1. FY09 GOES-R3 Project Proposal Title Page

1. 1. FY09 GOES-R3 Project Proposal Title Page • Title: GOES-R Risk Reduction for the Geostationary Lightning Mapper • Project Type: Product Development • Status: Continuation • Duration: 1 year • Leads: • Richard Blakeslee, William Koshak (NASA/MSFC) Co-managers • Walt Petersen (MSFC), Steve Goodman (NOAA/NESDIS) • Other Participants: • Bill McCaul (USRA) • Robert Boldi, Larry Carey, Dennis Buechler (UAHuntsville) • Paul Krehbiel (New Mexico Tech) • Eric Bruning, Rachel Albrecht (NESDIS CICS Post-Docs) • Chris Schultz, Yuanming Suo (UAH GRA student) • Henry Fuelberg (Scott Rudlosky, FSU GRA) • NWSFOs, NCEP/SPC, Hazardous Weather Testbed, SPoRT, NSSL

2. 2. Project Summary • Exploratory Research • Cell Tracking: Define and track thunderstorm cells through space & time . This supports both lightning jump and lightning warning algorithms. • Lightning Jump:Investigate connections between lightning flash rate changes and severe weather (particularly tornados). • Lightning Warning: Determine optimum methodology for providing warning of potential lightning threat to a region. • Lightning Forecast: Develop cloud-resolving WRF model simulations to predict total lightning flash rates as a function of space & time. • QPE: Investigate connections between precipitation processes, updraft strength, and lightning flash rate. • Photogrammetry: Image sharpening and improvements of geolocation accuracy to subpixel levels. • Flash Type Discrimination: Investigate feasibility of discriminating ground flashes from cloud flashes based on the diffuse cloud-top optical emissions. • Supporting Activity • Continue improvement of LMA applications, networks, and associated training modules at WFOs. LMA datasets serve as key sources for GLM proxy data (highly leveraged activity extensively used but not funded by R3) Note: the development of GLM proxy datasets for testing of GLM algorithms (filtering, clustering, cell tracking, lightning jump) was moved to AWG on 8/1/08, so is not a topic of this presentation. .

3. 3. Motivation/Justification • Supports NOAA Mission Goal(s): • Weather & Water (high impact weather, severe storms, air quality-NOx) • Commerce & Transportation (aviation weather hazards) • Climate (inter-annual to decadal change) • Ecosystems (forest and wildland fires) • Justification: • Exploratory research is specifically targeted to clarify and exploit the fundamental information content of space-based lightning optical measurements. Such explorations have been carefully chosen to maximize return-on-investment (ROI) of GLM instrument.

4. 4. Methodology • Exploratory Research • There are 7 different exploratory research projects involved (see slide 2) each with their own methodology. A common approach is that they will each use heritage satellite lightning data, proxy GLM datasets, and/or other lightning datasets from field campaigns to test the feasibility of the investigated algorithm. A large sampling involving many case studies is necessary to both achieve and quantify confidence. • Supporting Activity • Two sites to DC LMA to improve network robustness and performance. • Implemented common processing code (maintained by NMT) for Alabama and DC which facilitates data sharing and allows network expansion. • Two sites from Atlanta area being processed with Alabama LMA (experimental). Working with Atlanta WFO to access lightning in AWIPS. • Access to all archived data from KSC LDAR II, and expect real time data end of July (and will produce AWIPS products for Melbourne, KSC, JSC). • Initial AWIPS II products are being developed.

5. 5. Summary of Previous Results The R3 Quarterly reports (available upon request) provide detailed information and summaries of previous results. 5

6. 6. Expected Outcomes • Cell Tracking • Better performance of hazard cell tracking/predicting algorithm. FAR down, POD up. • Operational demonstrations at NWS WFOs and SPC • Draft ATBD completed end of FY10 if acceptable PG results during FY10. • Lightning Jump and QPE Algorithms • Ample case studies will clarify & substantiate optimal methodologies for relating lightning to severe Wx, ice-precipitation processes, updraft strength. • WFO training modules will be upgraded to reflect what was learned. • South African Weather Service provided sample CG lightning data from their National Network; initiate collaboration with Hydrology Team (Kuligowski) using SEVERI (ABI proxy)-GLM proxy data for Rainrate and Nowcasting Algorithms. • Draft ATBD for the Lightning Jump algorithm completed end of FY10 if acceptable PG results during FY10. • Lightning Warning and Forecast Algorithms • Warning: Get probability that ground flash occurs within T-minutes of cloud flash. • Forecast: Improved confidence that WRF convection is in right place @ right time. • Photogrammetry Algorithm • Demonstrate flash image sharpening and improved geolocation. • Flash Type Discrimination Algorithm • Ability to retrieve the ground flash fraction of a set of N flashes based on the Central Limit Theorem Method, and the MNEG (Maximum Number of Events in a Group) optical characteristic. Will have to determine how small N can be made.

