1 Pre-launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond Steven Goodman, NOAA/NESDIS/ORA Richard.

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Presentation transcript:

1 Pre-launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond Steven Goodman, NOAA/NESDIS/ORA Richard Blakeslee and William Koshak, NASA/MSFC  GLM Overview and Heritage  GLM Risk Reduction  GLM Algorithms & Applications  Summary and Next Steps Data from the NASA OTD and LIS instruments Global Distribution of Lightning OUTLINE OF TALK 3 rd MALD, AMS Annual Meeting, January 20-24, 2008

2 Observational Requirement LEVELLEVEL Geo Coverage Vert. Res Horiz. Res Mapping Accuracy Msmnt Range Msmnt. Accuracy Refresh Rate Data Latency Lightning Detection TCONUS Sfc to cloud top 10 km5 km Real Time 70-90% Detection Contin- uous < 1 min Lightning Detection THemispheric Sfc to cloud top 10 km5 km Real Time 70-90% Detection Contin- uous < 1 min Lightning Detection TMesoscale Sfc to cloud top 10 km5 km Real Time 70-90% Detection Contin- uous < 1min Provide continuous, full-disk lightning measurements for storm warning and nowcasting. Provide early warning of tornadic activity. Accumulate a long-term database to track decadal changes of lightning. GLM Overview and Heritage

3  Heritage: Lightning Mapper Sensor for GOES-M, NASA EOS LIS/OTD  NASA Lead Role for Instrument »NOAA Funded »RFP Released 26 July 2005 »Formulation Studies (3) Completed March 2007 »Implementation Phase Contract Valued at $96.7M Awarded to Lockheed Martin Space Systems Company December 2007 –1 Prototype Model –4 Flight Models  NOAA Lead Role for Ground System »GOES-R Risk Reduction- Science Team - August 2006 »Algorithm Working Group- Lightning Applications Team - June 2007 –ATBD, Algorithm Theoretical Basis Document, March 2008 –Proxy data from the NASA Lightning Imaging Sensor/TRMM and Regional Test Beds (e.g., US Lightning Mapping Arrays- North Alabama, Washington, DC, Oklahoma) GLM Implementation Status (January 2008) The GLM is a single channel, near-IR imager/transient detector used to measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system

4 LISOTD TRMM boost completed August 22, Present OTDLIS Pre-boostLIS post-boost GLM Inclination70°35° 0°0° Altitude735 km350 km402 km 42,164 km FOV (across)1253 km583 km668 km Full-disk FOV (diagonal)1934 km870 km1001 km 18,000 km Pixel FOV (nadir)7.9 km3.7 km4.3 km 8 km Pixel FOV (corner)25.9 km10.3 km12.0 km 12 km Observation time190 s80 s92 s Continuous Orbital Period99.5 min91.5 min92.56 min 1436 min

5 Mean annual global lightning flash rate (flashes km -2 yr -1 ) derived from a combined 8 years from April 1995 to February (Data from the NASA OTD instrument on the OrbView-1 satellite and the LIS instrument on the TRMM satellite.) Goodman et al., Lightning, in Our Changing Planet: A View from Space, M. King, et al., ed., Cambridge University Press Global Distribution of Lightning Activity LIS Overpass Australia

Mapping storm initiation, growth, decay  TRMM provides us a huge database of paired lightning, radar, IR and passive microwave observations (training, validation)  Over entire tropics & subtropics (generalization)  Total lightning increases as storm intensifies – can increase lead time for warning of severe and tornadic storms TRMM LIS-Lightning: May 1999 Stroud, OK Tornado GOES-R GLM Perspective 1-min total lightning activity

7 Hurricane Katrina: Lightning Imaging Sensor (LIS) 24 Aug Aug Aug Aug 05 Los Alamos Sferics Array, August 28, 2005, Shao et al., EOS Trans., 86 How does lightning activity vary as TC/Hurricane undergoes intensity change? Is there a useful predictor? LIS Background Images read out once per min 4 km nm Orbit swath 600 km

