Godiva2 Overview

GWS Workshop, Nottingham, June 16 2008 © University of Reading 2008www.reading.ac.uk

Interactive visualization of four-dimensional environmental data using an enhanced Web Map ServiceJon Blower,Reading e-Science Centre,Environmental Systems Science Centre, University of Readingj.d.blower@reading.ac.ukhttp://www. reading.ac.uk/godiva2

2Interactive visualization of 4D environmental data

Issues we will cover

• Why does environmental science need geospatial web services?

• Online visualization of scientific data using a Web Map Service– What is meant by an “enhanced” WMS?

• Difficulties applying geospatial web services to scientific data

• Ideas for future research

3Interactive visualization of 4D environmental data

Flood prediction

We need to see into the future

Search and rescue

Climate prediction

All require interdisciplinary science!

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How can we make useful predictions?

• Need computer models that:– encapsulate our scientific knowledge– are validated by observations

• Output from these models needs to be disseminated:– within the scientific community– to government policy makers– to emergency response situations– ... often in (near) real time

• Need to compare models with other geospatial data sources– e.g. land use maps, locations of assets– Requires interoperability

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The importance of visualization

• Detecting features in models (e.g. storms)

• Diagnosing problems in models

• Preview data before downloading

• Make sense of large datasets

• Puts data into wider context

• Communicate complex concepts

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Existing scientific visualization software

• Problem-solving environments– Matlab, IDL

• 3-D desktop visualization– MayaVi

• 3-D remote visualization– Silicon Graphics

• Web-based– Live Access Server

• Geographic Information Systems (GIS)

• All require expert knowledge• Limited interoperability

between systems

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Barriers to effective visualization

• Computer model outputs are large...– Four-dimensional rasters (x,y,z,t)– High-resolution– Many variables– Ensembles– Tera/petabyte scale

• ... complex ... – Many file formats and conventions– Many numerical grids (right)

• ... and distributed– Too much data to hold in one place

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Summary so far

• Predictions need models that are validated by data

• Numerical model data pose several practical challenges

• Visualization of env. sci. data is v. important but complex

• Need to visualize data from lots of different sources

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Open Geospatial Web Services

(plus many more!)

Web Coverag

e Service

Gridded data(rasters)

Web Map

Service

Map imagery(PNG, JPG, GIF)

Web Feature Service

Simple features

Complex features

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Web Map Service: a closer look

• GetCapabilities -> metadata

• GetMap -> map image, based on client-selected parameters, inc:– Image width/height– Image coordinate reference system– Style

• GetFeatureInfo -> information about a particular map pixel– Format not standardized

• Supports multidimensional data

• Mandated by EC INSPIRE directive as means for visualizing geospatial data

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Lightweight visualization methods

• Most heavyweight logic is transferred to server– Pros and cons!

• Imagery transmitted over Web in standard formats

• WMS interfaces (often)• Simple data formats,

Javascript APIs

• Simpler approach, easier to use

• But functionality often limited

NASA World Wind

Google Earth

Microsoft Virtual Earth

OpenLayers

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Limitations of WMS for science

• Map-oriented– Scientists want to slice data in lots of ways

• Need extra metadata for scientific data– Format not standardized

• Clients and servers often don’t support z and t– (but it’s in the specification)

• Server implementations often slow for high-res raster data– Can’t use interactively

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A new system: Godiva2• Interactively explore

4D geospatial raster datasets on the web

• ~40 datasets– Research data,

operational forecasts, satellite products

• Images generated dynamically for maximum flexibility

• OGC Website of the Month, January 2008

http://www.reading.ac.uk/godiva2

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Selection of depth

Select from all the depth levels of the model

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Selection of time (range)Select from all the timesteps in the model

Selection of a time range leads to an animation

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Finding the data value at a point

Click on the data layer, data value and precise position is shown

Lon: -64.08 Lat: 36.21 Value: 19.27

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Timeseries plots

If a time range is selected, can create a timeseries plot at a point

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Vector plots

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Selection of colour palette

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Contrast-stretching

• Manual or automatic

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Polar projections

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Choice of background images

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Export to Google Earth• Allows visualization of multiple

data sources

• Hurricane Katrina, August 2005

• Storm track positions (analysed from ECMWF vorticity data) by Lizzie Froude, ESSC