7. 7. Recall FY09 Major Milestones for Reference • FY09 • Cell Tracking [Boldi] • Incorporate images from ABI to construct a convective classification of electrified storms. Classification to be used in the modeling of the evolution of the cell being tracked. • Complete draft ATBD for Cell Tracking Algorithm (near end of FY09) • Lightning Jump & QPE Algorithms [Petersen, Carey, Schultz, Albrecht, Goodman] • Identify & evaluate additional storm cases for Lightning Jump & QPE algorithms. • Lightning (storm level LMA analysis) trending in AWIPS implemented. • Complete additional AWIPS warning decision assessments at select WFOs. • Finalize Upgrades to DC LMA network. • Collaborate with Hydrology team on blended ABI-GLM rainrate and nowcasting algorithm. • Complete draft ATBD for Lightning Jump Algorithm (near end of FY09) • Lightning Warning and Forecast Algorithm [Buechler, McCaul] • Warning: Determine typical duration between 1st cloud flash and 1st CG in storm • Forecast: Have NSSL complete WRF ensemble runs to which we apply forecast algorithm to, and evaluate results. • Photogrammetry Algorithm [Carey, Suo] • Apply a test photogrammetric technique to image sharpen & geolocate LIS flashes. • Flash Type Discrimination Algorithm [Koshak] • Complete IDL code to plot split screen views of OTD/LIS ground and cloud flashes.

8. 7. FY09 Accomplishments (Cell Tracking) • Hazardous Cell Tracker Completed • Finds “Storm-Cells” using (proxy) ABI/GLM flash data. • Tracks Cells using LL/MIT multi-scale tracker (Next Slide). • Uses methods based on Schultz 2009’s “Lightning-Jump-Algorithm” to predict tornadic cells and other severe weather • Running in real-time using NEXRAD/LMA data • Outputs viewable in Google Earth Predicted Path of Storm Lightning Jump algorithm predicts a tornadic event

9. 7. FY09 Accomplishments : Multi-scale Tracker Large Scale Motions Storms move differently when viewed on different scales Small Scale Motions

10. 8. FY10 Plans (Cell Tracking) • Transition from NEXRAD-based ABI proxy data to proxy data sets with greater similarity to expected ABI images • Grid GLM proxy data to 10 km footprint, rather than current 2 km. • Evaluate GLM alone performance issues • Data fusion or image-blending of 10 km GLM data with higher resolution ABI data to create storm intensity maps at resolutions better than GLM. • Support Lightning-Warning real-time prototype • Motion Vectors • Growth and Decay fields • Data fusion, Functional Template Support Infrastructure • Demonstrate data delivery to (N)AWIPS(I/II) displays. • Develop Java-based application to read algorithm output and display data on AWIPS-II or other Java-compatible display.

11. 7. FY09 Accomplishments (Lightning Jump) Thunderstorm breakdown: North Alabama – 83 storms Washington D.C. – 2 storms Houston TX – 13 storms Dallas – 9 storms • Six separate lightning jump configurations tested • Case study expansion: • 107 T-storms analyzed • 38 severe • 69 non-severe • The “2σ” configuration yielded best results • POD beats NWS performance statistics (80-90%); • FAR even better i.e.,15% lower (Barnes et al. 2007) • Caveat: Large difference in sample sizes, more cases are needed to finalize result. • M.S. Thesis completed and study in press (JAMC); forms the conceptual basis of the lightning jump ATBD C. Schultz, W.A. Petersen, L.D. Carey

12. 8. FY10 Plans (Lightning Jump) • Establishing national validity via regime expansion: • Addition of more DC LMA cases (NE US) and cases from the STEPS field program (Mid-Western US) • Expansion to other regimes with LMAs and LDARS: Oklahoma (Mid-West), Kennedy Space Center (ST SE US), Socorro and/or White Sands, NM, Tucson, AZ (Desert SW). • Application of jump algorithms to recently developed GLM proxy flash products (LMA-LIS based) for algorithm tuning. • ATBD: Not comfortable preparing in FY09 prior to completion of GLM proxy tests. 12

13. 7. FY09 Accomplishments (Lightning Warning) • Developed a nowcast display using a combination of total lightning and NLDN cloud-to-ground lightning. • Developed a point display to visualize an approaching lightning threat • Determined the average duration between 1st flash and 1st CG in storm is 4.4 minutes. The first flash was a CG in about 18% of cases (based on 77 storms, mostly summer time)

