Authors

• Main Authors: Takeshi Enomoto, Takemasa Miyoshi, Jun Inoue, Qoosaku Moteki, Miki Hattori, Shozo Yamane
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Subjects: Atmospheric General Circulation Model; Ensemble Kalman Filter; Observing System Experiment; Atmospheric Predictability; Optimization; Arctic; Indian
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.405.4835; http://www.jamstec.go.jp/esc/publication/annual/annual2009/pdf/2project/chapter1/p055_Enomoto.pdf

A data assimilation system for global atmospheric observations has been developed using an ensemble method. This system is composed of the Atmospheric General Circulation model for the Earth Simulator (AFES) and the Local Ensemble Transform Kalman Filter (LETKF). It assimilates global atmospheric observations from public data archives efficiently on the Earth Simulator. The new architecture of the renovated Earth Simulator required re-optimization of AFES and LETKF. The dynamical core of the AFES has been optimized to run twice as fast. The optimized version of LETKF achieved a bump of more than three times. A stream from 1 January 2008 is being conducted to give preliminary results. Smoother fields in the polar regions are achieved by the updated LETKF. Predicted precipitation compares well with satellite observations. Analysis error estimated as analysis ensemble spread is used to evaluate atmospheric observations and to study atmospheric predictability. Observing system experiments are conducted to clarify the influence of pressure observations by Arctic drifting buoys and to identify the planetary-scale propagation of the impact of additional dropsonde observations in the Indian Ocean. Precursory signals are found in various atmospheric phenomena in which the analysis ensemble spread increases prior to the events.