Brian Motta National Weather Service/Training Division
description
Transcript of Brian Motta National Weather Service/Training Division
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Brian Motta
National Weather Service/Training Division
GOES-R Lightning Science Team and AWG Meeting
September 2009
Brian Motta
National Weather Service/Training Division
GOES-R Lightning Science Team and AWG Meeting
September 2009
Discussion on Training Module on Total Lightning and GLM
Discussion on Training Module on Total Lightning and GLM
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Boulder Meeting Dates @ COMET
Boulder Meeting Dates @ COMET
AMS Tropical Conference, Tucson 10-14 May 2010
SRSST, Satellite Curriculum Development Workshop, GOES-R Proving Ground 17-25 May 2010
AMS Tropical Conference, Tucson 10-14 May 2010
SRSST, Satellite Curriculum Development Workshop, GOES-R Proving Ground 17-25 May 2010
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NOAA – COMET – EUMETSAT - WMOWorking Together for Satellite Training
NOAA – COMET – EUMETSAT - WMOWorking Together for Satellite Training
GOES Proving GOES Proving GroundGround
Users & DevelopersUsers & Developers
NOAA/EUMETSAT NOAA/EUMETSAT Bilateral ProgramBilateral Program
VISIT (CIRA/CIMSS)VISIT (CIRA/CIMSS)SPoRTSPoRT
UCAR/COMETUCAR/COMET
WMO (VL & FG) WMO (VL & FG) NWS Training NWS Training DivisionDivision
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Dual-polarization RadarsDual-polarization Radars
From R. Anthes “Global Weather Services in 2025 – UCAR (1999)
Dual Polarization-Scanning Radar
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National WRT - MPARNational WRT - MPAR
From NSSLwebsite
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ABI: Improved ResolutionABI: Improved ResolutionToday’s GOES Imager: Simulated “ABI” Spectral Bands:
. . . over a wider spectrum
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Increased PerformanceIncreased Performance
Performance Capability GOES I-M GOES N-P GOES RImaging Visible Resolution 1 km 1 km 0.5 km
IR Resolution 4-8 km4-8 km N4 km O/P
1-2 km
Full Disk Coverage Rate 30 min 30 min 5 min # of Channels 5 5 16Solar Monitoring GOES-M only Yes YesLightning Detection No No YesOperate through Eclipse No Yes YesGround System Backup Limited Limited LimitedArchive and Access Limited Limited YesRaw Data Volume per spacecraft 2.6 Mbps 2.6 Mbps 75 Mbps
GOES-R maintains continuity of the GOES mission GOES-R also provides significant increases in spatial,
spectral, and temporal resolution of products
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Geostationary Lightning Mapper (GLM)
Geostationary Lightning Mapper (GLM)
• Detects total strikes: in cloud, cloud to cloud, and cloud to ground
– Compliments today’s land based systems that only measures cloud to ground (about 15% of the total lightning)
• Increased coverage over oceans and land
– Currently no ocean coverage, and limited land coverage in dead zones
-250 -200 -150 -100 -50 0 50
-80
-60
-40
-20
0
20
40
60
80
longitude (deg)
latit
ude
(deg
)
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Predict onset of tornadoes, hail, microbursts, flash floodsTornado lead time -13 min national average, improvement desired
Track thunderstorms, warn of approaching lightning threats
Lightning strikes responsible for >500 injuries per year
90% of victims suffer permanent disabilities, long term health problems, chiefly neurological Lightning responsible for 80 deaths per year (second leading source after flooding)
Improve airline routing around thunderstorms, safety, saving fuel, reducing delays
In-cloud lightning lead time of impending ground strikes, often 10 min or more
Provide real-time hazard information, improving efficiency of emergency management
NWP/Data Assimilation
Locate lightning strikes known to cause forest fires, reduce response times
Multi-sensor precipitation algorithms
Assess role of thunderstorms and deep convection in global climate
Seasonal to interannual (e.g., ENSO) variability of lightning and extreme weather (storms)
Provide new data source to improve air quality / chemistry forecasts
Predict onset of tornadoes, hail, microbursts, flash floodsTornado lead time -13 min national average, improvement desired
Track thunderstorms, warn of approaching lightning threats
Lightning strikes responsible for >500 injuries per year
90% of victims suffer permanent disabilities, long term health problems, chiefly neurological Lightning responsible for 80 deaths per year (second leading source after flooding)
Improve airline routing around thunderstorms, safety, saving fuel, reducing delays
In-cloud lightning lead time of impending ground strikes, often 10 min or more
Provide real-time hazard information, improving efficiency of emergency management
NWP/Data Assimilation
Locate lightning strikes known to cause forest fires, reduce response times
Multi-sensor precipitation algorithms
Assess role of thunderstorms and deep convection in global climate
