Scope of Meteo/GIS in the International Context Olga Wilhelmi NCAR ADAGUC Workshop KNMI October

Outline Current state in integration of GIS and Atmospheric Sciences  Progress  Challenges Usability of atmospheric data in GIS Usability and uses of GIS for meteorological and climatological applications Future directions

The Purpose Challenges of earth system science research community include:  integration of complex physical processes into weather forecast and climate system models  understanding interactions between climate, environment, and society  integrating social and environmental information with weather and climate It is important to make atmospheric science usable and data accessible to a wide community of users, including researchers, educators, practitioners and policy-makers

The Challenge

The Challenge (cont.) Methods and concepts  Limited knowledge of GIS concepts and data models among atmospheric scientists  GIS community is making faster progress in adopting atmospheric concepts than atmospheric community adopting GIS concepts Technology  Dimensions  Interoperability between applications Data  Formats  Semantics People  Learning curve  Adoption of standards and data management practices

International Activities COST 719 ( ) NCAR GIS Initiative (2001- present) Professional societies (EGU, AMS) University Consortium for Geographic Information Science Open Geospatial Consortium ESRI Atmospheric User Group Others

Uses of GIS Visualization of information Spatial analysis (exploration of spatial patterns, relationships, networks; spatial statistics) Data distribution (web portals; web services) Data integration (interoperability; coupled systems, interdisciplinary research) First, need to resolve issues related to data usability and interoperability

Usability of Atmospheric Data Atmospheric Data Modeling working group categorized atmospheric data for usability in GIS as  GIS Ready (fully described, point and click)  GIS Friendly (some effort to transform into GIS- Ready; “not so friendly” if heavy processing needed)  GIS Alien (cannot be fully described)

GIS Ready: Existing GIS Data Structures GIS Data Object Spatial StructureExamples Points 2d – f(x,y), {z,t} as attributes 3d – f(x,y,z), {t} as attribute Observations & locations, model centroids, remote sensor retrievals at centroids, lightning strikes, Tropical Cyclone and Tornado location Arcs 2d – f(x,y), {z,t} as attributes 3d – f(x,y,z), {t} as attribute Atmospheric fronts, air parcel trajectories, isopleths (analysis), balloon aircraft ship & buoy tracks, satellite ground track, Tropical Cyclone & Tornado tracks Polygons 2d/3d – f(x,y), {z,t} as attributes Radar, air mass or tracer boundaries, zone/areal forecast, satellite footprints along a surface Rasters 2d/3d – f(i,j), {x,y} by projection, {z} by value or external layer, attributes not supported Model grid analyses and forecasts, satellite images Shipley et al.

GIS Friendly: Images require additional info QTUA11.tif QTUA11.tfw QTUA11.aux World File Projection 500 hPa chart on ArcGlobe Shipley et al.

GIS Friendly: Data Processing Required Lidar cross section over Cincinnati, OH Shipley et al.

GIS Alien (at least for now) Meteogram Time Series weather forecast (Meteogram) for Washington DC, starting 21 June 2006 P (x,y,z,t), attributes {p,q,u,v,…} Shipley et al.

Potential GIS Data Structures 4d points P (x,y,z,t), attributes {p,q,u,v,…} Observations, model grid products, time series, moving observation platforms Points in arbitrary dimensions Thermodynamic diagrams, z = f(T), p = f(θ); time series f(t); hyperspectral information, I = f(x,y,p,λ) Moving arcs Pl (x,y,z,t), attributes Time series of atmospheric fronts, isopleths (aka “analysis”), streamlines, intersections of volumes Arcs in arbitrary dimensionsChange of state or constituent transformation during transport of a point along a Lagrangian path, intersections of surfaces Moving polygons Py (x,y,z,t), attributes Radar feature morphology, air mass or tracer boundary deformation and motion, Polygons in arbitrary dimensions Identification of “spatial” patterns in data of arbitrary dimensions, event detection and identification Surfaces Defined by a set of points in multiple dimensions Pollutant layer or tracer (water vapor, potential vorticity) transport and transformation Volumes Defined by a closed surface Radar feature morphology, air mass or tracer boundary deformation and motion, n-dim grids & rasters R (i,j,k,…), attributes embedded VisAD Shipley et al.

NetCDF in ArcGIS (now GIS-Ready) In ArcGIS 9.2 NetCDF data is accessed as Raster Feature Table Direct read Exports GIS data to netCDF

NetCDF Tools Toolbox: Multidimension Tools  Make NetCDF Raster Layer  Make NetCDF Feature Layer  Make NetCDF Table View  Raster to NetCDF  Feature to NetCDF  Table to NetCDF  Select by Dimension

Using NetCDF Data Display  Same display tools for raster and feature layers will work on netCDF raster and netCDF feature layers. Graphing  Driven by the table just like any other chart. Animation  Multidimensional data can be animated through a dimension (e.g. time, pressure, elevation) Analysis Tools  A netCDF layer or table will work just like any other raster layer, feature layer, or table. (e.g. create buffers around netCDF points, re- project rasters, query tables, etc.)  Python

Emerging Standards CF Version 2  The next generation of the Climate and Forecast convention of netCDF GALEON  OGC testbed project to evaluate netCDF suitability as a WCS well known binary. Common Data Model (CDM)  A new API to access netCDF, HDF, GRIB, and OpenDAP through a single API. NcML  NetCDF Markup Language, like GML for netCDF earth science data

Data Visualization Symbology  Identifying common symbols and creating defaults for weather and climate variables  Integrating ESRI layer file and OGC style files  Developing 3-D symbols for weather phenomena  Use naming standards from CF convention

Spatial Analysis Interpolation methods  More progress in interpolating climate data than weather data Challenges  Temporal analysis (e.g., time series statistics, temporal interpolation, analysis and modeling of transitions, raster time series)  Working across scales (upscaling, downscaling) Many suitable existing geoprocessing tools for  Model verification  Impact and risk assessment (interdisciplinary)  Spatial patterns and suitability analysis

Example: Impacts of Climate Change

Data Integration Coordinate Systems –  Many atmospheric models are based on a sphere – much GIS data is based on an ellipsoid  Temporal coordinate systems Interoperability  Data  Applications AIS Client GIS Client

Data Distribution Web portals Data Discovery

Distributing outputs from NCAR’s Global Climate Model (CCSM) in a GIS format (shapefile, text file) Ongoing work: downscaling Example: GIS Climate Change Data Portal

Users of GIS Climate Change Data Portal  Since February 2005: 127K hits, 15K files downloaded, more than 1200 registered users from 95 countries  Many non-traditional users  Challenge: education about appropriate use of data Resource management Salmon conservation Human Health Energy Water Resources Agriculture Biomass potential Climate Change Education

Future Direction Distributed collaboratories for geosciences  Increased computing capacity and capability  Increased focus on multidisciplinary research Web services  Self-contained, modular applications that can be described, published, and accessed over the Internet  promote interoperability by minimizing the requirements for understanding between client and service and between services Extensible, interoperable web services for data discovery, access and transformation  Data services (e.g., WMS, WFC, WCS, ArcGIS server)  Geoprocessing services (web GIS, ArcGIS server)  Catalog services (e.g., THREDDS, CS-W)

Summary We are seeing progress in integration of GIS with atmospheric sciences, however many challenges remain Ongoing work with international data standards, web services, and integration of atmospheric and geospatial data make steps towards better understanding of the Earth System and solving societally relevant problems ADAGUC is on the right track for addressing challenging questions of data distribution and interoperability

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