8 GLM Risk Reduction  Analysis of proxy data from TRMM LIS and VHF Lightning Mapping Arrays  Test beds to collect comprehensive data on storms to advance the science  Forecaster assessments of total lightning data in the Warning Decision-Making Process

9 Algorithm Development Strategy Candidate Algorithms Testing and Validation - Proxy and Simulated Data - Algorithm Selection - Demonstrating Algorithm Performance

10 Candidate Algorithms  Clustering Algorithm »Description: takes events and creates groups and flashes –Pros: TRMM LIS and OTD heritage –Cons: non yet extended/optimal for GEO  Cell Tracking Algorithm –Pros: LISDAD and RDT heritage, SCIT has limitations –Cons: technically challenging, needs AWIPS implementation for optimal utility  Flash Trending “Jump” Algorithm »Description: trends flash rates with time for individual storms –Pros: Prototype in development and promising – increase lead time –Cons: Requires additional research, needs AWIPS implementation for optimal utility  Other Application Team Uses of GLM »Hydology-Precipitation, AQ-NOx/Ozone, Clouds-Cloud Type/TRW, Hurricane Intensification, Aviation-Turbulence, Convective Initiation, Volcanoes LISDAD- Lightning Information System Data Acquisition and Display (NASA/MIT LL) RDT- Rapidly Developing Thunderstorm (EUMETSAT SAF/MeteoFrance)

11 Methodology  GLM Proxy Data »Inter-compare LMA channel w/ LIS optical to determine how best to use LMA data as proxy to GLM data. »Applications of LMA data (such as in lightning jump algorithm) will extend to GLM using the proxy data. »Re-sampling of LIS data will also serve as an alternate GLM proxy.  Exploratory Research (see supplementary charts for items below): »WRF model simulations »Interconnections (Ice-precipitation, updrafts, flash rate) »Flash type discrimination  Clustering/Filtering »Remove non-lightning events »Approach needs to be consistent/coordinated with GLM instrument contractor responsible for L1B algorithms »Lightning products- events, groups, flashes

Washington, DC Lightning Mapping Array (in progress- 8 stations as of Septembber 2007) Channel 10, MHz (upper VHF), 8-10 stations

13 DC Regional Storms November 16, 2006 Resampled 5-min source density at 1 km and 10 km LMA 1 km resolution LMA 10 km resolution

14 LJA: Lightning Jump Algorithm Identifies Growth and Decay P. Gatlin, April 2007, M.S. Thesis “SEVERE WEATHER PRECURSORS IN THE LIGHTNING ACTIVITY OF TENNESSEE VALLEY THUNDERSTORMS“ “Jump” Precedes tornado by min, avg 17 min POD FAR CSI

15 Lightning Jump Algorithm: Test Case Radar and lightning for storm A during a lightning jump. The 0.5º elevation scan of (a) reflectivity (dBZ) and (b) storm-relative velocity (kts) at 1634 UTC overlaid on county boundaries; (c) 3-D view of reflectivity; (d) composite VHF source density (sources km -2 min -1 ) with a 1 km x 1 km resolution in the 0-10 km layer and +/-CGs from UTC. Patrick Gatlin, April 2007

16 Lightning Jump Algorithm: Test Case Time-height series of radar and lightning data for the tornadic, classic supercell A on 5 May (a) Reflectivity (dBZ; contours), VIL (kg m -2 ; dash-dot); (b) composite VHF source density (sources km -2 min -1 ; contours), 2-min total (solid) and 5-min total (dotted) flash rates (flashes min -1 ), CG flash rate (flashes min -1 ; dash-dot) and severe weather occurrence (wind speed, hail diameter, Fujita scale); (c) shear (10 -3 s -1 ; contours), 5-min DFRDT (flashes min-2; solid), and lightning jump threshold (dash-dot); (d) mass-weighted angular momentum (10 4 m -2 s -1 ), IC:CG ratio (solid) and 5-min +/-CG flashes (hatched/solid vertical bars). Patrick Gatlin, April 2007