Seasonal to interannual (e.g., ENSO) variability of lightning and extreme weather (storms)
Provide new data source to improve air quality / chemistry forecasts
GLM GLM (Geostationary Lightning Mapper)(Geostationary Lightning Mapper)
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Firehose or raging river ?Firehose or raging river ? Leverage Human Role in Process
Early warnings (warn on forecast)Super-resolution accuracy and precision in space/time Increased warning lead timesDecision assistance
Disparate pieces of complimentary data from operational and experimental systems Smarter data assimilation and modeling systems Consolidation of products, parameters, satellites, instruments, and visualizations has already begun
Leverage Human Role in ProcessEarly warnings (warn on forecast)Super-resolution accuracy and precision in space/time Increased warning lead timesDecision assistance
Disparate pieces of complimentary data from operational and experimental systems Smarter data assimilation and modeling systems Consolidation of products, parameters, satellites, instruments, and visualizations has already begun
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The Grand ChallengeThe Grand Challenge
• Use data before lead time vanishes
• Warn on forecast concept
• Ingest, process, calibrate, correct, geolocate, transmit, display, fuse/combine within 5-30 seconds of observation so uncertainty can be determined and decisions can be made
• Use data before lead time vanishes
• Warn on forecast concept
• Ingest, process, calibrate, correct, geolocate, transmit, display, fuse/combine within 5-30 seconds of observation so uncertainty can be determined and decisions can be made
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Forecasters’Satellite Requirements
Forecasters’Satellite Requirements
LatencyLatency- Currently, not adequate to meet operational requirements for polar & derived products - Currently, not adequate to meet operational requirements for polar & derived products - Increased availability is necessary for consistent use by operational forecasters- Increased availability is necessary for consistent use by operational forecasters
AWIPS AvailabilityAWIPS Availability- Some useful, operational imagery/data/products currently unavailable in AWIPS - Some useful, operational imagery/data/products currently unavailable in AWIPS (primary NWS tool used to issue forecasts, watches, warnings and advisories)(primary NWS tool used to issue forecasts, watches, warnings and advisories)- Plans must be made to incorporate new satellite products in AWIPS- Plans must be made to incorporate new satellite products in AWIPS- NWS/NESDIS needs to demo increased satellite capabilities- NWS/NESDIS needs to demo increased satellite capabilities
Ease of AWIPS Interrogation- Currently, several step process to get POES & GOES sounder data. Should be click- and-go process. Need an interactive sampling capability (similar to today’s pop-up SkewT). Especially true of GOES sounder data.
Quick turnaround from research to operations- Too cumbersome and time consuming to get new data in AWIPS. Used LDM as “backdoor” for some products, this should not have to be SOP.- GOES proving ground and AWIPS2 could be very helpful.
BANDWIDTH
LatencyLatency- Currently, not adequate to meet operational requirements for polar & derived products - Currently, not adequate to meet operational requirements for polar & derived products - Increased availability is necessary for consistent use by operational forecasters- Increased availability is necessary for consistent use by operational forecasters
AWIPS AvailabilityAWIPS Availability- Some useful, operational imagery/data/products currently unavailable in AWIPS - Some useful, operational imagery/data/products currently unavailable in AWIPS (primary NWS tool used to issue forecasts, watches, warnings and advisories)(primary NWS tool used to issue forecasts, watches, warnings and advisories)- Plans must be made to incorporate new satellite products in AWIPS- Plans must be made to incorporate new satellite products in AWIPS- NWS/NESDIS needs to demo increased satellite capabilities- NWS/NESDIS needs to demo increased satellite capabilities
Ease of AWIPS Interrogation- Currently, several step process to get POES & GOES sounder data. Should be click- and-go process. Need an interactive sampling capability (similar to today’s pop-up SkewT). Especially true of GOES sounder data.
Quick turnaround from research to operations- Too cumbersome and time consuming to get new data in AWIPS. Used LDM as “backdoor” for some products, this should not have to be SOP.- GOES proving ground and AWIPS2 could be very helpful.
BANDWIDTH
Thanks to Frank Alsheimer and Jon Jelsema, CHS, SC
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Training IdeasTraining Ideas
Keep in mind that operational forecasters are constantly bombarded with information from satellite, radar, mesonets, unofficial observations, model guidance (both deterministic and ensemble), etc. It easily can become a data management nightmare.
