JpGU-AGU Joint Meeting 2017

Presentation information

[EE] Poster

A (Atmospheric and Hydrospheric Sciences) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS12] [EE] High performance computing for next generation weather, climate, and environmental sciences using K

Sat. May 20, 2017 3:30 PM - 5:00 PM Poster Hall (International Exhibition Hall HALL7)

convener:Hiromu Seko(Meteorological Research Institute), Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology), Masayuki Takigawa(Japan Agency for Marine-Earth Science and Technology)

[AAS12-P07] Two-year analysis experiments with NICAM-LETKF

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

Keywords:Data assimilation, NICAM, Satellite observation

We applied the Local Ensemble Transform Kalman Filter (LETKF) to the Non-hydrostatic ICosahedral Atmospheric Model (NICAM). Observation operators to assimilate the conventional observations, satellite-borne Atmospheric Microwave Sounder Unit-A (AMSU-A), and the Global Satellite Mapping of Precipitation (GSMaP) data were developed. The purpose of this study is to verify the long-term stability of the NICAM-LETKF system. We performed experiments to assimilate all observations for two years and two months from June 2014 to July 2016.
The first experiment was not successful. We found that the NICAM-LETKF system became unstable due to an extreme outlier of the 100-member ensemble. Therefore, we applied the relaxation to prior spread (RTPS) instead of the default setting of an adaptive multiplicative inflation method, and found that the NICAM-LETKF system was stable for more than two years. The analyzed atmospheric fields were largely improved by assimilating the AMSU-A radiances. The humidity bias is also improved by assimilating the GSMaP data while the NICAM is known to have a dry bias, especially over land.
We applied the Local Ensemble Transform Kalman Filter (LETKF) to the Non-hydrostatic ICosahedral Atmospheric Model (NICAM). Observation operators to assimilate the conventional observations, satellite-borne Atmospheric Microwave Sounder Unit-A (AMSU-A), and the Global Satellite Mapping of Precipitation (GSMaP) data were developed. The purpose of this study is to verify the long-term stability of the NICAM-LETKF system. We performed experiments to assimilate all observations for two years and two months from June 2014 to July 2016.
The first experiment was not successful. We found that the NICAM-LETKF system became unstable due to an extreme outlier of the 100-member ensemble. Therefore, we applied the relaxation to prior spread (RTPS) instead of the default setting of an adaptive multiplicative inflation method, and found that the NICAM-LETKF system was stable for more than two years. The analyzed atmospheric fields were largely improved by assimilating the AMSU-A radiances. The humidity bias is also improved by assimilating the GSMaP data while the NICAM is known to have a dry bias, especially over land.