Scope of Meteo/GIS in the Scope of Meteo/GIS in the International ContextInternational Context

Olga Wilhelmi

NCAR

ADAGUC Workshop

KNMI

October 3-4 2006

OutlineOutline

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 PurposeThe 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 ChallengeThe Challenge

The Challenge (cont.)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 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 GISUses of GISVisualization 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 DataUsability 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:GIS Ready:Existing GIS Data StructuresExisting GIS Data StructuresGIS Data

ObjectSpatial Structure Examples

Points 2d – f(x,y), {z,t} as attributes3d – 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 attributes3d – 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:GIS Friendly:Images require additional infoImages require additional info

14861.3-36.775-5.697-14922.7-12838043.010927734.5

14861.3-36.775-5.697-14922.7-12838043.010927734.5

QTUA11.tif

QTUA11.tfw

QTUA11.aux

World FileWorld File

Projection

500 hPa chart500 hPa charton ArcGlobeon ArcGlobe

Shipley et al.

GIS Friendly: GIS Friendly: Data Processing RequiredData Processing Required

Lidar cross section Lidar cross section over Cincinnati, OHover Cincinnati, OH

Shipley et al.

GIS Alien (at least for now)GIS Alien (at least for now)MeteogramMeteogram

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 StructuresPotential GIS Data Structures4d 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 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 NetCDF in ArcGIS (now GIS-ReadyGIS-Ready))In ArcGIS 9.2 NetCDF data is accessed as

Raster

Feature

Table

Direct read

Exports GIS data to netCDF

NetCDF ToolsNetCDF 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 DataUsing 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

Data VisualizationData 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 AnalysisSpatial 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 Example: Impacts of Climate Change Change

Data Integration 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

GISClient

Data DistributionData Distribution

Web portals

Data Discovery

Distributing outputs from NCAR’s Global Climate Model (CCSM) in a GIS format (shapefile, text file)

Ongoing work: downscaling

http://www.gisClimateChange.org

Example: GIS Climate Change Example: GIS Climate Change Data PortalData Portal

Users of GIS Climate Change Users of GIS Climate Change Data PortalData 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 DirectionFuture 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)

SummarySummary

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!Thank You!

For more information:For more information:

http://www.gis.ucar.edu

E-mail: olgaw@ucar.edu