Leveraging GOES Capabilities to Maximize Response to User Needs

1. Leveraging GOES Capabilities to Maximize Response to User Needs SIXTH GOES USERS’ CONFERENCE Madison WI Don Berchoff, Director Office of Science & TechnologyNovember 3, 2009 GOES Users Conference

2. Stroll Down Memory Lane • GOES 1 (1975): Imagery; cloud drift derived winds and temperatures; space environmental monitor • GOES 4 (1980): Atmospheric sounder added (temperature and moisture), but can’t image and sound simultaneously • GOES 7 (1987): Distress signals (testing) • GOES 8 (1994): Flexible scanning, high resolution images and simultaneous imaging and sounding • GOES 12 (2001): Solar X-Ray Imager

3. Can’t Imagine Life WithoutSatellite Data Sustained Real-time Observations of the Atmosphere, Oceans, Land and Sun vital to NOAA Operations and Research

4. Lifeblood of Operators andResearchers • Detect, characterize, warn, track • Hurricanes • Severe or possibly tornadic storms • Flash flood producing weather systems • Analysis and forecasting • Surface temperatures (sea and land), winds, atmospheric stability, soundings, air quality, hazards • Numerical models: Data assimilation...radiances, soundings • Ocean environment monitoring • Climate monitoring/continuity • Environmental data collection – buoys, rain gauges, river levels, ecosystem monitoring

5. What Excites Me About GOES-R • Possibilities for: ….greater high impact event warning lead times to reduce loss of life and property ….storm-scale modeling and forecasts critical to enhancing people’s lives and Nation’s economy ….improved solar/space monitoring and forecasts to mitigate impacts to vital national infrastructure assets

6. But We Have Challenges to fully realize possibilities • Huge data explosion • Rapid data assimilation requirements (e.g., NextGen)—people, models • Demands on data management architecture • Data access on-demand within resource constraints

7. Other Satellite Radar Model NWS AWIPS SBN Increase in AWIPS Database in GOES-R Era 20 18 16 w/MPAR 14 w/GOES-S 12 w/GOES-R w/NPOESS C2 10 Daily-Mean Data Rate (Mbps) 8 w/NPOESS C1 6 w/NPP 4 2 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Calendar Year

8. But We Have Challenges to fully realize possibilities • Huge data explosion • Rapid data assimilation requirements (e.g., NextGen)—people, models • Demands on data management architecture • Data access on-demand within resource constraints • Integrating all observing data sources to achieve desired effect and outcome

9. The Operational Environment Is Changing • Speed at which decisions are made • Demand for decision support services is increasing • US industry needs the most accurate, accessible, timely and reliable weather data to make critical decisions that impact our national economy • Aviation weather impacts were $41B in 2007 • U.S. modeling and data assimilation critical for giving the U.S. a competitive advantage in the global economy • Federal deficits and resource constraints • Integrated observations • More efficient R-T-O (projects, modeling) • Every dollar counts!

10. Science Service Area Key Products/Services S&T Goal 2025Examples Research Needs and Opportunities: Examples Fire Weather Red Flag Warning >24hr Lead Time (LT) with 95% POD Simulations (high-resolution) of integrated fire weather/behavior Hydrology Inundation Forecasts Dependable Street Scale Probabilistic Warnings Physically based hydrologic models and ensembles Aviation Convection Initiation 30 mins LT Initiation and evolution of convection Severe Weather Severe Weather Tornado Warning Tornado Warning Warn on Forecast, LT > 1hr Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics Improved understanding of tornado formation and severe weather microphysics Winter Weather Winter Weather Winter Hazards Winter Storm Warning 30 hour LT High-Res User-Defined Thresholds Snow band formation and snow intensity Snow band formation and snow intensity Marine Marine Storm Warnings Storm Warnings Probabilistic Warning, LT > 5 days Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore Improve wave model physics from shelf to shore Tropical Weather Tropical Weather Hurricane Track, Intensity Forecasts Hurricane Track, Intensity Forecasts Errors reduced by 50% Errors reduced by 50% Causes of rapid intensity changes Causes of rapid intensity changes Climate Climate Seasonal/IA Forecasts Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events Accurate 6 month+ LTs on forcing events Earth system modeling with ensemble prediction and uncertainty Earth system modeling with ensemble prediction and uncertainty Air Quality Accuracy >85% out to day 5 Advanced simulations of generation and reactive chemical transport of airborne particulate matter Air Quality Predictions Space Weather >90% accuracy, out to day 2 Data Assimilation: Ionosphere, Magnetosphere, and Solar Wind Geomagnetic Storm Warnings Tsunami <5 mins after triggering event Enhanced observations and models Emerging Areas/Surface Wx 1km resolution, 5 min updates Meteorological influences on renewable and sustainable energy systems Tsunami Warnings Wind Forecasts Why Are We Doing This…To Improve Services

