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06/27/22 1 NWS SCIENCE AND TECHNOLOGY Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology November 3, 2009 GOES Users Conference SIXTH GOES USERS’ CONFERENCE Madison WI

NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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Page 1: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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Leveraging GOES Capabilities to Maximize Response

to User Needs

Leveraging GOES Capabilities to Maximize Response

to User Needs

Don Berchoff, Director Office of Science & TechnologyNovember 3, 2009 GOES Users Conference

Don Berchoff, Director Office of Science & TechnologyNovember 3, 2009 GOES Users Conference

SIXTH GOES USERS’ CONFERENCEMadison WI

Page 2: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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Stroll Down Memory LaneStroll 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

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

Page 3: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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Can’t Imagine Life WithoutSatellite Data

Sustained Real-time Observations of the Atmosphere, Oceans, Land and Sun vital to NOAA Operations and Research

Page 4: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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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

Page 5: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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What Excites Me About GOES-RWhat 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

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

Page 6: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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But We Have Challenges to fully realize possibilities

But We Have Challenges to fully realize possibilities

Huge data explosionRapid data assimilation requirements (e.g.,

NextGen)—people, models

Demands on data management architecture

Data access on-demand within resource constraints

Huge data explosionRapid data assimilation requirements (e.g.,

NextGen)—people, models

Demands on data management architecture

Data access on-demand within resource constraints

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Other

Satellite

Radar

Model

NWS AWIPS SBNIncrease in AWIPS Database in GOES-R Era

w/NPP

w/NPOESS C1

w/GOES-R

w/NPOESS C2

w/GOES-S

w/MPAR

0

2

4

6

8

10

12

14

16

18

20

Dai

ly-M

ea

n D

ata

Ra

te (

Mb

ps

)

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Calendar Year

Page 8: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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But We Have Challenges to fully realize possibilities

But We Have Challenges to fully realize possibilities

Huge data explosionRapid 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

Huge data explosionRapid 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

Page 9: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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The Operational Environment Is Changing

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!

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!

Page 10: NWS SCIENCE AND TECHNOLOGY 11/3/20151 Leveraging GOES Capabilities to Maximize Response to User Needs Don Berchoff, Director Office of Science & Technology

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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 Tornado Warning Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics

Winter Weather Winter Hazards High-Res User-Defined Thresholds Snow band formation and snow intensity

Marine Storm Warnings Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore

Tropical Weather Hurricane Track, Intensity Forecasts

Errors reduced by 50% Causes of rapid intensity changes

Climate Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events

Earth system modeling with ensemble prediction and uncertainty

Severe Weather Tornado Warning Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics

Winter Weather Winter Storm Warning 30 hour LT Snow band formation and snow intensity

Marine Storm Warnings Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore

Tropical Weather Hurricane Track, Intensity Forecasts

Errors reduced by 50% Causes of rapid intensity changes

Climate Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events

Earth system modeling with ensemble prediction and uncertainty

Accuracy >85% out to day 5 Advanced simulations of generation and reactive chemical transport of airborne particulate matter

>90% accuracy, out to day 2 Data Assimilation: Ionosphere, Magnetosphere, and Solar Wind

<5 mins after triggering event Enhanced observations and models

1km resolution, 5 min updates Meteorological influences on renewable and sustainable energy systems

Air Quality Predictions

Geomagnetic Storm Warnings

Tsunami Warnings

Wind Forecasts

Air Quality

Space Weather

Tsunami

Emerging Areas/Surface Wx

Why Are We Doing This…To Improve Services

Why Are We Doing This…To Improve Services

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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 Tornado Warning Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics

Winter Weather Winter Hazards High-Res User-Defined Thresholds Snow band formation and snow intensity

Marine Storm Warnings Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore

Tropical Weather Hurricane Track, Intensity Forecasts

Errors reduced by 50% Causes of rapid intensity changes

Climate Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events

Earth system modeling with ensemble prediction and uncertainty

Severe Weather Tornado Warning Warn on Forecast, LT > 1hr Improved understanding of tornado formation and severe weather microphysics

Winter Weather Winter Storm Warning 30 hour LT Snow band formation and snow intensity

Marine Storm Warnings Probabilistic Warning, LT > 5 days Improve wave model physics from shelf to shore

Tropical Weather Hurricane Track, Intensity Forecasts

Errors reduced by 50% Causes of rapid intensity changes

Climate Seasonal/IA Forecasts Accurate 6 month+ LTs on forcing events

Earth system modeling with ensemble prediction and uncertainty

Accuracy >85% out to day 5 Advanced simulations of generation and reactive chemical transport of airborne particulate matter

>90% accuracy, out to day 2 Data Assimilation: Ionosphere, Magnetosphere, and Solar Wind

<5 mins after triggering event Enhanced observations and models

1km resolution, 5 min updates Meteorological influences on renewable and sustainable energy systems

Air Quality Predictions

Geomagnetic Storm Warnings

Tsunami Warnings

Wind Forecasts

Air Quality

Space Weather

Tsunami

Emerging Areas/Surface Wx

Why Are We Doing This…To Improve Services

Why Are We Doing This…To Improve Services

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

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Why Are We Doing This…Save Lives/Economic Benefits

Why Are We Doing This…Save Lives/Economic Benefits

Service Area Improvements

Potential Benefits

Tropical Cyclone, Track,Intensity, Precip Forecasts

Aviation, Fire, and MarineForecasts

Flood and River Predictions

Air Quality Predictions

Space Weather

Seasonal Climate Forecasts forEnergy, Agriculture, Ecosys, etc

Tornado and Flash FloodWarnings

Reduce $10B/yr in tropcyclone damage

Reduce $1B/yr indamage from severe wx

Reduce $60 B/yr lossesfrom air traffic delays

Reduce $4.3B/yr inflood damage

Reduce mortality from50,000/yr from poor AQ

Reduce $365M/yr inlosses (power industry)

Reduce $7B/yr inlosses (drought)

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Integrated Observation/Analysis System

Integrated Observation/Analysis System

Analysis

Inventory systems,and metadata standards

Assess interdepend-encies, oversampling, gaps, levels of criticality

Current

Individual Systems Public Private Universities

RadarSatelliteSurface; in-SituUpper AirEtc

StrategiesNational 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

Future

Weather Information

Database

Open Architecture

System B

System C

System A

MADIS

IOOS

Integrated Radar System

Rawindsones

Satellites

System B

System C

System A

MADIS

IOOS

Integrated Radar System

Rawindsones

Satellites

Exploit Strengths and Weaknesses of all Data to Optimize Capabilities Synergistically

GOES-R

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Observations and the CubeObservations and the Cube

WIDB Cube

CustomGraphic

Generators

CustomGraphic

Generators

Governmental Decision MakingGovernmental Decision Making

DecisionSupportSystems

DecisionSupportSystems

CustomAlphanumeric

Generators

CustomAlphanumeric

Generators

ObservationsObservationsNumericalPredictionSystems

NumericalPredictionSystems

PostprocessedProbabilistic

Output

PostprocessedProbabilistic

Output

NWSForecaster

NWSForecaster

ForecastingForecasting

Automated ForecastSystems

Automated ForecastSystems

Forecast IntegrationForecast Integration

RadarsRadars

AircraftAircraft

SurfaceSurface

SoundingsSoundings

Private SectorPrivate Sector

Private SectorPrivate Sector

Weather IndustryWeather Industry Private IndustryPrivate Industry

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Building a Road to the FutureBuilding 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!

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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OverviewOverviewOverviewOverview

GOES’ Importance

Environmental and Customer Challenges

NWS Goals

Call to Action

Conclusion