Toward Assimilation of GPM-derived Precipitation Data with NICAM-LETKF

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Wednesday, 7 January 2015
Takemasa Miyoshi, RIKEN Advanced Institute for Computational Science, Kobe, Japan; and S. Kotsuki, K. Terasaki, G. Y. Lien, and E. Kalnay

Among Eugenia Kalnay's broad interests and contributions, the most recent ones include the assimilation of global precipitation data into numerical weather prediction (NWP) models. Lien et al. pioneered to tackle the difficult problem with a simple low-resolution AGCM known as the SPEEDY model and proved the concept by assimilating the real TRMM Multi-satellite Precipitation Analysis (TMPA) data into a low resolution version of the NCEP GFS. Based on the success, we plan to extend the study to the assimilation of the new Global Precipitation Measurement (GPM)-derived precipitation data into the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) using the Local Ensemble Transform Kalman Filter (LETKF). Although we plan to start with the relatively low 112-km resolution version of NICAM, NICAM is meant to be a global cloud-resolving model that has been successfully integrated at an extremely high 870-m resolution with the Japanese K computer and contains precise microphysics schemes. With NICAM, we seek the most effective use of the GPM Dual-frequency Precipitation Radar (DPR) in NWP. As the first step, the GPM-derived precipitation measurements are compared with the NICAM simulations initiated by the NCEP FNL. The three-dimensional radar reflectivity is compared relatively well with the NICAM-generated bulk-physics water quantities. This presentation will include the most up-to-date results by the time of the symposium.