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The IRI Climate Data Library: translating between data cultures

This work presented by the Benno Blumenthal International Research Institute for Climate Prediction at Columbia University explores the merging and mapping of climate data from different data cultures. It highlights the needed functionality and changes in data models and functionality. Examples and lessons learned are provided, showcasing the translation of data between different formats and projections.

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The IRI Climate Data Library: translating between data cultures

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  1. Benno Blumenthal International Research Institute for Climate Prediction Columbia University The IRI Climate Data Library: translating between data cultures

  2. Work presented has been done by • John del Corral • Michael Bell • Emily Grover-Kopec

  3. Outline • Data Cultures • Needed Functionality for usefully merging • Data model changes • Functionality changes • Example • Mapping SimpleFeatures to OpenDAP • Lessons Learned

  4. Economics Public Health “geolocated by entity” Ocean/Atm “geolocated by lat/lon” multidimensional GIS “geolocation by vector object or projection metadata” spectral harmonics equal-area grids GRIB grid codes climate divisions IRI Data Collection Dataset • Dataset • Dataset • Variable • ivar • ivar multidimensional

  5. GRIB netCDF images binary spreadsheets shapefiles Database Tables queries Servers OpenDAP THREDDS images w/proj IRI Data Collection Dataset • Dataset • Dataset • Variable • ivar • ivar

  6. GRIB netCDF images binary spreadsheets shapefiles Database Tables queries Servers OpenDAP THREDDS images w/proj IRI Data Collection Dataset • Dataset • Dataset • Variable • ivar • ivar User Interface viewer manipulations calculations Calculations “virtual variables” Clients OpenDAP THREDDS Data Files netcdf binary images Tables OpenGIS WMS v1.3

  7. Functionality for new Users • want to use our current data holdings • aggregation (particularly of images) • time analysis of GIS data • translation to “entity” basis

  8. Data Model Changes • projection attributes • SpatialReferenceSystemWKT (An OpenGIS standard) • SpatialReferenceSystemDims • geometry data object • OpenGIS Simple Feature

  9. Functionality Changes • geometry display: fill, fillby, stroke, mark, ... • projection functionality: objects with differing projection attributes can be made to match • rasterization: geometry object converted to raster • computes the fraction of each gridbox that is covered by the given geometry object

  10. Example • NDVI[x y time] Albers projection and location [d_name] Lon/lat geometry produces NDVIg[d_name time]

  11. weighted-average

  12. Simple Features and OpenDAP v2.0 structure

  13. Simple Features and OpenDAP v2.0 attributes • SpatialReferenceSystemWKT Projection information • OpenGISSimpleFeature Point, LineString, Polygon, MultiPoint, MultiLineString,MultiPolygon,GeometryCollection Or • Dimensionality • 0 (point) 1 (line) 2 (polygon)

  14. Sample Attributes NDVI [ x y time] SpatialReferenceSystemWKT PROJCS["Albers_Equal_Area_Conic",GEOGCS["GCS_North_American_1927",DATUM["D_North_American_1927",SPHEROID["Clarke_1866",6378206.4,294.9786982]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Albers"],PARAMETER["False_Easting",0],PARAMETER["False_Northing",0],PARAMETER["Central_Meridian",20],PARAMETER["Standard_Parallel_1",21],PARAMETER["Standard_Parallel_2",-19],PARAMETER["Latitude_Of_Origin",1],UNIT["Meter",1]] SpatialReferenceSystemDims x y

  15. Lessons Learned • nice to have a location object • Place to put attributes For example, if I have an image ndvi[x y time] with an Albers projection, clearly the projection is an attribute of x y. Seeing as x y are contained in ndvi, putting the SpatialReferenceSystemWKT attribute almost makes sense, but then I have to add the other attribute SpatialReferenceSystemDims to clarify. But now I have attributes in a container which sometimes apply up, and sometimes down.

  16. Lessons Learned • Our structural representations (OpenDAP, netCDF) are missing recursion • Must be able to specify different standards for different parts of a dataset – country codes are an iso standard, for example, but are unlikely to ever be part of CF or WCS • Must be able to transmit everything, not just anointed datasets • Bringing together different concepts in different data cultures (multidimensionality, location as object) improves both sides of the divide.

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