Unidata’s Common Data Model

1. Unidata’s Common Data Model John Caron Unidata/UCAR Nov 2006

2. Goals / Overview • Look at the landscape of scientific datasets from a few thousand feet up. • What semantics are needed to make these useful? • georeferencing • specialized subsetting

3. An Abstract Data Model describes data objects and what methods you can use on them. An API is the interface to the Data Model for a specific programming language A file format is a way to persist the objects in the Data Model. An Abstract Data Model removes the details of any particular API and the persistence format. What’s a Data Model?

4. Scientific Datatypes Point Trajectory Station Profile Radial Grid Swath Common Data Model Layers Coordinate Systems Data Access

5. THREDDS Catalog.xml Application Scientific Datatypes Datatype Adapter NetCDF-Java version 2.2 architecture NetcdfDataset CoordSystem Builder ADDE NetcdfFile I/O service provider OPeNDAP NetCDF-3 NIDS NcML NetCDF-4 GRIB HDF5 GINI Nexrad DMSP …

6. NetCDF-4 and Common Data Model (Data Access Layer)

7. I/O Service Provider Implementations • General: NetCDF, HDF5, OPeNDAP • Gridded: GRIB-1, GRIB-2 • Radar: NEXRAD level 2 and 3, DORADE • Point: BUFR, ASCII • Satellite: DMSP, GINI • In development • NOAA: GOES (Knapp/Nelson), many others

8. Coordinate Systems needed • NetCDF, OPeNDAP, HDF data models do not have integrated coordinate systems • so georeferencing not part of API • Need conventions to specify (eg CF-1, COARDS, etc) • Contrast GRIB, HDF-EOS, other specialized formats

9. NetCDF Coordinate Variables dimensions: lat = 64; lon = 128; variables: float lat(lat); float lon(lon); double temperature(lat,lon);

10. Coordinate Variables • One-dimension variable with same name as its dimension • Strictly monotonic values • No missing values The coordinates of a point (i,j,k) is {CV1(i), CV2(j), CV3(k)}

11. Limitations of 1D Coordinate Variables • Non lat/lon horizontal grids: float temperature(y,x) float lat(y, x); float lon(y, x); • Trajectory data: float NKoreaRadioactivity(pt); float lat(pt); float lon(pt); float altitude(pt); float time(pt)

12. General Coordinates in CF-1.0 float P(y,x); P:coordinates = “lat lon”; float lat(y, x); float lon(y, x); float Sr90(pt); Sr90:coordinates = “lat lon altitude time”;

13. Coordinate Systems (abstract) • A Coordinate System for a data variable is a set of Coordinate Variables2 such that the coordinates of the (i,j,k) data point is {CV1(i,j,k),CV2(i,j,k),CV3(i,j,k),CV4(i,j,k)…} previous was {CV1(i), CV2(j), CV3(k)} • The dimensions of each Coordinate Variable must be a subset of the dimensions of the data variable.

14. Need Coordinate Axis Types float gridData(t,z,y,x); float time(t); float y(y); float x(x); float lat(y,x); float lon(y,x); float height(t,z,y,x); float radialData(radial, gate) float distance(gate) float azimuth(radial) float elevation(radial) float time(radial)

15. The same?? float stationObs(pt); float lat(pt); float lon(pt); float z(pt); float time(pt); float trajectory(pt); float lat(pt); float lon(pt); float z(pt); float time(pt);

16. Revised Coordinate Systems • Specify Coordinate Variables • Specify Coordinate Types (time, lat, lon, projection x, y, height, pressure, z, radial, azimuth, elevation) • Specify connectivity (implicit or explicit) between data points • Implicit: Neighbors in index space are (connected) neighbors in coordinate space. Allows efficient searching.

