Real Data Assimilation Using the Local Ensemble Transform Kalman Filter (LETKF) System for a Global Non-hydrostatic NWP model on the Cubed-sphere
- 34 Downloads
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.
Key wordsEnsemble data assimilation local ensemble transform Kalman filter (LETKF) numerical weather prediction (NWP) atmospheric global model (AGM)
Unable to display preview. Download preview PDF.
- Aravequia, J. A., I. Szunyogh, E. J. Fertig, E. Kalnay, D. Kuhl, and E. J. Kostelich, 2011: Evaluation of a strategy for the assimilation of satellite radiance observations with the Local Ensemble Transform Kalman Filter. Mon. Wea. Rev., 139, 1932-1951, doi:10.1175/2010MWR3515.1.CrossRefGoogle Scholar
- Grody, N., F. Weng, and R. Ferraro, 1999: Application of AMSU for obtaining water vapor, cloud liquid water, precipitation, snow cover and sea ice concentration. Proc. the Tenth International ATOVS Study Conference, Colorado, USA, BMRC, 230-240.Google Scholar
- Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0028-9.Google Scholar
- Kang, J.-H., H.-W. Chun, S. Lee, H.-J. Song, J.-H. Ha, I.-H. Kwon, H.-J. Han, H. Jeong, and H.-N. Kwon, 2018: Development of an observation processing package for data assimilation in KIAPS. Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0030-2.Google Scholar
- Kleist, D. T. and K. Ide, 2015: An OSSE-based evaluation of hybrid variational-ensemble data assimilation for the NCEP GFS. Part I: System description and 3D-hybrid results. Mon. Wea. Rev., 143, 433-451, doi:10.1175/MWR-D-13-00351.1.Google Scholar
- Miyoshi, T., and Y. Sato, 2007: Assimilating satellite radiances with a Local Ensemble Transform Kalman Filter (LETKF) applied to the JMA global model (GSM). Sci. Online Lett. Atmos., 3, 37-40.Google Scholar
- Miyoshi, T., S. Yamane, and T. Enomoto, 2007: Localizing the error covariance by physical distances within a Local Ensemble Transform Kalman Filter (LETKF). Sci. Online Lett. Atmos., 3, 89-92.Google Scholar
- Song, H.-J., S. Shin, J.-H. Ha, and S. Lim, 2017: The advantages of hybrid 4DEnVar in the context of the forecast sensitivity to initial conditions. J. Geophys. Res., 122, 12226-12244, doi:10.1002/2017JD027598.Google Scholar
- Thépaut, J.-N., 2003: Satellite data assimilation in numerical weather prediction: An overview. Proc. the Annual Seminar on Recent Development in Data Assimilation for Atmosphere and Ocean, Reading, UK, ECMWF, 75-94.Google Scholar
- Yamazaki, A., T. Enomoto, T. Miyoshi, A. Kuwano-Yoshida, and N. Komori, 2017: Using Observations near the poles in the AFES-LETKF data assimilation system. Sci. Online Lett. Atmos., 13, 41-46, doi:10. 2151/sola.2017-008.Google Scholar