14. 8. FY10 Plans (Lightning Warning) • Incorporate motion vectors from cell tracking to improve lightning probability nowcasts. • Investigate best ways to provide the lightning warning products to the end user. Continue interaction and discussions with NWS forecasters about its use and visualization. • Incorporate products and displays into GOES-R proving ground activities. • Use GLM proxy data. • Proposed New Start: Lightning warning product using multi-sensor GLM - ABI derived flash probabilities (GOES-R Blended Product) . 14

15. 7. FY09 Accomplishments (Lightning Forecast ) • To provide quantitative LTG threat forecasts (1-12h), built algorithms to convert 2-km WRF proxy fields to LTG flash rate density, based on global observations of links between lightning and precip ice. • LTG threat is a blend of two calibrated threats, one based on graupel flux at -15˚C (handles time variability), other on vertically integrated ice (handles areal coverage); calibration based on North Alabama LMA data for a series of diverse storm cases. • Completed and published paper on WRF-based LTG forecasting. • Commenced study of generality of algorithm by applying the published method to 4-km CAPS ensembles of WRF forecasts from 2008. • McCaul, E.W., Jr., S. Goodman, K. LaCasse, D. Cecil, 2009: Forecasting lightning threat using cloud-resolving model simulations. Wea. Forecasting, 24, 709-729. This paper was also highlighted in Bull. Amer. Meteorol. Soc. as a “Paper of Note.” • McCaul, E. W., Jr., and S. Goodman, 2008: Use of vertically integrated ice in WRF-based forecasts of lightning threat. 24th Conf. Severe Local Storms, Sanannah, Paper 3B-3.

16. 8. FY10 Plans (Lightning Forecast) • Continue application of published lightning forecast method to 4-km and other WRF simulations and ensembles, to assess robustness of the method, and to find approaches to generalizing the methods under varying physics schemes and differing grid meshes. • Collaborate with groups such as CIMSS seeking ways to generate proxy versions of lightning data to accompany their high-res WRF simulations of important cases. 16

17. 7. FY09 Accomplishments (QPE) • FY09 Data Mining: Developing a dataset: • Condensed 11-yr TRMM features database (TMI, PR, LIS) into ASCII subset • For each feature/embedded precipitation type (i.e., convective/stratiform), database includes: • Convective and stratiform pixel #’s (rain volume); • LIS Lightning flash counts and density; • Mean/Max feature rain rate, ice/liquid water path; • Mean/Max microwave scattering index • Reduce IR ambiguity: Lightning isolates different conv./strat. rainfall behavior • Go after Low-hanging fruit Rain / No Rain Rain Type • Example: GLM for SCAMPR precip detection and Conv./Strat. partitioning

18. 8. FY10 Plans (QPE) • Continued data mining of TRMM features ASCII database and expansion from features to cell scale • Quantify GLOBAL TROPICAL Conv/Strat. precipitation variability with lightning. • Define convective/stratiform precipitation pixels as a function of flash (groups) extension. • Fundamental role for passive microwave (PMW) observations in SCAMPR. But over continents PMW-diagnosed rainfall is a strong F(ice scattering), subject to larger bias and random error. • Can GLM indicate cells with preferred PMW bias/error for tuning SCAMPR? • Preliminary TRMM analysis suggests PMW high (low) bias when flash density is large (low) • Are there “unique” relationships between lightning, lightning-tendency, ice water path, and rainfall rate- possible “bridge” to “tuning” of IR estimates over land when lightning occurs.

19. 7. FY09 Accomplishments (Photogrammetry) • Yuanming Suo (UAH ECE) defended his MS thesis in July 2009 under advisement of Larry Carey. • A novel approach for flash detection in the presence of CCD noise was proposed. Compared to the LIS approach, the proposed flash detection approach provides: • a better mean square error of 20% for estimate of the difference image (original – background), • zero false trigger per frame and 100% flash detection efficiency, • less than 0.32 km2 of missing flash coverage (~ 42 km2 for LIS approach) and less than 0.036 km of flash centroid error (0.3 pixel ~ 1.2 km for LIS approach), • the same average event density as true lightning flash (32% loss for LIS approach).

