SWE) and snow cover are available from AMSR2 and IMS (Interactive Multisensor Snow and Ice Mapping System) satellite, respectively. Therefore, this study performs the land data assimilation experiments using these satellite retrievals with ensemble-based data assimilation system. The result is evaluated by ground based in situ soil moisture measurements and "Assimilation Gain" enables us to explain which components affect soil moisture skill improvement in each experiment. The assimilation of multi-sensor retrievals, which extend the temporal and spatial coverage of soil moisture, exhibits better performance compared with the single sensor experiments and the snow assimilation also shows the beneficial impacts of assimilating satellite-based snow retrievals.