THE GOES-R GLM LIGHTNING JUMP ALGORITHM (LJA): RESEARCH TO OPERATIONAL ALGORITHM Elise V. Schultz 1, C. J. Schultz 1,2, L. D. Carey 1, D. J. Cecil 2, G.

Slides:



Advertisements
Similar presentations
SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)
Advertisements

SPoRT Activities in Support of the GOES-R and JPSS Proving Grounds Andrew L. Molthan, Kevin K. Fuell, and Geoffrey T. Stano NASA Short-term Prediction.
Radar Climatology of Tornadoes in High Shear, Low CAPE Environments in the Mid-Atlantic and Southeast Jason Davis Matthew Parker North Carolina State University.
Cell Tracking Algorithm (R3) Dan Cecil, UAH
Total Lightning Collaborations with NASA SPoRT and the National Weather Service Southern Thunder Workshop July, 2011 Norman, OK Christopher Darden,
Convective Initiation Studies at UW-CIMSS K. Bedka (SSAI/NASA LaRC), W. Feltz (UW-CIMSS), J. Sieglaff (UW-CIMSS), L. Cronce (UW-CIMSS) Objectives Develop.
Improving Excessive Rainfall Forecasts at HPC by using the “Neighborhood - Spatial Density“ Approach to High Res Models Michael Eckert, David Novak, and.
Inter-comparison of Lightning Trends from Ground-based Networks during Severe Weather: Applications toward GLM Lawrence D. Carey 1*, Chris J. Schultz 1,
Travis Smith (OU/CIMMS) February 25–27, 2015 National Weather Center
Transitioning unique NASA data and research technologies to operations GOES-R Proving Grounds Fifth Meeting of the Science Advisory Committee November,
Proxy Data and VHF/Optical Comparisons Monte Bateman GLM Proxy Data Designer.
Lightning Jump Evaluation RITT Presentation Tom Filiaggi (NWS – MDL) 12/18/13 Reduction of FAR?
SCAN SCAN System for Convection Analysis and Nowcasting Operational Use Refresher Tom Filiaggi & Lingyan Xin
Data Integration: Assessing the Value and Significance of New Observations and Products John Williams, NCAR Haig Iskenderian, MIT LL NASA Applied Sciences.
1 Douglas Mach GLM Lead Code Developer University of Alabama in Huntsville Huntsville, Alabama, USA 3rd Annual GOES-R GLM Science Meeting 1-3 December.
The Lightning Warning Product Fifth Meeting of the Science Advisory Committee November, 2009 Dennis Buechler Geoffrey Stano Richard Blakeslee transitioning.
David Hotz and Anthony Cavallucci National Weather Service, Knoxville/Tri-Cities, Tennessee Geoffrey Stano ENSCO/SPoRT, Huntsville, Alabama Tony Reavley.
Lightning Jump Algorithm Update W. Petersen, C. Schultz, L. Carey, E. Hill.
Geoffrey Stano– NASA / SPoRT – ENSCO, Inc. Brian Carcione– NWS Huntsville Jason Burks– NWS Huntsville Southern Thunder Workshop, Norman, OK July.
Christopher J. Schultz 1, Lawrence D. Carey 2, Walter A. Petersen 3, Daniel Cecil 2, Monte Bateman 4, Steven Goodman 5, Geoffrey Stano 6, Valliappa Lakshmanan.
Ken Cummins 1, with help from: Richard J. Blakeslee 2, Lawrence D. Carey 3, Jeff C. Bailey 3, Monte Bateman 4, Steven J. Goodman 5 1 University of Arizona,
Towards an object-oriented assessment of high resolution precipitation forecasts Janice L. Bytheway CIRA Council and Fellows Meeting May 6, 2015.
GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.
Lightning Jump Algorithm Current testing and future framework.
H U N T S V I L L E, A L A B A M A Utility of the GLM in an Evolving Decision Support Environment Brian C. Carcione National Weather Service, Huntsville,
Engineering Economic Analysis Canadian Edition
Event-based Verification and Evaluation of NWS Gridded Products: The EVENT Tool Missy Petty Forecast Impact and Quality Assessment Section NOAA/ESRL/GSD.
NASA SPoRT’s Pseudo Geostationary Lightning Mapper (PGLM) GOES-R Science Week Meeting September, 2011 Huntsville, Alabama Geoffrey Stano ENSCO, Inc./NASA.
AWIPS Tracking Point Meteogram Tool Ken Sperow 1,2, Mamoudou Ba 1, and Chris Darden 3 1 NOAA/NWS, Office of Science and Technology, Meteorological Development.
Christopher J. Schultz 1, Walter A. Petersen 2, Lawrence D. Carey 3* 1 - Department of Atmospheric Science, UAHuntsville, Huntsville, AL 2 – NASA Marshall.
GLM Science Meeting September 29-30, Update on GLM Cluster/Filter Algorithm Testing Douglas Mach, UAHuntsville Monte Bateman, USRA GLM AWG/R3 Science.
Proving Ground Activities with Aviation Weather Center, Storm Prediction Center and NASA SPoRT GLM Science Meeting Huntsville, Alabama 20 September 2012.
Bryan Jackson General Forecaster WFO LWX. Introduction Utilizing Total Lightning data from the DC- Lightning Mapping Array (DC-LMA) to create a preview.
