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

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

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

Dual-polarization RadarsDual-polarization Radars

From R. Anthes “Global Weather Services in 2025 – UCAR (1999)

Dual Polarization-Scanning Radar

5

National WRT - MPARNational WRT - MPAR

From NSSLwebsite

6

ABI: Improved ResolutionABI: Improved ResolutionToday’s GOES Imager: Simulated “ABI” Spectral Bands:

. . . over a wider spectrum

7

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

8

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

)

9

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)

10

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

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

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

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.

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

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

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)

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

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

19

NOAA ProfilersNOAA Profilers

20

Inter-use and Cross-tasking

Inter-use and Cross-tasking

21

Global Space-based Inter-Calibration System(GSICS)

Global Space-based Inter-Calibration System(GSICS)

22

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

May 13, 2008

23

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

May 13, 2008

24

Atmospheric RiversA key to understanding West Coast

extreme precipitation events

Atmospheric RiversA key to understanding West Coast

extreme precipitation events

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.

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.

27

Solution:Data Integration/Synergy– Blended TPW

Solution:Data Integration/Synergy– Blended TPW

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

29

NextGen - Conceptually

NextGen - Conceptually

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

31

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)

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

33

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

34

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

35

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

Global Cylindrical IR image for Science On a Sphere

Global Cylindrical IR image for Science On a Sphere

A new one-stop source for all GOES-R information.http://www.GOES-R.gov

A joint web site of NOAA and NASA