17. Gridded Data Connected means Neighbors in index space are neighbors in coordinate space float gridData(t,z,y,x); float time(t); // Time float y(y); // GeoX float x(x); // GeoY float z(t,z,y,x); // Height or Pressure • Cartesian coordinates • All dimensions are connected

18. Coordinate Systems UML

19. Scientific Data Types • Based on datasets Unidata is familiar with • APIs are evolving • How are data points connected? • Intended to scale to large, multifile collections • Intended to support “specialized queries” • Space, Time • Corresponding “standard” NetCDF file conventions

20. Gridded Data • Cartesian coordinates • All dimensions are connected • x, y, z, time • recently added runtime and ensemble • refactored into GridDatatype interface float gridData(t,z,y,x); float time(t); float y(y); float x(x); float lat(y,x); float lon(y,x); float height(t,z,y,x);

21. GridDatatype methods CoordinateAxis getTaxis(); CoordinateAxis getXaxis(); CoordinateAxis getYaxis(); CoordinateAxis getZaxis(); Projection getProjection(); int[] findXYindexFromCoord( double x_coord, double y_coord); LatLonRect getLatLonBoundingBox(); Array getDataSlice (Range[] …) GridDatatype makeSubset (Range[] …)

22. Radial Data • Polar coordinates • All dimensionsare connected • Not separate time dimension radialData(radial, gate) : distance(gate) azimuth(radial) elevation(radial) time(radial)

23. Swath • lat/lon coordinates • not separate time dimension • all dimensionsare connected swathData(line,cell) lat(line,cell) lon(line,cell) time(line) z(line,cell) ??

24. Point Observation Data • Set of measurements at the same point in space and time • Point dimension not connected float obs1(pt); float obs2(pt); float lat(pt); float lon(pt); float z(pt); float time(pt); Structure { lat, lon, z, time; v1, v2, ... } obs( pt);

25. PointObsDataset Methods // Iterator<StructureData> Iterator getData( LatLonRect boundingBox, Date start, Date end);

26. Time series Station Data Structure { name; lat, lon, z; Structure{ time; v1, v2, ... } obs(*); // connected } stn(stn); // not connected

27. StationObs Methods // List<Station> List getStations( LatLonRectboundingBox); // Iterator<StructureData> Iterator getData( Station s, Date start, Date end);

28. Trajectory Data • pt dimension is connected • Collection dimension not connected Structure { lat, lon, z, time; v1, v2, ... } obs(pt); // connected Structure { name; Structure { lat, lon, z, time; v1, v2, ... } obs(*); // connected } traj(traj) // not connected

29. Profiler/Sounding Station Data Structure { name; lat, lon, time; Structure { z; v1, v2, ... } obs(*); // connected } loc(nloc); // not connected Structure { name; lat, lon; Structure { time, Structure { z; v1, v2, ... } obs(*); // connected } time(*); // connected } stn(stn); // not connected

30. Unstructured Grid • Pt dimension not connected • Looks the same as point data • Need to specify the connectivity explicitly float unstructGrid(t,z,pt); float lat(pt); float lon(pt); float time(t); float height(z);

31. Data Types Summary • Data access through a standard API • Convenient georeferencing • Specialized subsetting methods • Efficiency for large datasets

32. CDM Payoff N + M instead of N * M things on your TODO List! File Format #1 Visualization &Analysis NetCDF file File Format #2 OpenDAP Server File Format #N WCS Service Web Service

33. THREDDS Data Server HTTP Tomcat Server Catalog.xml Application THREDDS Server • OPeNDAP • HTTPServer • WCS NetCDF-Java library hostname.edu Datasets IDD Data

34. Next: DataType Aggregation • Work at the CDM DataType level, know (some) data semantics • Forecast Model Collection • Combine multiple model forecasts into single dataset with two time dimensions • With NOAA/IOOS (Steve Hankin) • Point/Station/Trajectory/Profile Data • Allow space/time queries, return nested sequences • Start from / standardize “Dapper conventions”

35. Forecast Model Collections

36. Conclusion • Standardized Data Access in good shape • HDF5, NetCDF, OPeNDAP • Write an IOSP for proprietary formats (Java) • But that’s not good enough! • To do: • Standard representations of coordinate systems • Classifications of data types, standard services for them