Investigating the use of Deep Convective Clouds (DCCs) to monitor on-orbit performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging.
The Benefit of Improved GOES Products in the NWS Forecast Offices Greg Mandt National Weather Service Director of the Office of Climate, Water, and Weather.
Evaluation of the Pseudo-GLM GLM Science Meeting Huntsville, Alabama September 2013 Geoffrey Stano – NASA SPoRT / ENSCO Inc. Kristin Calhoun – NOAA.
Relationships between Lightning and Radar Parameters in the Mid-Atlantic Region Scott D. Rudlosky Cooperative Institute of Climate and Satellites University.
PREDICTABILITY OF WESTERN NORTH PACIFIC TROPICAL CYCLONE EVENTS ON INTRASEASONAL TIMESCALES WITH THE ECMWF MONTHLY FORECAST MODEL Russell L. Elsberry and.
Storm tracking & typing for lightning observations Kristin Calhoun, Don MacGorman, Ben Herzog.
Determining Relationships between Lightning and Radar in Severe and Non-Severe Storms Scott D. Rudlosky Florida State University Department of Earth, Ocean,
Geoffrey Stano – ENSCO / SPoRT David Hotz and Anthony Cavalluci– WFO Morristown, TN Tony Reavley – Director of Emergency Services & Homeland Security of.
The Impact of Lightning Density Input on Tropical Cyclone Rapid Intensity Change Forecasts Mark DeMaria, John Knaff and Debra Molenar, NOAA/NESDIS, Fort.
Discriminating Between Severe and Non-Severe Storms Scott D. Rudlosky Henry E. Fuelberg Department of Meteorology Florida State University.
An Object-Based Approach for Identifying and Evaluating Convective Initiation Forecast Impact and Quality Assessment Section, NOAA/ESRL/GSD.
TWO-YEAR ASSESSMENT OF NOWCASTING PERFORMANCE IN THE CASA SYSTEM Evan Ruzanski 1, V. Chandrasekar 2, and Delbert Willie 2 1 Vaisala, Inc., Louisville,
Transitioning research data to the operational weather community Overview of GOES-R Proving Ground Activities at the Short-term Prediction Research and.
Lightning Jump Evaluation RITT Presentation Tom Filiaggi (NWS – MDL) 11/28/12 Evaluation of “2σ” as Predictor for Severe Weather.
Forecaster Training for HWT Current: Articulate (via NASA SPoRT) – Previous: intro ppt on arrival Monday Two events in Warning Event Simulator (WES) –
Spatial Verification Methods for Ensemble Forecasts of Low-Level Rotation in Supercells Patrick S. Skinner 1, Louis J. Wicker 1, Dustan M. Wheatley 1,2,
Tropical Cyclone Rapid Intensity Change Forecasting Using Lightning Data during the 2010 GOES-R Proving Ground at the National Hurricane Center Mark DeMaria.
Travis Smith Hazardous Weather Forecasts & Warnings Nowcasting Applications.
CI VERIFICATION METHODOLOGY & PRELIMINARY RESULTS
Total Lightning AWIPS II Operational Demonstration Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Matt Smith, and.
Investigating Lightning Cessation at KSC Holly A. Melvin Henry E. Fuelberg Florida State University GOES-R GLM Workshop September 2009.
UAH 28 Sept 2008R. Boldi NSSTC/UAH 1 Hazardous Cell Tracking Robert Boldi 29 September 2008 NSSTC/UAH.
 Prior R3 (Schultz et al MWR, Gatlin and Goodman 2010 JTECH, Schultz et al WF) explored the feasibility of thunderstorm cell-oriented lightning-trending.
NWS / SPoRT Coordination Call 24 March, 2011 March 2011, Coordination Call.
C. Schultz, W. Petersen, L. Carey GLM Science Meeting 12/01/10.
HWT Experimental Warning Program: History & Successes Darrel Kingfield (CIMMS) February 25–27, 2015 National Weather Center Norman, Oklahoma.
Total Lightning Applications Sixth Meeting of the Science Advisory Committee 28 February – 1 March 2012 National Space Science and Technology Center, Huntsville,
Operational Use of Lightning Mapping Array Data Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Dennis Buechler, and.
1 Application of MET for the Verification of the NWP Cloud and Precipitation Products using A-Train Satellite Observations Paul A. Kucera, Courtney Weeks,
Authors: Christopher J Schultz, Walter A. Petersen, and Lawrence D
Paper Review Jennie Bukowski ATS APR-2017
Pamela Eck, Brian Tang, and Lance Bosart University at Albany, SUNY
A Real-Time Automated Method to Determine Forecast Confidence Associated with Tornado Warnings Using Spring 2008 NWS Tornado Warnings John Cintineo Cornell.
Nic Wilson’s M.S.P.M. Research
Automated Extraction of Storm Characteristics
A Real-Time Learning Technique to Predict Cloud-To-Ground Lightning
Aiding Severe Weather Forecasting
Presentation transcript:

