1. Guangqing Zhou, Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS) Bali, Indonesia 4 Sep. 2007 On Data Assimilation/Reanalysis: Ocean and Plateau

2. AIPO: Ocean CEOP/WEBS: Tibet Plateau Two Projects will deliver Data Assimilation/Reanalysis

3. Goals: 1.To improve understanding of the oceanic circulation around Indian Ocean, South China Sea and West Pacific areas, 2.and the water and energy cycles on plateau surface and in the atmosphere 3.and their impacts on Asian Monsoon via data assimilation/ reanalysis.

4. Objectives: To provide the assimilated data : (1)AIPO will establish an at least 15 year high resolution oceanic reanalysis data Area: 25 º S-40ºN and 30ºE-180ºE Grid: 0.25º in latitude and longitude Period: 1991-2005? (2) CEOP/WEBS will establish a 10-year high resolution plateau-covering data set Area: Tibet Palteau Grid: 0.5º in latitude and longitude with an 1-hour interval Period: 1998-2007

5. Research Activities: On the ocean data assimilation: (1) to validate and evaluate the existing remote-sensing sea surface parameter products, determine their multi-scale properties and uncertainties. Then to develop a method which can merge multi-source remote sensing observations and to generate a high resolution and regular-covering remote- sensing sea surface parameters. (2) to analyze the different in situ observations and determine their relative errors, and establish the relations between sea temperature and salinity.

6. Research Activities: (3) to validate the ocean model, determine the simulation errors and uncertainties, and develop a new background error covariance model which can describe more accurately the properties in AIPO area by ensemble method. (4) to establish a data set of high resolution sea surface parameters from multi-source remote-sensing observations and an at least 15 year (1991-2005) and high resolution (0.25° grid size expected) oceanic reanalysis product for the region of 25°S-40°N and 30°E-180°E by assimilating in situ and satellite data via ensemble method. (5) to validate and evaluate these assimilation/reanalysis data.

7. Research Activities: On the plateau covering data assimilation: (1) to validate the land data assimilation system at The University of Tokyo (LDAS-UT), which is to be used to produce surface water and energy budget. The system has been validated at the CEOP-Tibet mesoscale area with affluent in situ data. More validations will be conducted based on CEOP data. (2) to analyze and correct the LDAS forcing data: The LDAS-UT is driven by precipitation, radiation, wind, air temperature and humidity. These data can be provided by GCMs, reanalysis, or satellite retrieval. It is important to select appropriate data type to drive the LDAS-UT, since their accuracy is different. By Combining the remote-sensing data, in situ data and reanalysis, to generate a more reliable and accurate forcing data set (1998-2007).

8. Research Activities: (3) to establish a 10-year plateau soil water and flux budget data set (1998-2007), with a spatial resolution of 0.5 degree, and a temporal resolution of 1 hour, by assimilating the satellite microwave data. (4) to analyze water and energy cycles on plateau surface. (5) to analyze the atmosphere heating over the plateau, with particular attention to their climatology and temporal variability. No more information was obtained from CEOP/WEBS

9. Implementation: Financial Support: Ocean assimilation: in frame of AIPO supported by China Gov. Plateau assimilation: in frame of CEOP/WEBS Data Requirements: Ocean data assimilation: XBT, CDT, ARGO, TAO, GHRSST, satellite altimeter, and high resolution forcing data (ECMWF or other?) etc. New field or experiment observations also needed Plateau covering assimilation: satellite microwave brightness temperature, in situ data, ???? Model: Ocean assimilation: HYCOM with Ensemble Kalman Filter and OI Plateau assimilation: LDAS-UT

10. Implementation: Periods: AIPO: 2006.09 - 2011.09 CEOP/WEBS: ????.?? - ????.?? Data Release (plan) : Ocean Assimilated DATA: 2011 (?) Plateau Assimilated DATA: ????? Data Format and Access (proposed) : NetCDF with full documentations (metadata) via FTP and DODS Restriction for Data Use after release (proposed) : No for Scientific Researches and non-commercial use, for commercial use, decided by producers Acknowledgement and citation, Feedback

11. Huge and difficult Tasks for Data Managements: Some Comments 1.In first step, we should know:  what kinds of data we want and need in AMY? real time or/and delayed data: ??? operational or/and experimental: ??? what types of data we can and need to collect and archive? two aspects: technique capability now among AMY-related projects data providers: -- What kinds of data can be provided by various project voluntarily? If is there a big gap between what we want and what we can? I suggest: detail information of data should be supplied to the DMWG by every project which wants to contribute to AMY: what data? Exchange and Usage limitation? Existing or planning? how to access these data (existing data)? the persons responsible for the data management and access in the projects, etc.

12. Huge and difficult Tasks for Data Managements: Some Comments 2.Policy and Strategy: Dr Masuda has done excellent works policy and agreement should be accepted and followed by each data provider! and Can be operated easily and smoothly! 3.At this stage, distributed depositaries of data in individual project may be feasible, We need a catalog with information of the data which will be exchanged and shared: classification, metadata (documentation), access link, responsible persons for the data, etc. 4.Second Step, if possible, to integrate and archive the exchanged data to some key data centers Budget support: Important for maintaining, at least in AMY period. How to integrate: Technical Standard for data collection, archiving and dissemination. 5.Coordination with other groups! Modeling and Hindcast data to be archived ?

13. A part of AIPO project Ocean data assimilation supporting Chinese climate research Ocean-Atmosphere Interaction over the Joining Area of Asia and Indian-Pacific Ocean (AIPO) and Its Impact on the Short-Term Climate Variation in China Some Progress in Ocean Data Assimilation

14. Model Area Data Assimilation area with high resolution 1/4 Model area used for nesting (3/4)

15. Model: HYCOM with ¾ resolution Area: 30E-70W, 50S-60N Forcing: 6-hour ECMWF Temp., SLP, dew-point temp., wind stress, etc. River Fresh Water: Seasonal Lateral Boundary: release to GDEM data Period: 1981-2005 Model Validation with larger area

16. model woa01 Annually mean SSS

17. Annually mean S at depth 150m model woa01

18. model woa01 Annually mean T at depth 250m

19. Annual T at 5N in Indian Ocean model woa

20. T along the Equator in Pacific Ocean model woa

21. 表层流场 JanJul

22. Assimilation Technique Checking - Ensemble Kalman Filter in Pacific SST correlation between selected points and their vicinity

23. RMS errors (unit: ℃ and psu) a) surface temperature, b) surface salinity, c) temperature in the layer ( ), d) salinity in the layer ( ), e) temperature in the layer ( ), f) salinity in the layer ( ).

24. Thank you ！