1. Pat Pellegrini, Ph.D Gita Ashar June 21, 2013 Complex Weather data and a Multi-platform Audience: Big data at The Weather Network

2. Background How do we do what we do? Big data at The Weather Network The mobile challenge Summary Big Data at The Weather Network: Agenda 3. Background 4. The Weather Network: Almost 25 years in delivering weather Private company established in 1989, owned by Pelmorex Media Inc. Started as a 24 hour television channel in English and French Proprietary technology to develop and deliver high quality, reliable weather-related information Consistently recognized as one of the best managed companies in Canada (7 successive years) Highly recognized brand, trusted source used by consumers daily (Ipsos-Reid 16th most influential brand in Canada and 6th among Canadian companies) Largest meteorological staff in the Canadian private sector Undisputed leader in our market category connecting with more than 20 million Canadians every month across all screens 5. Our core purpose Proactively provide the most accurate, up-to-date and relevant weather information through breakthrough new technology and content so consumers can be better prepared and plan their day. 6. Mobile & Apps 7. Interactive TV 8. HD, in car navigation 9. 10.052M 4.651M TWN reaches a significant number of unique customers There are approximately 20M** individual weather consumers that use at least one of our products 16.473M APPSTV 2% WEB 37% 5% 36% 11% 7% 1% * Based on average Q2 FY11 results. ** Numbers are directional and based on sample data provided by segmentation survey Use only one platform Use all three platforms Use only TV and Web Use only Web and Apps Use only TV and Apps 10. TWN is the top choice for weather across all platforms Television Web Source: 2011 Consumer Segmentation Study - Environics 11. TWN is the top choice for weather across all platforms PC Apps Mobile Apps Source: 2011 Consumer Segmentation Study - Environics 12. TWN products are seen as high quality among consumers Source: Harris Decima Equitrend 2011 Quality Scores *Note: Average is defined as those brands that fall between the 25th and 75th percentiles Base: Among those familiar with brand Competitive Set Quality Avg: 7.1 AverageTop 10% Top 25% 13. How do we do what we do? 14. Products and Information Meteorology Operations Data People and Technology 15. Climatology Profiling Observation Engine RGOES Satellite Pelmorex Forecast Engine Nowcasting System PLDN lightning CRAD radar RWIS Product Generation Systems GIS 16. Climatology Observation Engine RGOES Satellite Pelmorex Forecast Engine Nowcasting System PLDN lightning CRAD radar RWIS Product Generation Systems GIS MAPLE Forecaster FEC NWP Model Weather Icons 1km Downscale Profiling Bias Correct Mapping 17. Continuous innovation in weather content 14 day trend Hourly forecast for next 72 hours Precipitation amounts for long-term forecast 18. Your Weather. Closer than ever. PointCast moves The Weather Network from 5000 forecasts across the country to individually generated forecasts for over 800,000 locations derived from Postal Codes. 19. Ubiquity of weather, Pre-loaded apps, consumer and platform fragmentation Growth of global weather providers means global competition; we have formally expanded internationally To compliment WX, offer multiplatform travel-related information, for commuters or international travelers Create best consumer and brand experience across platforms delivered on demand and across the world. Challenges, Growth, Strategy Increasing Complexity Global Competition Travelers Network C.O.P.E. 20. Big Data at The Weather Network 21. Big Data at The Weather Network VARIETY VOLUME VELOCITY NWP Models Radar Satellite Social Media Finer resolutions Ensemble models Dual-polarization Phased array Polar orbiting GOES-R Twitter Facebook o Traditional Forecasts o Watches & Warnings o Nowcasting o Week 3 & 4 Forecasts o Probabilistic Information 22. Data Cdn Obs US Obs International Obs RWIS stations Weather Alert Zones Cdn FX US FX International FX Postal Codes 2006 600+ 900+ 1600+ 5 410 1000 850+ 1600+ 0 Current 10,000+ 1800+ 3000+ 250+ 1500+ 160,000+ 28,000+ 22,000+ 840,000+ Increasing Volume 2006 to Current 23. Variety and Velocity 24. VARIETY NWP Models > Traditional NWP models > Nowcasting / short-range > Monthly & Seasonal > Ensemble models 25. VOLUME ECMWF > Spacing of grid points: ~16km > Vertical levels: 91 > Number of grid points: 194,804,064 > Time step: 10 minutes > Number of computations required for 10-day forecast: - 6.3 quadrillion!!! (6,300,000,000,000,000 or 6.3 x 1015 computations) 26. Radar Imagery Example 27. Composite Radar Created 28. Active Radar Areas Clipped 29. Radar Layers Merged for Display 30. Radar Imagery Tiled 31. Level 9 & 10 Dynamically created 32. Mapping on a common platform 33. VARIETY Social Media > Twitter & Facebook > Live video streaming 34. VARIETY Social Media Wadena, SK July 19, 2012 35. New Forecast Opportunities Nowcasting 0-6 hour forecasts Week 3 & 4 Forecasts Some skill is achievable Lucrative forecast range in energy sector Probabilistic Information Confidence values, threshold exceedance VELOCITY How can we produce ALL of these forecasts in a reasonable time?! 36. Temperature & Wind Forecast Improvement Project 37. The Mobile Challenge Source: Adobe (Omniture) Site Catalyst, Average Yearly Unique Applications 38. Thank You!!