THE GOES-R GLM LIGHTNING JUMP ALGORITHM (LJA): RESEARCH TO OPERATIONAL ALGORITHM Elise V. Schultz 1, C. J. Schultz 1,2, L. D. Carey 1, D. J. Cecil 2, G. T. Stano 3, M. Bateman 4, and S. J. Goodman 5 1 University of Alabama in Huntsville, Huntsville, AL 2 NASA/MSFC, Huntsville, AL 3 NASA SPoRT/ENSCO, Huntsville, AL 4 Universities Space Research Association, Huntsville, AL 5 NOAA/NESDIS/GOES-R Program Office, Greenbelt, MD

INTRODUCTION Lightning Jump Algorithm (LJA) developed by Schultz et al. (2009, 2011) uses Lightning Mapping Array (LMA) data to suggest likelihood of subsequent severe weather Geostationary Lightning Mapper (GLM) on GOES-R will see flashes differently than LMA, yielding different flash counts and locations Full automation and testing needed before operational implementation and utilization Robust and objective storm cell tracking needed in order to compute flash rate history / tendency

LJA PROJECT GOALS  OBJECTIVE: To refine, adapt and demonstrate the LJA for transition to GOES-R GLM (Geostationary Lightning Mapper) readiness and to establish a path to operations  Reduce risk in GLM lightning proxy, cell tracking, LJA automation, and data fusion (e.g., radar + lightning).  Develop and refine a fully automated, objective cell tracking algorithm using lightning (GLM Proxy) and radar (VIL) for a large sample  Algorithm completely hands off with no manual corrections during processing  Warning Decision Support System - Integrated Information (WDSSII)

DATA SAMPLE SET  Events included from 2002 through 2011  Subset of Schultz’s current database  Domain  Cell clusters tracked using WDSSII within larger domain.  Clusters included in database for the period (min. 30 min of lifetime) of lifetime that occurred within 120km of the LMA center **During time spent in domain Domain Various scales were tested during tracking in WDSSII * Values for <10km report distance and 10 flashes/min threshold

