Improving LETKF assimilation of remotely-sensed dense observations through direct model-space covariance localization
| Project/Area Number |
17H07352
|
|---|
| Research Category |
Grant-in-Aid for Research Activity Start-up
|
| Allocation Type | Single-year Grants |
|---|
| Research Field |
Meteorology/Physical oceanography/Hydrology
|
|---|
| Research Institution | Japan, Meteorological Research Institute |
|---|
Principal Investigator |
HOTTA Daisuke 気象庁気象研究所, 気象観測研究部, 主任研究官 (60805365)
|
|---|
| Research Collaborator |
SEKO Hiromu
SHOJI Yoshinori
KALNAY Eugenia
OTA Yoichiro
|
|---|
| Project Period (FY) |
2017-08-25 – 2019-03-31
|
| Project Status |
Completed (Fiscal Year 2018)
|
| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
|
|---|
| Keywords | データ同化 / アンサンブル・カルマンフィルタ / 共分散局所化 / 地上GNSS観測 / アンサンブルカルマンフィルタ / GNSS / 気象学 / 統計数学 / リモートセンシング / 衛星測位 |
|---|
| Outline of Final Research Achievements |
Ensemble assimilation of remote-sensed non-local observations like ground-based GNSS phase delays or satellite radiances requires to localize background error covariance matrices without resorting to the concept of the "observed position." In this study, a model-space localization scheme is proposed that can be applied to ensemble-transform square root filter which is a variant of ensemble-based data assimilation schemes. The proposed scheme is implemented and tested on an idealized one-dimensional system which mimics assimilation of ground-based GNSS observations. A new Eigen-spectral localization scheme is also proposed which performs well in cases where the true background covariance does not assume localized structure. Its effectiveness is similarly verified on an idealized one-dimensional system.
|
| Academic Significance and Societal Importance of the Research Achievements |
アンサンブル・データ同化手法は従前の変分法同化手法と比べ，大気の流れとともに日々変化する背景誤差の変化を適切に取り扱える長所がある一方，地上GNSS遅延量観測や衛星輝度温度など非局所的な観測の同化のインパクトが変分法による場合より小さいことが知られていた．本研究の成果は，その原因の一部が背景誤差共分散のアドホックな局所化にあることを示唆する点で学術的に意義があり，また現実的な解決策を提案した点で実用上も有益である．本研究の理論的成果は，地上GNSS観測等の非局所的リモートセンシング観測からより多くの情報を取り出すアンサンブルデータ同化システムの開発を通じて気象予測の精度向上に貢献しうる．
|
Report
(3 results)
Research Products
(18 results)
