1. Korea Institute of Atmospheric Prediction Systems (KIAPS) ( 재 ) 한국형수치예보모델개발사업단 LETKF 앙상블 자료동화 시스템 테스트베드 구축 및 활용방안 Implementation and application of LETKF data assimilation system to KIAPS AGCM Jong-Im Park and Ji-Sun Kang Korea Institute of Atmospheric Prediction Systems KIAPS International Workshop Apr. 17-18, 2013

2. Introduction Introduction  KIAPS 3D Data Assimilation Algorithm  Plans for KIAPS Data Assimilation System  Ensemble Data Assimilation  Local Ensemble Transform Kalman Filter (LETKF) could be the first system to be constructed as an operational system  Hybrid(3D-Var/LETKF)  3D hybrid assimilation systems of which components consist of the ensemble assimilation system, descent algorithm for minimization of 3D-Var cost function  Variational data assimilation (3D/4D-Var)  KIAPS also plan to develop a 4-d variational data assimilation system

3. Application of LETKF to AGCM  Local Ensemble Transform Kalman Filter (Hunt et al. 2007)  LETKF has been developed at the University of Maryland  Since 2007, there are many advanced technologies developed and implemented to the standard LETKF  Adaptive multiplicative inflation method (Miyoshi, 2011)  Running in place (Kalnay and Yang, 2010)  Variable localization (Kang et al., 2011, 2012)  ensemble forecast sensitivity to observation (EFSO; Kalnay et al., 2012)  LETKF data assimilation system can be used well for operational systems, because it can be easily parallelized.  Example of LETKF application to operational systems  JMA operational system (Miyoshi et al. 2010)  CPTECH, Brazil (Aravequia et al., 2006)  KIAPS has a strong collaboration with the Univ. of Maryland in terms of development of LETKF to the next generation AGCM.

4. Framework Overall structure of ensemble data assimilation system at the KIAPS  LETKF – CAM_SE  We are implementing LETKF to NCAR CAM_SE (Community Atmospheric Model- Spectral Element) model, because it has the similar dynamic core as HOMME/KIAPS.  Since EnKF analysis system is model-independent and NCAR CAM_SE has the same dynamic core as HOMME/KIAPS, the LETKF-NCAR CAM system can be immediately used for the HOMME/KIAPS when released  LETKF – SPEEDY  While developing LETKF-CAM_SE(HOMME/KIAPS), we would develop many essential methods to advance the current LETKF algorithm, using the simplified system as a testbed.

5. LETKF-SPEEDY  LETKF-SPEEDY  As a test bed, LETKF data assimilation has been implemented to an intermediate- complexity general circulation model SPEEDY(Simplified PrimitivE-Equation DYnamics) (Molteni, 2003)  SPEEDY can reduced computation time due to simplification of physics parameterization and low resolution  Many recently developed techniques of ensemble Kalman filter data assimilation have been examined (e.g. Kalnay et al., 2007; Miyoshi 2011; Kang et al. 2011, 2012) within LETKF-SPEEDY system  Structure of LETKF-SPEEDY

6. LETKF-SPEEDY  Observing System Simulation Experiments -The system will be utilized for validating data assimilation techniques -Sensitivity experiments can be done with simulated observations  SPEEDY - Spectral model with T30L7 - Prognostic variables : U, V, T, q, Ps  Example of simulated observations: radiosonde  Observation interval : 00Z, 12Z  Observation error: U and V (1 m/s), T (1 K), q (0.001kg/kg)

7. Example of OSSEs Global average of RMSE=3.15 Localization scale of observation : 100 km Localization scale of observation : 700 kmGlobal average of RMSE=0.06  Test of localization scales using LETKF-SPEEDY  We know the TRUTH in OSSEs!!!  Optimal case!

8. Progress  Various experiments have been performed using LETKF-SPEEDY under observing system simulation experiments  Current system uses radiosonde as simulated observation localization scales  Different localization scales of observation experiments were performed applying covariance inflation  To avoid filter divergence, applying covariance inflation experiments were performed - Fixed multiplicative inflation (standard) - Additive inflation  OSSEs of LETKF-SPEEDY show reasonable performance for each experimental setting  LETKF-SPEEDY system is reliable enough to used for the research and development purpose

9. Summary  KIAPS Ensemble Data Assimilation System – An ensemble data assimilation using LETKF has been started at the KIAPS LETKF-CAM_SELETKF-HOMME/KIAPS  LETKF-CAM_SE, LETKF-HOMME/KIAPS -We are implementing LETKF data assimilation system to CAM_SE model. -LETKF-NCAR CAM system can be immediately used for the HOMME/KIAPS when released  Test bed : LETKF-SPEEDY   Observing system simulation experiments have been performed very well -Various experiments using LETKF-SPEEDY show that the system is reliable enough to be used for the research and development purpose -We would develop many essential methods to advance the current LETKF algorithm, using the simplified system as a testbed.

10. Future plans Ongoing work Ongoing work – Observation operator suitable for an irregular model grid system (HOMME/KIAPS) New observation operator has been developed within LETKF codes (Dr. Kang) LETKF-SPEEDY system will be used for testing this new operator LETKF-SPEEDY will be tested including satellite data(AMSU-A, IASI) and GPS radio occultation data through Observation System Simulation Experiments(OSSEs). Advanced methods Advanced methods – We plan to test many useful techniques in EnKF, such as forecast sensitivity to observations (Kalnay et al. 2012), adaptive observation (Liu et al., 2007), etc.

11. Thank You