Ensemble Data Assimilation of GSMaP Precipitation with the - - PowerPoint PPT Presentation

Ensemble ¡Data ¡Assimilation ¡of ¡GSMaP ¡ Precipitation ¡with ¡the ¡Nonhydrostatic ¡ Global ¡Atmospheric ¡Model ¡NICAM

RIKEN-UMD Data Assimilation Conference, Oct 7, 2015@ UMD Shunji Kotsuki1, Koji Terasaki1, Guo-Yuan Lien1, Takemasa Miyoshi1,2, and Eugenia Kalnay2

1Data Assimilation Research Team, RIKEN-AICS, Japan 2University of Maryland, College Park, Maryland, USA

Shunji Kotsuki

• Postdoctoral Researcher
• Background

– Doctor of Engineering at Kyoto-Univ., Japan (in 2013) – Hydrology & water resources

• Prof. Miyoshi’s Team in RIKEN

– from Jan., 2014 – working on

• Precipitation DA with NICAM-LETKF
• Land data assimilation

River discharge simulation

Brief self introduction

Outline

• Introduction
• Gaussian-Transformation
• DA-cycle experiments
• Forecast experiments

Goals

• To improve NWP using satellite-derived precipitation

data following Lien et al. (2013, 2015a, 2015b)

• To produce a new precipitation product through data

assimilation

Improvement achieved with GFS-LETKF (U-wind @ 500 hPa) Li Lien en et et al. (2 (2015) ) Radiosondes ONLY Radiosondes+Precip. (No-Transform) Radiosondes+Precip. (Log-Transform) Radiosondes+Precip. (Gaussian-Transform)

Experimental Setting

• Numerical Model

– NICAM (Satoh and Tomita 2004, Satoh et al. 2008, 2014)

• GL6 (approx. 110 km resolution)
• Observations

– PREPBUFR: only upper sounding data (ADPUPA) – GSMaP/Gauge (Ushio et al. 2009)

• with Gaussian transformation
• Data assimilation

– LETKF (Hunt et al. 2007) – NICAM-LETKF (Terasaki et al. 2015) with 36 members

• 3D-LETKF
• Localization: 400 km for horizontal & 0.4 log(p) for vertical
• Relaxation to prior perturbation (Zhang et al. 2004; α = 0.7)