11. Science Service Area Key Products/Services S&T Goal 2025Examples Research Needs and Opportunities: Examples Fire Weather Red Flag Warning >24hr Lead Time (LT) with 95% POD Simulations (high-resolution) of integrated fire weather/behavior Hydrology Inundation Forecasts Dependable Street Scale Probabilistic Warnings Physically based hydrologic models and ensembles Aviation Convection Initiation 30 mins LT Initiation and evolution of convection Severe Weather Severe Weather Tornado Warning Tornado Warning Warn on Forecast, LT > 1hr Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics Improved understanding of tornado formation and severe weather microphysics Winter Weather Winter Weather Winter Hazards Winter Storm Warning 30 hour LT High-Res User-Defined Thresholds Snow band formation and snow intensity Snow band formation and snow intensity Marine Marine Storm Warnings Storm Warnings Probabilistic Warning, LT > 5 days Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore Improve wave model physics from shelf to shore Tropical Weather Tropical Weather Hurricane Track, Intensity Forecasts Hurricane Track, Intensity Forecasts Errors reduced by 50% Errors reduced by 50% Causes of rapid intensity changes Causes of rapid intensity changes Climate Climate Seasonal/IA Forecasts Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events Accurate 6 month+ LTs on forcing events Earth system modeling with ensemble prediction and uncertainty Earth system modeling with ensemble prediction and uncertainty Air Quality Accuracy >85% out to day 5 Advanced simulations of generation and reactive chemical transport of airborne particulate matter Air Quality Predictions Space Weather >90% accuracy, out to day 2 Data Assimilation: Ionosphere, Magnetosphere, and Solar Wind Geomagnetic Storm Warnings Tsunami <5 mins after triggering event Enhanced observations and models Emerging Areas/Surface Wx 1km resolution, 5 min updates Meteorological influences on renewable and sustainable energy systems Tsunami Warnings Wind Forecasts Why Are We Doing This…To Improve Services • Our Next Grand Science Challenge • Huge Economic Impacts • Enable Warn-on-Forecast

12. Why Are We Doing This…Save Lives/Economic Benefits Service Area Improvements Potential Benefits Reduce $10B/yr in tropcyclone damage Tropical Cyclone, Track,Intensity, Precip Forecasts Reduce $1B/yr indamage from severe wx Tornado and Flash FloodWarnings Reduce $60 B/yr lossesfrom air traffic delays Aviation, Fire, and MarineForecasts Reduce $4.3B/yr inflood damage Flood and River Predictions Reduce mortality from50,000/yr from poor AQ Air Quality Predictions Reduce $365M/yr inlosses (power industry) Space Weather Reduce $7B/yr inlosses (drought) Seasonal Climate Forecasts forEnergy, Agriculture, Ecosys, etc

13. Integrated Observation/Analysis System Strategies National Mesonet Network of networks Integrated Radar (Lidar, gap-fillers, MPAR) Global Systems Multisensor platforms Optimization with OSEs, OSSEs Standards, Architectures, Protocols Maximize value of investment Current Individual Systems Public Private Universities Radar Satellite Surface; in-Situ Upper Air Etc Analysis Inventory systems, and metadata standards Assess interdepend-encies, oversampling, gaps, levels of criticality Future Weather Information Database Open Architecture GOES-R Exploit Strengths and Weaknesses of all Data to Optimize Capabilities Synergistically

14. Observations and the Cube Weather Industry Private Industry Observations Forecasting Private Sector Numerical Prediction Systems Network Enabled Operations Postprocessed Probabilistic Output NWS Forecaster Radars Data Integration WIDB Cube Aircraft Automated Forecast Systems Surface ForecastIntegration Soundings Grids Decision Support Systems Custom Graphic Generators Custom Alphanumeric Generators Governmental Decision Making

15. Building a Road to the Future • GOES has proven its operational value • GOES-R is bringing exciting new capabilities • Significantly more robust enabling technologies and architectures are needed • Strong partnerships are an essential part of reaching NOAA Goals!

17. Overview • GOES’ Importance • Environmental and Customer Challenges • NWS Goals • Call to Action • Conclusion