17 SCAN MENU Lightning Jump Algorithm: Prototype Implementation in AWIPS/SCAN SCAN Storm Cells / Site Storm Threat Superimposed with DC LMA image (July 04, 2007 at 23:01Z) Courtesy of Mamoudou Ba

18 Testing and Validation Demonstrating Algorithm Performance…  Truth data »Ground-based lightning networks, in-situ »Ancillary data »Field Campaigns »Hazardous Weather Testbed- Huntsville, AL and Norman, OK  Algorithm Test plan »Use proxy/simulated data cases »Perform verification using truth data above in conjunction with proxy/simulated data cases to perform verification  Error Estimation/Accuracy »Validate against “heritage” ground truth sources above »Metrics: Root Mean Square Error and Bias; POD, FAR, CSI for warning lead time  Latency »Evaluate run time »Our goal is to process as much data as we can, identify bottlenecks and optimize in order to assess/address latency risks

19 Testing and Validation Proxy and Simulated Data… Proxy Data  TRMM LIS/OTD- resampled to GLM resolution  VHF total lightning- remapped to GLM resolution  SEVERI, MODIS as ABI proxies concurrent with LIS and ground-based lightning data- for merged ABI-GLM products Simulated Data  WRF, RAMS, cloud resolving models Planned for concurrent proxy data set - severe storm case in Tennessee Valley (WRF, MODIS, LIS, LMA )

20 GLM Proxy Data Tool developed to start inter-comparing LIS (squares), LMA (dots), and NLDN (Xs) for Proxy Data Development. Courtesy of Monte Bateman

21 LIS Performance Assessment 6/15/2005 Overpass with LIS Superimposed Bill Boeck, LISIW, Sept. 2006

22 Parallax Correction  +X forward => data moves from right with time  LIS footprint squares, calculated LMA = X  LMA (NSSL) data between 5 km and 14 km altitude Bill Boeck, LISIW, Sept Storm centroiding- need to be mindful of parallax offset when merging GLM with ground-based data, e.g., NLDN, NEXRAD

23 Coincidence Mapping LIS with other Corresponding Lightning Data NLDN at 0 km Green X =-CG, Magenta X =+CG LIS at 12 km White + LMA at Z vs. T position colored by time LASA at 18 km Green + =-IC, Magenta + =+IC LASA Green X =-CG, Magenta X =+CG Bill Boeck, LISIW, Sept. 2006

24 Total Lightning Impacts Decision Making  Has directly contributed to several correct severe warning decisions at HUN, OHX, and BMX. “…the LMA density map gives you a great overall view of where storms with intensifying updrafts are located. So it gives you a good map of where to concentrate attention.” “I believe the flash density rates were the primary factor in holding off on a warning.”  Data archived by WFO  Used in Warning Event Simulator for office training

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27 Conclusions Significance: The GLM offers a new capability to observe all lightning day and night and with near-uniform coverage of the US and adjacent oceans to improve NOAA’s ability to issue forecasts and warnings that will save lives.

28 Summary and Next Steps  Develop GLM proxy dataset for testing of GLM algorithms (filtering, clustering, cell tracking, lightning jump) under development.  Transition heritage TRMM/LIS clustering & filtering algorithms to GEO.  Continue upgrade of LMA networks, the evaluation of LMA data utility, and the improvement of associated training modules at WFOs.  Develop cloud-resolving WRF model simulations to predict total lightning flash rates as a function of space & time.  Jump Algorithm test at selected WFOs (Sterling, VA; Huntsville, AL; Norman, OK; Others)  Continued AWIPS modifications to support algorithm prototyping  Investigate connections between precipitation processes, updraft strength, and lightning flash rate.  Investigate the potential for discriminating ground & cloud flashes based on differences in their cloud-top optical characteristics.