Transcript Document

Scope of Meteo/GIS in the
International Context
Olga Wilhelmi
NCAR
ADAGUC Workshop
KNMI
October 3-4 2006
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 (2001-2006)
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 GISReady; “not so friendly” if heavy processing
needed)
 GIS Alien (cannot be fully described)
GIS Ready:
Existing GIS Data Structures
GIS Data
Object
Spatial Structure
Examples
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
World File
500 hPa chart
on ArcGlobe
QTUA11.tfw
14861.3
-36.775
-5.697
-14922.7
-12838043.0
10927734.5
QTUA11.aux
Projection
Shipley et al.
GIS Friendly:
Data Processing Required
Lidar cross section
over Cincinnati, OH
Shipley et al.
GIS Alien (at least for now)
Meteogram
P (x,y,z,t), attributes {p,q,u,v,…}
Time Series weather forecast (Meteogram) for Washington DC,
starting 21 June 2006
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 dimensions
Change 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
Radar feature morphology, air mass or tracer
boundary deformation and motion,
surface
n-dim grids
& rasters
R (i,j,k,…), attributes
VisAD
embedded
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, reproject rasters, query tables, etc.)
 Python
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
Example: GIS Climate Change
Data Portal
Distributing outputs
from NCAR’s Global
Climate Model (CCSM)
in a GIS format
(shapefile, text file)
Ongoing work:
downscaling
http://www.gisClimateChange.org
Users of GIS Climate Change
Data Portal
Biomass
potential
Resource
management
Salmon
conservation
Climate
Change
Education
Water
Resources
Agriculture
Energy
Human
Health
 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
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
Thank You!
For more information:
http://www.gis.ucar.edu
E-mail: [email protected]