1. © University of Reading 2008www.reading.ac.uk Reading e-Science Centre 9 September 2008 Harmonization of environmental data using the Climate Science Modelling Language Jon Blower, Alastair Gemmell (Reading e-Science Centre) Andrew Woolf, Dominic Lowe, Arif Shaon (STFC e-Science Centre) Stephen Pascoe (British Atmospheric Data Centre) Keiran Millard, Quillon Harphem (HR Wallingford)

2. We need to integrate and compare lots of different types of data…

3. SSM/I HadCM3 HiGEM ERA-40 Satellite Re-analysis product Low res. Climate GCM HadCM3 Hi-res Climate GCM, New physics Putt, Gurney and Haines …for validating numerical models…

4. … calibrating instruments … +=

5. …data assimilation… Black line: control run time Green stars: observations Red line: assimilation run

6. Flood prediction... and making predictions Search and rescue Climate prediction

7. Where we are now (mostly) Separate websites for each data provider

8. The need for harmonization Each community has evolved its own means for presenting data: –File formats –Metadata conventions –Coordinate systems These are not usually mutually compatible … and vital metadata can be missing No widely-accepted standards exist for certain types of data Hence scientists spend lots of time dealing with low-level technical issues Need a common view onto all these datasets

9. Open Geospatial standards Aim to describe all geographic data XML encoding –Geography Markup Language Web Services for data exchange Rooted in international standards Mandated by European INSPIRE directive But fiendishly complex Evolved from map-oriented systems –Vertical and temporal information not handled cleanly

10. Bridging the gap: CSML Climate Science Modelling Language –Abstract data model defined using ISO/OGC approach –XML encoding based upon GML Adapts open geospatial standards to environmental science data –“Best of both worlds” Wraps existing data –Doesn’t expect providers to convert data Data are seen as geographical “features”, not as a file system

11. Selected CSML Feature Types PointSeriesFeature (timeseries at a point) ProfileFeature (vertical profile at a point) GridSeriesFeature (series of multidimensional grids) SwathFeature (single satellite sweep) SectionFeature (vertical section) Feature Types are classified by their geometry

12. Harmonizing 2 databases using CSML Different data providers, different internal representation –Met Office “MIDAS” dataset –“Environmental Change Network” dataset Modelled both databases as collections of CSML PointSeriesFeatures Allowed sharing of plotting and analysis tools –CSML-XML documents converted to maps, plots and KML Intermediate step via XML not necessary in ideal world

13. Java-CSML Need reusable libraries to apply CSML more widely Aim is to reduce cost of developing data-driven applications Interoperates with other means of modelling data in Java: –GeoAPI, Common Data Model High-level analysis/visualization routines completely decoupled from low-level data access

14. Java-CSML: Design attempts 1.Transform CSML’s XML schema to Java code using automated tool Led to very deeply-nested code 2.Based upon OGC-sponsored GeoAPI Incomprehensible unless very familiar with ISO standards GeoAPI is a moving target 3.Based on well-known Java concepts Accessible to “typical” Java programmer Compatibility with other data models assured through wrappers Insulated against inevitable changes to standards More code needs to be written by Java-CSML designers Less code needs to be written by users

15. Java-CSML Application 1: Coastal oceanography decision support system Red line: Smartbuoy data Blue dots: model output

16. Behind the scenes Smartbuoys (via Web Feature Service) Physical model (via NetCDF files) Biological model (via OPeNDAP server) Java-CSML wrappers Java-CSML Plotting routines

17. Java-CSML Application 2: Atmospheric ozone Control run Assimilation run

18. Specializing CSML Features A generic data model can’t encode all possible metadata without becoming extremely complex In CSML generic feature types can be specialized –cf. object-oriented inheritance Hence core data model retains simplicity ProfileFeature ArgoProfileFeature int qualityFlag

19. Java-CSML Application 3: Ocean data assimilation ArgoProfileFeatureProfileFeature Red lines: Argo data Blue lines: model output

20. Summary CSML bridges gap between bottom-up (science) and top-down (GIS) approaches to modelling data –Wraps existing data holdings Data modelled as Feature Types distinguished by geometry and “sensible plotting” –Complexity managed through feature inheritance Doesn’t attempt to model everything! –Other technologies deal with discovery, provenance, security… Java-CSML framework allows data intercomparison applications to be built quickly –Automates tedious and error-prone tasks

21. Wider lessons “Interoperable” data formats not necessarily suitable for storage –Because no single data model can satisfy every application –Abstraction usually leads to data loss! Trade-offs between scope and complexity –Don’t attempt to put everything in one specification Symbiotic relationship between standards, tools and applications –Must be developed in parallel