Rusty Billingsly and Brian Motta National Weather Service NOAA Satellite Science Week May 2012 Rusty Billingsly and Brian Motta National Weather Service.

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Presentation transcript:

Rusty Billingsly and Brian Motta National Weather Service NOAA Satellite Science Week May 2012 Rusty Billingsly and Brian Motta National Weather Service NOAA Satellite Science Week May 2012 Discussion on Training GLM, Total Lightning, Apps Discussion on Training GLM, Total Lightning, Apps

2 GLM Discussion: Issues 1.Operational Severe Weather Forecast/Warning Applications 2.NWS Impact-Based Decision Support Services (public safety for large venues/events) for a WRN 3.Lightning Alerts for dense & prolific cloud-ground strikes 4.Public, Aviation, Fire Weather Applications, Marine Safety

3 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

4 Predict onset of tornadoes, hail, microbursts, flash floods Tornado 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/airport safety routing around thunderstorms, 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 Large venue public safety, HAZMAT safety, & outdoor/marine warnings 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 floods Tornado 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/airport safety routing around thunderstorms, 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 Large venue public safety, HAZMAT safety, & outdoor/marine warnings 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 GOES R Lightning Mapper

Currently Available Training  VISIT CONUS Lightning  VISIT Lightning Meteorology I  VISIT Lightning Meteorology II  COMET Intro to Tropical Met  COMET Fire Weather Climatology  COMET GOES-R Benefits  VISIT GOES-R 101  SPoRT Lightning Mapping Array  SPoRT PGLM Training  VISIT Use of GOES/RSO Imagery w/Remote Sensor Data for Diagnosing Severe Wx

Current Training Activities New activities  Mosaic product (all networks product)  New visualizations (maximum density) New training module  Initial use for NWS  Examples to be converted to PGLM New training has more operational focus Severe weather applications Airport Weather Warnings Lightning safety applications Aim for release before Spring 2012 Update current PGLM training Lightning Safety New Module

AWG Meeting Discussion What you said:What I heard: 1) Forecasters want Intermediate Reduce “black box” for forecasters data/displays for GLMso they understand the processing 2) High flash rates (5-12 per second)Forecasters will see that it’s a can be problematic for algossignificant storm, what is value-add ? 3) Need GLM-compatible flash definitionLMAs provide more detailed data needed to understand storm-scale lightning science/apply GLM flash grid 4) 3D view can show flash initiationThis can be important information for and ground strike locationforecasters to consider 5) Lightning info needs to be integratedOptimal displays are ad-hoc. Need to with radar, vertical profile informationbuild decision-making visualizations 6) Lightning Jump AlgorithmThresholds sensitive to storm environment, type. Tornado signature ? Correlation with svrwx better. 1” hail* 7) Lightning Warning Algo5-min Lightning Prob. Maps, merge LMA and CG point data. Output:CG lightning lead time given 1 st IC. 8) SCAMPER/QPE AlgoCalibrates IR predictors to MW RR What you said:What I heard: 1) Forecasters want Intermediate Reduce “black box” for forecasters data/displays for GLMso they understand the processing 2) High flash rates (5-12 per second)Forecasters will see that it’s a can be problematic for algossignificant storm, what is value-add ? 3) Need GLM-compatible flash definitionLMAs provide more detailed data needed to understand storm-scale lightning science/apply GLM flash grid 4) 3D view can show flash initiationThis can be important information for and ground strike locationforecasters to consider 5) Lightning info needs to be integratedOptimal displays are ad-hoc. Need to with radar, vertical profile informationbuild decision-making visualizations 6) Lightning Jump AlgorithmThresholds sensitive to storm environment, type. Tornado signature ? Correlation with svrwx better. 1” hail* 7) Lightning Warning Algo5-min Lightning Prob. Maps, merge LMA and CG point data. Output:CG lightning lead time given 1 st IC. 8) SCAMPER/QPE AlgoCalibrates IR predictors to MW RR

8 Other Gov’t Decision Makers WFO LZK WFO SGFWFO LSX WFO PAH Paducah EOC NWSTD Facilitator(s) Collaborative simulations and interagency exercises: partnerships, teamwork and decision support Intraoffice and interoffice simulations (like FEMA, NASA/SMG, DOD) Wash. Co. EOC Vision 2014: Distributed and Collaborative Simulations Focused on Human Factors and Decision-Support Internet WAN/NOAAnet/VRF NCEP & RFCs