Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere

• Published in: Asia-Pacific journal of atmospheric sciences Vol. 54; no. Suppl 1; pp. 351 - 360
• Main Authors: Shin, Seoleun, Kang, Jeon-Ho, Chun, Hyoung-Wook, Lee, Sihye, Sung, Kwangjae, Cho, Kyoungmi, Jo, Youngsoon, Kim, Jung-Eun, Kwon, In-Hyuk, Lim, Sujeong, Kang, Ji-Sun
• Format: Journal Article
• Language: English
• Published: Seoul Korean Meteorological Society 01.06.2018; Springer Nature B.V; 한국기상학회
• Subjects: Additives; Atmospheric Sciences; Climatology; Data; Data assimilation; Data collection; Earth and Environmental Science; Earth Sciences; Geophysics/Geodesy; Handling; Kalman filters; Meteorological satellites; Prediction models; Radiance; Remote sensing; Sampling error; Satellite data; Satellites; Spaceborne remote sensing; Systems analysis; Weather forecasting; 대기과학; numerical weather prediction (NWP); atmospheric global model (AGM); Ensemble data assimilation; local ensemble transform Kalman filter (LETKF)
• ISSN: 1976-7633; 1976-7951
• DOI: 10.1007/s13143-018-0022-2

An ensemble data assimilation system using the 4-dimensional Local Ensemble Transform Kalman Filter is implemented to a global non-hydrostatic Numerical Weather Prediction model on the cubed-sphere. The ensemble data assimilation system is coupled to the Korea Institute of Atmospheric Prediction Systems Package for Observation Processing, for real observation data from diverse resources, including satellites. For computational efficiency in a parallel computing environment, we employ some advanced software engineering techniques in the handling of a large number of files. The ensemble data assimilation system is tested in a semi-operational mode, and its performance is verified using the Integrated Forecast System analysis from the European Centre for Medium-Range Weather Forecasts. It is found that the system can be stabilized effectively by additive inflation to account for sampling errors, especially when radiance satellite data are additionally used.