The “Climate Explorer” The US Climate Resilience Toolkit Interactive Mapping and Graph...

The “Climate Explorer”The US Climate Resilience Toolkit Interactive

Mapping and Graph Application

February, 2015

James Fox, Greg Dobson, Mark Phillips, Jeff Hicks

David Herring, David Stroud

http://toolkit.climate.gov/

Online Map Viewer

Interactive Map Functionality

Layer Transparency and Base Map Toggle

Legend (Layer Information)

Historical Climate Data, with Normals

Taking Action Case Study – New Jersey

Permalink allows ability to link to Case Studies

Taking Action Case Study – South Florida

Map Library (OpenLayers)

config.json

Climate Explorer (JavaScript, jQuery)

Multigraph

Client Tier

WMS/REST

Service

Permalink

Graph Builder

Data Source(NCDC)

CSV files

Data Transformer

Data ProcessingServer

Nightly cron job gets latest data

Historical station data repositoryFile storage (all data consists of flat files)

Data Tier

UIconfig.json

template.html

Web Browser

CSV files

ContainerModule

Application Data

Key

Climate Explorer

Data TransformerTakes large yearly GHCND datasets and turns them into small station files. It also can calculate derived data (e.g., YTD Precip)

MultigraphInteractive graph library

Graph BuilderUtilities for formatting the graphs and for doing some data massage.

Map LibraryLightweight map library built on OpenLayers. Uses JSON config.

CEUIConverts a mockup HTML file into a usable client application based on various config pieces (topics and sub topics, etc) and controls UI interactions with the data app

PermalinkAllows a dynamic client application (like CE) to have its state persisted via the URL. As the user changes things in the application, a link is generated that can be shared.

Transforms data files into optimal format for interactive use

Where does the data come from?

• Overlays are WMS or REST– Sources: US Drought Monitor, NOAA Coastal Services

Center, Homeland Security, USGS, FEMA, USDA, NASA

• Historical data is Global Historical Climatology Network-Daily (GHCN-D)– Thousands of stations– Compared against 1981-2010 Climate Normals– Precipitation is a derived yearly cumulative

GHCN-D Data Conversion

• In: yearly multi-GB files for all vars/all stations2014.csv

USC00273626,20140225,TMIN,-106,,,H,1800 USC00273626,20140225,TOBS,-56,,,H,1800...

• Out: 1000’s of multi-KB files, one-var/one-stationUSC00273626/TMIN.csv

20140225,-106

20140226,-150...

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A Few Parting Thoughts…Key Metrics for Decision Support Tools

• Data must be accurate, current and scaled for the decision• Visualizations make data meaningful and locally relevant• Storytelling is critical for policy makers to understand

uncertainty and follow a decision making process• Group Decision Making is a continuing, iterative process that

needs to be actively supported with appropriate technologies and toolsets

• Collaboration is key – the three sectors of government, academia and industry must work together to meet this challenge

Thank you! Questions?jfox@unca.edu