Japan Geoscience Union Meeting 2016

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International Session (Oral)

Symbol A (Atmospheric and Hydrospheric Sciences) » A-CG Complex & General

[A-CG10] Earth and Planetary satellite observation projects Part II: Satellite Earth Environment Observation

Mon. May 23, 2016 3:30 PM - 5:00 PM 303 (3F)

Convener:*Riko Oki(Japan Aerospace Exploration Agency), Tadahiro Hayasaka(Graduate School of Science, Tohoku University), Kaoru Sato(Department of Earth and Planetary Science, Graduate School of Science, The University of Tokyo), Masaki Satoh(Atmosphere and Ocean Research Institute, The University of Tokyo), Yoshiaki HONDA(Center for Environmental Remote Sensing, Chiba University), Kenlo Nasahara(Faculty of Life and Environmental Sciences, University of Tsukuba), Takashi Nakajima(Tokai University, School of Information Science & Technology, Dept. of Human & Information Science), Taikan Oki(Institute of Industrial Science, The University of Tokyo), Tsuneo Matsunaga(Center for Environmental Measurement and Analysis, National Institute for Environmental Studies), Yukari Takayabu(Atmosphere and Ocean Research Institute, the University of Tokyo), Hiroshi Murakami(Earth Observation Research Center, Japan Aerospace Exploration Agency), Hajime Okamoto(Kyusyu University), Gail Skofronick Jackson(NASA Goddard Space Flight Center), Paul Chang(NOAA College Park), David Crisp(Jet Propulsion Laboratory, California Institute of Technology), Chair:Paul Chang(NOAA College Park), Taikan Oki(Institute of Industrial Science, The University of Tokyo)

4:00 PM - 4:15 PM

[ACG10-09] Satellite data assimilation using NICAM-LETKF

*Koji Terasaki1, Shunji Kotsuki1, Takemasa Miyoshi1 (1.RIKEN Advanced Institute for Computational Science)

Keywords:data assimilation, AMSU-A, GSMaP

Data assimilation plays an important role in increasing the accuracy of the numerical weather prediction (NWP). We applied the Local Ensemble Transformed Kalman Filter (LETKF) to the atmospheric general circulation model NICAM (Non-hydrostatic ICosahedral Atmospheric Model). In this study, the conventional observations, satellite microwave radiances from AMSU-A (Advanced Microwave Sounding Unit-A), and satellite-based global precipitation data GSMaP (Global Satellite Mapping of Precipitation) are assimilated. It is difficult to assimilate precipitation observations because of the non-Gaussian error distribution and highly nonlinear precipitation process. Methods are developed to get benefits from these three types of observations. The results indicate that adding more observations makes the analysis more accurate.