20. 8. FY10 Plans (Photogrammetry) • Recommend pause in photogrammetry R3 research in FY10 to: • Conduct in-depth interaction and discussion with instrument/algorithm vendor regarding potential use of Yuanming algorithm in present or subsequent GLM configurations. • Real-time software implementation is highly impractical due to long processing times. • Would likely require direct implementation in electronic circuit hardware (e.g., programmable logic device such as FPGA) on board GOES-R GLM instrument. Feasibility is unknown. • Not yet known if techniques in Yuanming’s approach could be implemented on post-processed level 1b data and backgrounds (may not be possible). • Re-evaluate the feasibility to develop a Photogrammetry Algorithm that just acts on the GLM flash product directly (as shown in previous block diagram) to sharpen image and/or improve geolocation accuracy to sub-pixel. 20

21. 7. FY09 Accomplishments (Flash-Type Discrimination) • Improved partitioning of OTD CONUS flashes into IC and CG flashes • and produced final frequency distributions of several optical characteristics. • But, the IC and CG distributions overlap making discrimination impractical. • However, introduced Central Limit Theorem Discrimination Methodology • which uses the distributions of the means (which have little overlap!), thus • making discrimination feasible (i.e., retrieving the ground flash fraction of a set of N flashes is feasible using this approach). Applying Central Limit Theorem converts overlapping (N = 1) distributions into non-overlapping distributions for larger values of N, thus making flash-type discrimination feasible.

22. 7. FY09 Accomplishments (Flash-Type Discrimination) • Completed IDL FlashMovie s/w that allows one to examine individual flashes in space and time from a bird’s eye view to help inter-compare distinct features of CGs and ICs. • Results from FlashMovie analyses showed that MNEG (Maximum # Events in a Group) and MGA (Maximum Group Area) are very good ‘return stroke detectors’ and work best in the Central Limit Theorem method. • Applied Central Limit Theorem method using real CONUS flashes with • known ground flash fraction, and demonstrated acceptably small retrieval • error. • Two journal papers summarizing these accomplishments are 100% and 90% ready for submission, respectively, to JTECH: • Koshak, W. J., Optical Characteristics of OTD Flashes and the Implications for • Flash-Type Discrimination, to be submitted in JTECH, August 2009. • Koshak, W. J., R. S. Solakiewicz, Retrieving the Fraction of Ground Flashes from • Satellite Lightning Imager Data, to be submitted in JTECH, August 2009.

23. 8. FY10 Plans (Flash-Type Discrimination) • Respond to reviewer comments on the two JTECH papers to be submitted • in August 2009, and finalize the publications. • Whereas the Central Limit Theorem method is clearly useful for the • LIS/OTD climatology where the sample size of flashes, N, is large in a given • lat/lon bin, determine how well the method would work when N is smaller (as • in a GLM application over meteorological time-scales). • Perform additional simulated retrievals, but with decreasing N. • Assess ground flash fraction retrieval errors.

24. 8. FY10 Plans (Aviation) • Motivation: • Propose new line of exploratory R3 research for FY10 that is budget neutral by pausing Photogrammetry effort. • Aviation applications are “low hanging fruit” that clearly demonstrate value of stand alone GLM and combined (e.g., GLM-ABI) approaches in traditionally data denied areas (e.g., oceanic scenario where radar is unavailable). • Recent high-profile aviation accidents over oceans provide additional motivation. • Team: • Larry Carey (lead). • Walt Petersen, Erica Hill (2nd year UAH graduate student), others? • Coordinate with GLM proxy data team and other GLM R3 projects (e.g., cell tracking, lightning jump). • Coordinate with on-going NETGEN aviation activites (e.g., WRF modeling for aviation). 24

25. 8. FY10 Plans (Aviation) • Proposed Milestones: • Investigate and develop best proxy GLM total lightning data set for one or more large domain oceanic case studies with relevance to aviation applications (e.g., thunderstorm, lightning, turbulence, and icing avoidance). • Long range VLF networks might provide proxy GLM data at suitable resolution and sufficient detection efficiency (e.g., Vaisala Global Lightning Dataset (GLD360), WWLLN, or Zeus). • Model simulated clouds could provide indirect GLM proxy data over large ocean sector but cost and representativeness would need to be explored. • Conduct one or more oceanic case studies for aviation applications. • Explore GLM lightning jump algorithm and qualitative visual products (e.g., flash origination density) and combined lightning-IR signatures for aviation applications. 25

26. 9. Funding Profile (K) • Summary of previous leveraged funding (proxy data is now AWG effort) • NASA MSFC VP61 Investment Funding was applied to support senior post doctoral scientist (Dr. Richard Solakiewicz) in NASA Postdoc Program (NPP) for exploratory research in flash discrimination . • NASA EPSCoR funding is currently in place to support proxy dataset development (OU student Stephanie Weiss, with guidance from Dr. Don MacGorman NOAA/NSSL). Additional correspondence with ground-based validation studies being conducted with Bill Beasley of OU.

27. 10. Expected Purchase Items • FY06 • 480 K • FY07 • 364.352 K • FY08 • 510 K • FY09 • 515 K • 388 K(to NSSTC) • 127 K (to CICS Post Docs) • FY10, present estimate • 526.546 K • 396.546 K(to NSSTC) • 130 K (to CICS Post Docs)