Estimation of cloud bottom height using satellite observation and its application to satellite data assimilation technique

2017 Fiscal Year Final Research Report

• Project/Area Number: 26289162
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Partial Multi-year Fund
• Section: 一般
• Research Field: Hydraulic engineering
• Research Institution: Kanazawa University
• Principal Investigator: TANIGUCHI KENJI 金沢大学, 環境デザイン学系, 准教授 (20422321)
• Co-Investigator(Kenkyū-buntansha): 中村 和幸 明治大学, 総合数理学部, 専任准教授 (40462171) ; 広瀬 望 松江工業高等専門学校, その他部局等, 准教授 (40396768) ; 久保 守 金沢大学, 電子情報学系, 助教 (90249772)
• Project Period (FY): 2014-04-01 – 2018-03-31
• Keywords: 気象学 / 数値シミュレーション / リモートセンシング / データ同化 / 水循環 / 洪水

Outline of Final Research Achievements

In this research, a data assimilation technique by Ensemble Kalman Filter (EnKF) was developed to improve hydrometeors (cloud water, cloud ice, snow, grauple and water vapor) in numerical weather model. Brightness temperatures (TB) observed by microwave radiometer onboard satellites were assimilated. Assimilation of TBs in multiple frequencies showed better results than the assimilation of single frequency TB. At the same time, comparing one-time assimilation experiment, sequential data assimilation using TBs at multiple observation time also showed improvement of atmospheric condition. Predicted rainfall also showed clear improvement in the maximum values in ensemble simulations.
Cloud bottom height which is used to define the lower boundary of vertical distribution of hydrometeors in the assimilation process showed clear relationship to atmospheric temperature. Using this relationship, appropriate cloud bottom height can be estimated for each assimilation time.

Free Research Field

水文気象学