• Sea surface temperature data from UK Met Office FOAM model

• Combination shows cooling of surface waters on right-hand side of cyclonic storm track

• High winds cause upwelling of cool, deep water

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Architecture of Godiva2 system

Java Web Application

(Spring, JSP)

Data abstraction layer

NetCDF files Other files

GetCapabilities GetMap GetFeatureInfo Custom metadata

Godiva2website

JSON

PNG, GIF

Generic WMS client

XML

Remote data

OPeNDAP

PNG, GIF

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Visualizing distributed data:the MERSEA project

OPeNDAP

DATANorth

Atlantic data centre

OPeNDAP

DATA

OPeNDAP

DATA

WMS @ Reading

Dynamic Quick Viewwebsite(= rebranded Godiva2)

Uses existing OPeNDAP-based architecture

Single point of failure

http://www.resc.rdg.ac.uk/mersea

Background imagery(from NASA etc)

Baltic data centre

Arctic data centre

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Removing the bottleneck:Federated visualization

OPeNDAP

DATA

WMS OPeNDAP

DATA

WMS OPeNDAP

DATA

WMS

Third-party WMS

Background imagery(from NASA etc)

Each data centre must install the WMS

Less network traffic

More robust

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What is the best use for this?

Have an idea

Discuss/exploreDo the work

Formally publish

Godiva2

Matlab, IDL etc

Disseminate

Godiva2

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Who’s using Godiva2?• 100,000 GetMap requests served in 3 months

– From 5 continents

• Customized versions of Godiva2 site set up for MERSEA and ECOOP projects– Major EU framework projects – INSPIRE compliance important!– Will be used in MyOcean

• UK National Centre for Ocean Forecasting

• Server software installed by:– Plymouth Marine Labs– AIMS, Australia– NOAA, US

• Code contributions from:– MeteoGalicia, Spain– TPAC, Tasmania– AIMS, Australia

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Enhancements to WMS• Piecemeal metadata-serving

– avoids large Capabilities document

• Extra metadata for science data– e.g. units of measurement

• New parameters in GetMap for styling:– Choose colour palette– Set contrast range– Linear or logarithmic scaling– Far simpler than Styled Layer Descriptor

• Generation of timeseries plots via GetFeatureInfo

• … but fully backward-compatible with WMS1.1.1 and 1.3.0

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Interoperability

NASA World Wind

Cadcorp SIS

Google Earth

3rd-party clients can’t use the custom WMS extensions

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Godiva2 summary

• Godiva2 site is useful for exploring and previewing data– Users need to download data for more sophisticated

analysis– Available as open-source software (http://ncwms.sf.net)

• Have focussed on marine data but applicability is much wider

• Use of WMS standard enables wide adoption and helps to build a community

• Successful example of delivering an application via the web

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Elephants I have ignored:The three “S”s

• Security

• Semantics

• Scalability

GWS Workshop, Nottingham, June 16 2008 © University of Reading 2008www.reading.ac.uk

Conclusions…

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Why is it hard to reconcilescientific data and open GIS standards?

GIS ScienceEverything is an attribute of a geographic location

Geographic location is an attribute of data

Map-oriented, i.e. 2.5D (although things are changing, slowly)

Fully four-dimensional data

Well-known, stable coordinate systems

Weird and wonderful coordinate systems (spatial and temporal)

XML is primary exchange mechanism

Data volumes often too large for XML

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Future work

• Support non-raster data– E.g. In-situ observations

• Support non-map slices– x-t (Hovmuller)– x-z, y-z (depth sections)

• Visualize multiple datasets at once

• Add capability for simple data processing

• Integrate with existing community software– THREDDS, GeoServer, ERDDAP

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More research needed…• Scalability of servers

– Key disadvantage of service-oriented software!

• “Science profile” for Web Map Service?– Earth Observation profile already exists

• How best to link with Processing Services?– E.g. for data intercomparisons– Service chaining

• Appropriate security methods?• Redesign of OGC services?

– Reveal information (esp. metadata) piecemeal

• Implementation of standards!

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Some final thoughts

• Geospatial Web Services are all about interoperability

• Interoperability is almost always lossy

• Law of diminishing returns applies

• In science we usually can’t lose any information

• Hence what is the practical limit for application of GWS technology in science?