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

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

What’s a Data Model? 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.

Coordinate Systems Common Data Model Layers Data Access Scientific Datatypes Grid Point Radial Trajectory Swath StationProfile

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

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

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

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

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

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)}

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)

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”;

Coordinate Systems (abstract) A Coordinate System for a data variable is a set of Coordinate Variables 2 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.

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)

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);

Revised Coordinate Systems 1.Specify Coordinate Variables 2.Specify Coordinate Types (time, lat, lon, projection x, y, height, pressure, z, radial, azimuth, elevation) 3.Specify connectivity (implicit or explicit) between data points –Implicit: Neighbors in index space are (connected) neighbors in coordinate space. Allows efficient searching.

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

Coordinate Systems UML

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

Gridded Data 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); Cartesian coordinates All dimensions are connected x, y, z, time recently added runtime and ensemble refactored into GridDatatype interface

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[] …)

Radial Data radialData(radial, gate) : distance(gate) azimuth(radial) elevation(radial) time(radial) Polar coordinates All dimensions are connected Not separate time dimension

Swath swathData(line,cell) lat(line,cell) lon(line,cell) time(line) z(line,cell) ?? lat/lon coordinates not separate time dimension all dimensions are connected

Point Observation Data Structure { lat, lon, z, time; v1, v2,... } obs( pt); 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);

PointObsDataset Methods // Iterator Iterator getData( LatLonRect boundingBox, Date start, Date end);

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

StationObs Methods // List List getStations( LatLonRect boundingBox); // Iterator Iterator getData( Station s, Date start, Date end);

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

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

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

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

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

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

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”

Forecast Model Collections

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