1. Coordinated Enhanced Observing Period (CEOP) http://www.ceop.net an Element of WCRPinitiated by GEWEX EOP1: Jul.-Sep. 2001 EOP3: Oct. 2002 - Sep. 2003 EOP4: Oct. 2003 - Dec. 2004

2. CEOP SCIENTIFIC OBJECTIVES LONG-TERM GUIDING GOAL To understand and model the influence of continental hydroclimate processes on the predictability of global atmospheric circulation and changes in water resources, with a particular focus on the heat source and sink regions that drive and modify the climate system and anomalies. OVERALL OBJECTIVE 1 To better document and simulate water and energy fluxes and reservoirs over land on diurnal to annual temporal scales and to better predict these on temporal scales up to seasonal for water resources application. Water & Energy Simulation & Prediction (WESP) OVERALL OBJECTIVE 2 Document the seasonal march of the monsoon systems, assess their living mechanisms, and investigate their possible physical connections. CEOP Intern-Monsoon Study (CIMS)

3. Coordinated Enhanced Observing Period Convergence of Observations A Prototype of the Global Water Cycle Observation System of Systems Three Unique Capabilities

4. Hans-Jörg Isemer, International BALTEX Secretariat, isemer@gkss.de CEOP Reference Sites Data Release Guidelines Data exchange guidelines: (3) Data users: No transfer to third parties. (4) Data release to data users: Turn-around period. Category 1 data: 6 months Category 2 data: 15 months (2) CDA and data users: Commercial exploitation of CEOP data is prohibited. (1)To comply with WMO Resolutions 40 (CG-XII) and 25 (CG- XIII) in particular: No financial implications. (5) Acknowledgement and citation (6) Co-Authorship for Reference Sites’ PIs recommended, collaboration base required if PI requests co-authorship (in particular for category 2 data) (7) CEOP Publication Library at CDA

5. 3 Types scale of Satellite datasets 2.Monsoon Region 1.Reference site:35 Points TB 250km Rain Soil RS >Asia >North America >South America >Australia >North Africa Water Vapor 3.Global

6. Model Data Content (as of 6 February 2007) The most recent version of the data status (time line) can be found from: http://www.mad.zmaw.de/wdc-for-climate/ceop/ 4.324 TByte

7. Coordinated Enhanced Observing Period Interoperability Arrangement A well organized collecting, processing, storing, and disseminating shared data, metadata and products Data Integrating/Archiving Center at University of Tokyo and JAXA of Japan Model Output Data Archiving Center at the World Data Center for Climate, Max-Planck Institute for Meteorology of Germany In-Situ Data Archiving Center at NCAR (National Center for Atmospheric Research) of USA Three Unique Capabilities

8.  Metadata conceptual modelMetadata conceptual model ISO 19115 metadata  Application requirementsApplication requirements  Metadata implementation modelMetadata implementation model Metadata standard development framework  Web service applicationWeb service application ISO19139 Metadata- implementation OGC WFS,WCS,CTS

9. Data Source Data/Doc Arrive at EOL Visual Inspection of Data and Plots Available On-Line EOL Updates Status Table and Detailed Notes Reference Site EOP-3/4 Data Flow Apply Auto/Manual Data/Doc Consistency Checks Format Verification Gross Limit Checks Exact/Inexact Dup Records Data/Flag/Doc Checks Generate Flagging and Site Statistics Merge Data

11. Coordinated Enhanced Observing Period Data Management Distributed- and Centralized- Data Integration Functions In-situ satellite model UCAR MPI UT/JAXA In-situ Computing Power Computing Power Distributed Integration Services GrADS/ DODS for Subsets GrADS/ DODS for Subsets GrADS/ DDDS for Subsets Centralized Integration Services Three Unique Capabilities http://jaxa.ceos.org/wtf_ceop/ http://monsoon.t.u-tokyo.ac.jp/ceop-dc/ceop-dc_top.htm

