Leveraging GOES Capabilities to Maximize Response to User Needs

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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
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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 cant 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

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Cant Imagine Life WithoutSatellite Data
Sustained Real-time Observations of the
Atmosphere, Oceans, Land and Sun vital to NOAA
Operations and Research
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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

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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 peoples lives and Nations
  economy
• .improved solar/space monitoring and
  forecasts to mitigate impacts to vital national
  infrastructure assets

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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

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NWS AWIPS SBN Increase in AWIPS Database in
GOES-R Era
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18
16
w/MPAR
14
w/GOES-S
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w/GOES-R w/NPOESS C2
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Daily-Mean Data Rate (Mbps)
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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
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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

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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!

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Why Are We Doing ThisTo Improve Services
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Why Are We Doing ThisTo Improve Services

• Our Next Grand Science Challenge
• Huge Economic Impacts
• Enable Warn-on-Forecast

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Why Are We Doing ThisSave 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
Seasonal Climate Forecasts forEnergy,
Agriculture, Ecosys, etc
Reduce 7B/yr inlosses (drought)
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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
Analysis Inventory systems, and metadata
standards Assess interdepend-encies,
oversampling, gaps, levels of criticality
Current Individual Systems Public Private
Universities Radar Satellite Surface
in-Situ Upper Air Etc
Future Weather Information
Database Open Architecture
GOES-R
Exploit Strengths and Weaknesses of all Data to
Optimize Capabilities Synergistically
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Observations and the Cube
Weather Industry
Private Industry
Observations
Forecasting
Private Sector
Numerical Prediction Systems
Network Enabled Operations
Postprocessed Probabilistic Output
NWS Forecaster

Data Integration
Radars
WIDB Cube
Aircraft
Automated Forecast Systems
Surface
Forecast Integration
Soundings
Grids
Custom Graphic Generators
Decision Support Systems
Custom Alphanumeric Generators
Governmental Decision Making
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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!

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Overview

• GOES Importance
• Environmental and Customer Challenges
• NWS Goals
• Call to Action
• Conclusion