To get forecasters to use any particular set of data, it must:Be easily available, on-demand, 24/7Be understandable, accurate, reliable,Be proven superior to (or at least equal to) other options for the problem at hand
Prior to Data Availability
The science behind the data High resolution simulations in AWIPS Improvements due to data inclusion Current data gaps/solutions
After Data is AvailableCase studies/simulations on AWIPS/WES showing satellite data led to improved decision-making. Current DLAC2 is a good example.
Keep in mind that operational forecasters are constantly bombarded with information from satellite, radar, mesonets, unofficial observations, model guidance (both deterministic and ensemble), etc. It easily can become a data management nightmare.
To get forecasters to use any particular set of data, it must:Be easily available, on-demand, 24/7Be understandable, accurate, reliable,Be proven superior to (or at least equal to) other options for the problem at hand
Prior to Data Availability
The science behind the data High resolution simulations in AWIPS Improvements due to data inclusion Current data gaps/solutions
After Data is AvailableCase studies/simulations on AWIPS/WES showing satellite data led to improved decision-making. Current DLAC2 is a good example.
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Info Systems DevelopmentInfo Systems Development
ESRL / GSD Prototypes and Activities• Next Generation Warning Tool
• Workshop October 27-29 @ GSD• http://fxa.noaa.gov/NGWT/NGWT_Workshop.html
• Probabilistic Workstation Development• Geo-Targeted Alerting System (GTAS)
•WFO-run Hysplit• Blended Total Precipitable Water
•(GSD-GPS, PSD-Science, CIRA, CIMSS)• Flow-following finite-volume Icosahedral Model (FIM)• Rapid Refresh RUC (RRR)
USWRP/NOAA Testbeds• HydroMeteorology Testbed & Others
AWIPS II Remote Sensing/Vis, IV&V, TIMs
ESRL / GSD Prototypes and Activities• Next Generation Warning Tool
• Workshop October 27-29 @ GSD• http://fxa.noaa.gov/NGWT/NGWT_Workshop.html
• Probabilistic Workstation Development• Geo-Targeted Alerting System (GTAS)
•WFO-run Hysplit• Blended Total Precipitable Water
•(GSD-GPS, PSD-Science, CIRA, CIMSS)• Flow-following finite-volume Icosahedral Model (FIM)• Rapid Refresh RUC (RRR)
USWRP/NOAA Testbeds• HydroMeteorology Testbed & Others
AWIPS II Remote Sensing/Vis, IV&V, TIMs
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DSS User Training Requirements
1) Highly Relevant – Decision Focused2) Time-limited3) New spatial and temporal demands4) Real-time decision support5) Just-In-Time training6) Uncertainty/probability information7) Simulations needed
DSS User Training Requirements
1) Highly Relevant – Decision Focused2) Time-limited3) New spatial and temporal demands4) Real-time decision support5) Just-In-Time training6) Uncertainty/probability information7) Simulations needed
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Training:Total LightningTraining:Total Lightning
Currently, 4 CWAs with LMAs
Each has own story
Archived data sets ?
Any WES cases ?
Decision-making Scenarios
Service impacts (eg, POD, leadtimes, etc)
Starting point(s)
Currently, 4 CWAs with LMAs
Each has own story
Archived data sets ?
Any WES cases ?
Decision-making Scenarios
Service impacts (eg, POD, leadtimes, etc)
Starting point(s)
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Training: GLMTraining: GLM
Algos
Proxy Data – Need Inventory
Simulated data (w/other GOES-R)
Scenarios
Impacts
Concepts
Visualizations
Decision-making
Algos
Proxy Data – Need Inventory
Simulated data (w/other GOES-R)
Scenarios
Impacts
Concepts
Visualizations
Decision-making
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AWG Meeting DiscussionAWG Meeting Discussion
What you said: What I heard:1) Forecasters want Intermediate Reduce “black box” for forecasters data/displays for GLM so they understand the processing2) High flash rates (5-12 per second) Forecasters will see that it’s acan be problematic for algos significant storm, what is value-add ?3) Need GLM-compatible flash definition LMAs provide more detailed data
needed to understand storm-scalelightning science/apply GLM flash grid
4) 3D view can show flash initiation This can be important information forand ground strike location forecasters to consider5) Lightning info needs to be integrated Optimal displays are ad-hoc. Need to with radar, vertical profile information build decision-making visualizations6) Lightning Jump Algorithm Thresholds sensitive to storm
environment, type. Tornado signature ?Correlation with svrwx better. 1” hail*
7) Lightning Warning Algo 5-min Lightning Prob. Maps, merge LMAand CG point data. Output:CG lightninglead time given 1st IC.
