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

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 Processing Server Nightly cron job gets latest data Historical station data repository File storage (all data consists of flat files) Data Tier UI config.json template.html Web Browser CSV files Container Module Application Data Key Climate Explorer Data Transformer Takes large yearly GHCND datasets and turns them into small station files. It also can calculate derived data (e.g., YTD Precip) Multigraph Interactive graph library Graph Builder Utilities for formatting the graphs and for doing some data massage. Map Library Lightweight map library built on OpenLayers. Uses JSON config. CEUI Converts 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 Permalink Allows 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 Climate Normals – Precipitation is a derived yearly cumulative

GHCN-D Data Conversion In: yearly multi-GB files for all vars/all stations 2014.csv USC , ,TMIN,-106,,,H,1800 USC , ,TOBS,-56,,,H, Out: 1000’s of multi-KB files, one-var/one-station USC /TMIN.csv , ,

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 16 Thank you! Questions?