12. Users Other Data Archives Locate Data Visualize Data Light Processing UCAR Archive (In-situ) MPI Archive (Model Output, MOLTS) DODSDODS CSDIC Archive (Satellite, Model Output) DODSDODS DODSDODS DODSDODS Live Access Server (LAS) WTF-CEOP Ferret GrADS IDLCDAT Matlab User client – Library of scripts Web Browser User client – data download and analysis WTF-CEOP Distributed Data Integration System

13. Centralized Data Integration Functions HTTP Server Visualization Engine Storage Management System Data Integration Engine Hierarchical Storage Management User Web Browser / Client Application DBMS Data Mining Engine

14. AIRS (Water Vapor)

15. Satellite Data GCM Reference site data

16. Land Surface Scheme Satellite Data Minimization Scheme Radiative Transfer Model Cost Function LDAS GCM Forcing Reference site data Validation Intercomparison

17. Input Data  High Applicability in Any Region LDAS-UT grid size: 0.5 degree Forcing –GPCP precipitation: 1 degree –ISCCP radiation: 2.5 degree –NCEP reanalysis: 1.5 degree Leaf area index: MODIS Microwave Tb: AMSR-E

18. First application: A case at CEOP Tibet site

19. OBS 1998 (2003 unavailable) NCEP JMA UKMO Seasonal variation （ May - September) Sensible （ H ）－ Latent(LE ）－ H RMSE [W/ ㎡ ] LE RMSE [W/ ㎡ ] LDASUT32.042.5 NCEP40.268.4 JMA32.379.8 UKMO35.380.1 LE daily-mean ( June) Observed Modeled LDASUT LDASUT- GCMs

20. LDAS Seasonality: May~Mid June, H > lE; Mid June~Aug; lE>H LDAS Regionality: H is dominant in N.W. TP, lE is dominant in S.E. TP LDASUTNCEP Seasonality of distributed Bowen Ratio: Sensible Heat Flux/Latent Heat Flux

21. Land Surface Scheme Satellite Data Minimization Scheme Radiative Transfer Model Cost Function LDAS GCM Forcing Reference site data Validation Land surface impacts on prediction Validation Intercomparison

22. Use the assimilation product as the initial condition Nighttime 3 Day Prediction Daytime sensible heat flux latent heat flux surface temperature Impacts of the Tibet surface initial conditions on the GCM prediction of the surface parameters (UT-JMA Cooperative Research)

23. FT 54FT 60FT 66FT 72 Control Anal UT Similar Rainfall Pattern but Stronger Rainfall Intensity Impacts of the Tibet surface initial conditions on the GCM prediction of the precipitation (UT-JMA Cooperative Research)

24. GCM Land Surface Scheme Satellite Data Minimization Scheme Radiative Transfer Model Cost Function Land surface impacts on prediction GCM Forcing Regional Model Physical Down-scaling Reference site data Validation

25. soil moisture (Surface perspective) Assimilation No Assimilation

26. (Atmospheric perspective) Vertical Wind field GMS IR1-based convective Index No Assimilation caseAssimilation case Vertical Wind field

27. Radar at BJ With Land Assimilation Without Assimilation 9-15 16-2223-04

28. Global Model NWPCs/ACs NCEP,JMA ECPC,BoM UKMO,CMC ECMWF CPTEC NCMWF EPSON MET GMAO GLDAS Regional Model Land Surface Model/LDAS HAPHAP extreme webs aerosol isotope cross- cutting monsoon cold semi-arid high- elevation Reference Sites River basins NCAR/EOL WDC-C MPI-M Satellite data CEOS JAXA NASA ESA NOAA EUMETSAT WGISS WGCV UT&JAXA DATA INTEGRATION & ANALYSIS Distributed System WTF-CEOP Centralized System U. Tokyo Global Dataset Projects Societal Benefits A Project of Projects