8) SCAMPER/QPE Algo Calibrates IR predictors to MW RR
What you said: What I heard:1) Forecasters want Intermediate Reduce “black box” for forecasters data/displays for GLM so they understand the processing2) High flash rates (5-12 per second) Forecasters will see that it’s acan be problematic for algos significant storm, what is value-add ?3) Need GLM-compatible flash definition LMAs provide more detailed data
needed to understand storm-scalelightning science/apply GLM flash grid
4) 3D view can show flash initiation This can be important information forand ground strike location forecasters to consider5) Lightning info needs to be integrated Optimal displays are ad-hoc. Need to with radar, vertical profile information build decision-making visualizations6) Lightning Jump Algorithm Thresholds sensitive to storm
environment, type. Tornado signature ?Correlation with svrwx better. 1” hail*
7) Lightning Warning Algo 5-min Lightning Prob. Maps, merge LMAand CG point data. Output:CG lightninglead time given 1st IC.
8) SCAMPER/QPE Algo Calibrates IR predictors to MW RR
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NOAA ProfilersNOAA Profilers
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Inter-use and Cross-tasking
Inter-use and Cross-tasking
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Global Space-based Inter-Calibration System(GSICS)
Global Space-based Inter-Calibration System(GSICS)
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NOAA Developmental Test BedsNOAA Developmental Test Beds
GOES-R Proving GroundHazardous Weather Test Bed (HWT)National Wx Radar Test Bed (NWRT) Hydrometeorological Test Bed (HMT) NOAA, Navy, NASA Joint Hurricane Test Bed (JHT)Climate Test Bed Aviation Weather Test BedNOAA/NCAR Development Test Center (DTC-WRF)Joint Center for Satellite Data Assimilation (JCSDA)NOAA/USAF Space Weather Test BedUnmanned Aircraft Systems Test BedShort-Term Prediction Research/Transition (NASA/SPoRT)NextGen Transportation System – Weather Incubators
GOES-R Proving GroundHazardous Weather Test Bed (HWT)National Wx Radar Test Bed (NWRT) Hydrometeorological Test Bed (HMT) NOAA, Navy, NASA Joint Hurricane Test Bed (JHT)Climate Test Bed Aviation Weather Test BedNOAA/NCAR Development Test Center (DTC-WRF)Joint Center for Satellite Data Assimilation (JCSDA)NOAA/USAF Space Weather Test BedUnmanned Aircraft Systems Test BedShort-Term Prediction Research/Transition (NASA/SPoRT)NextGen Transportation System – Weather Incubators
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May 13, 2008
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NOAA’s Hydrometeorological Testbed NOAA’s Hydrometeorological Testbed (HMT) Program(HMT) Program
Major Activity AreasMajor Activity Areas
• Quantitative Quantitative Precipitation Estimation Precipitation Estimation (QPE)(QPE)• Quantitative Quantitative Precipitation Forecasts Precipitation Forecasts (QPF)(QPF)• Snow level and snow Snow level and snow packpack• Hydrologic Applications Hydrologic Applications & Surface Processes& Surface Processes• Decision Support ToolsDecision Support Tools• VerificationVerification• Enhancing & Enhancing & Accelerating Research to Accelerating Research to OperationsOperations• Building partnershipsBuilding partnerships
A National Testbed Strategy with Regional ImplementationA National Testbed Strategy with Regional ImplementationA National Testbed Strategy with Regional ImplementationA National Testbed Strategy with Regional Implementation
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May 13, 2008
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Atmospheric RiversA key to understanding West Coast
extreme precipitation events
Atmospheric RiversA key to understanding West Coast
extreme precipitation events
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Pacific Mission Requirements
Atmospheric Rivers
Pacific Mission Requirements
Atmospheric RiversNOAA has the operational responsibility for providing accurate forecasts of precipitation and flooding in the western USAtmospheric river events observed to have a connection to severe flooding events in the western coastal regionsObservations look to provide a new resource to forecasters and a new decision support toolThe mission is central to the Pacific Testbed focus on water, its distribution, and impacts in the western US
NOAA has the operational responsibility for providing accurate forecasts of precipitation and flooding in the western USAtmospheric river events observed to have a connection to severe flooding events in the western coastal regionsObservations look to provide a new resource to forecasters and a new decision support toolThe mission is central to the Pacific Testbed focus on water, its distribution, and impacts in the western US
Atmosphericriver
Ralph et al., GRL, 2006.
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The HMT ConceptTestbed as a Process
The HMT ConceptTestbed as a Process
Marty RalphNOAA/ETL-PACJET
See: Dabberdt et. al. 2005 Bull. Amer. Meteor. Soc.
