| Budget Amount *help |
¥16,120,000 (Direct Cost: ¥12,400,000、Indirect Cost: ¥3,720,000)
Fiscal Year 2017: ¥2,470,000 (Direct Cost: ¥1,900,000、Indirect Cost: ¥570,000)
Fiscal Year 2016: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
Fiscal Year 2015: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2014: ¥9,490,000 (Direct Cost: ¥7,300,000、Indirect Cost: ¥2,190,000)
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| 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.
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