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Solution:Data Integration/Synergy– Blended TPW
Solution:Data Integration/Synergy– Blended TPW
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Blended TPW ProductOperational
Implementation
Blended TPW ProductOperational
ImplementationLessons Learned
• What started as a global SSM/I and AMSU TPW blend wasexpanded to include GOES TPW and GPS-Met data
• Blend became problematic as smaller-scale over-land variations
required more quality control and time for forecaster investigation of short-term signatures
• Forecasters uncomfortable with “black box” processing, prefer access to intermediate products and processing should questions arise
• Solid approach to science forms the foundation for forecasters to exploit new technology, key observations, better uncertainty info for decision makers
• Key briefing product in event timing, location, severity
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NextGen - Conceptually
NextGen - Conceptually
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Tools/Technology/Concepts
Tools/Technology/Concepts
Next Generation Joint Weather Requirements
(Joint Development Project Office)
Substantial leapfrog in communication,
processing investments
Data fusion, visualization, graphical layering,
transparency, volumes, only glimpsed by
today’s progressive disclosure display
clients like Google Earth
Immediate all-media probabilistic product
generation and distribution
Next Generation Joint Weather Requirements
(Joint Development Project Office)
Substantial leapfrog in communication,
processing investments
Data fusion, visualization, graphical layering,
transparency, volumes, only glimpsed by
today’s progressive disclosure display
clients like Google Earth
Immediate all-media probabilistic product
generation and distribution
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Virtual 4D Weather Cube
Virtual 4D Weather Cube
Virtual 4D Virtual 4D Weather Weather
CubeCube
4th
dimensiontime
HazardHazard
ObservationObservation
0 – 15 mins0 – 15 mins
15-60mins15-60mins
1- 24 hrs1- 24 hrs
Aviation weather informationin 3 dimensions
( latitude/longitude/height)
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NESDIS GOES Program Office
NPOESS JPO
NESDIS STAR, Mitch Goldberg
WMO Space Programme
NOAA/NSSL
UCAR/NCAR/Dr. Rick Anthes
NWA Remote Sensing Comm.
Colorado State University/CIRA
U Wisconsin/SSEC/CIMSS
NWS/Office Science Tech
NOAA/Earth Sys Research Lab
NESDIS GOES Program Office
NPOESS JPO
NESDIS STAR, Mitch Goldberg
WMO Space Programme
NOAA/NSSL
UCAR/NCAR/Dr. Rick Anthes
NWA Remote Sensing Comm.
Colorado State University/CIRA
U Wisconsin/SSEC/CIMSS
NWS/Office Science Tech
NOAA/Earth Sys Research Lab
Thank You / Acknowledgements
Thank You / Acknowledgements
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The 4-D Cube:A Conceptual Model
The 4-D Cube:A Conceptual Model
4D Wx Cube
CustomGraphic
Generators
CustomGraphic
Generators
Integration into User DecisionsIntegration into User Decisions
DecisionSupportSystems
DecisionSupportSystems
CustomAlphanumericGenerators
CustomAlphanumericGenerators
ObservationsObservationsNumericalModelingSystems
NumericalModelingSystems
StatisticalForecasting
Systems
StatisticalForecasting
Systems
NWSForecaster
NWSForecaster
ForecastingForecasting
Automated ForecastSystems
Automated ForecastSystems
Forecast IntegrationForecast Integration
4D WxSAS
RadarsRadars
AircraftAircraft
SurfaceSurface
SatellitesSatellites
SoundingsSoundings
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NextGen 101NextGen 101
TodayToday NextGenNextGen(new requirements)(new requirements)
Not integrated into aviation Not integrated into aviation decision support systems decision support systems (DSS)(DSS)
Inconsistent/conflicting on a Inconsistent/conflicting on a national scalenational scale
Low temporal resolution (for Low temporal resolution (for aviation decision making aviation decision making purposes)purposes)
Disseminated in minutesDisseminated in minutes
Updated by scheduleUpdated by schedule
Fixed product formats Fixed product formats (graphic or text)(graphic or text)
Totally integrated into DSSTotally integrated into DSS
Nationally consistentNationally consistent
High temporal resolutionHigh temporal resolution
Disseminated in secondsDisseminated in seconds
Updated by events Updated by events
Flexible formatsFlexible formats
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NextGen 101;Key Themes
NextGen 101;Key Themes
An integrated and nationally consistent common weather picture for observation, analysis, and forecast data available to all system users
An integrated and nationally consistent common weather picture for observation, analysis, and forecast data available to all system users
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Global Cylindrical IR image for Science On a Sphere
Global Cylindrical IR image for Science On a Sphere
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A new one-stop source for all GOES-R information.http://www.GOES-R.gov
A joint web site of NOAA and NASA
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