RADAR Data Five closest radars merged and gridded to 0.009° (~1km) using WDSSII Data outputs: Composite reflectivity, VIL, VIL_Density, MESH, azimuthal shear (approx. every 2 – 4 minutes) Proxy GLM Data Developed by Monte Bateman and Doug Mach Each flash location is determined by an amplitude weighted centroid of the groups/events Grid to 0.08° x 0.08° at 1 min and 5 min running average every 1 minute. Tracker (WDSSII w2segmotionll) Using VILFRD, builds clusters until a minimum size threshold is met. Several sizes/scales tested First see if values exceeding 100 cover large enough area. If not, include values exceeding 80. If not, include values exceeding 60, etc. Merge Tracks Merged tracks separated by < 15 km between time steps (1 minute). Tracks included in database with lifetimes ≥30 minutes Lightning Jump Algorithm Thresholds: Domain 120 km Algorithm spin-up 2 sigma Variables: Flash rate threshold Scale/size Storm Report Association Thresholds: Reports are grouped every 6 min 10 minute buffer after lightning data is cut off Variables: Report associated within X distance of cluster Verification Maximum 1 jump allowed every 6 minutes Storm reports can only confirm one forecast. Each report (after grouping) that occurs within a forecast period is considered a hit. When subsequent jump occurs during a forecast, only the time after the first forecast has expired is the subsequent forecast allowed to be verified. Otherwise it is a false alarm. Forecast/warning time set at 45 minutes Combine Proxy and RADAR Combine VIL and 5-minute Proxy GLM flash rate density (FLCT5) Track values where VILFRD ≥ 20, using increments of 20, with anything over 100 set to 100. Automated objective tracking and LJA flow chart

TRANSITION FROM LMA TO GLM PROXY  Observations from LMA ≠ GLM  Different instrument  Different frequency  Different part of flash  Must transition product from LMA to GLM proxy data stream  Based on LMA flash rate, Monte Carlo look-up table to realistic optical events (LIS), and GLM cluster-filter algorithm.  Compared 1-minute flash rates in LMA and GLM for 131 storms  20+ sources per flash threshold  GLM Proxy flash count is ~88% of the LMA flash count  Correlation in the trends are strong (R=0.9)

CELL TRACKING  Tracker (WDSSII w2segmotionll) builds cells until a minimum size threshold is met. Several size scales are tested ranging from ~30 to ~300 km 2.  Tracking uses maximum overlap approach for associating cells from one time step to the next.  Outside WDSSII, “broken tracks” are objectively merged. If WDSSII has a new cell begin at t+1 within 15 km of where a previous cell track ended at time t, those cell histories are tied together UTC Flct5: 5-minute GLM proxy flash count, updated every min. VIL: Vertically Integrated Liquid (radar) VILFRD: VIL combined with 5-minute Flash Rate Density VILFRD = 100 * ( ((VIL/45) ≤ 1) + (sqrt(Flct5/45) ≤ 1) )

 First, 1 minute flash rates are averaged over two minutes:  Next, the time rate of change of the avg. total flash rate (DFRDT) is calculated:  A standard deviation (σ) is calculated from the most recent 5 periods of DFRDT (not including the current observation time) to determine the jump threshold at any given point in time. Twice this standard deviation value is the jump threshold.  A jump occurs when DFRDT values increase beyond the jump threshold of 2σ and meets the flash rate threshold of 10 flashes min -1.  A jump ends when the DFRDT value drops below 0. Lightning Jump Algorithm Overview – 2 sigma

Example case  Features in VILFRD (combine GLM Proxy and VIL) are tracked (e.g., top left)  Lightning Jump Algorithm executed on individual clusters (e.g., bottom left)  SPC Storm reports associated with cluster  Verification methods applied.

SCALE VS. MAXIMUM REPORT DISTANCE FROM CLUSTER – POD:FAR : : : : : : : : : : : : : : : : : : 0.74 Constants: Algorithm spin-up time = 10 minutes Range limit = 120 km from LMA Center Forecast (warning) time: 45 minutes Flash rate threshold = 10 Report cut off time after lightning data ends = 10 minutes 2 sigma jump algorithm Constraints: Cell must exist in domain for >30 minutes Reports are not counted during cell spin up time. Scale Increasing distance to associate reports with cluster has a small affect towards decreasing FAR. POD remains about the same. POD increases with increasing scale size. Maximum Report Distance for Verification

SCALE VS. MAX REPORT DISTANCE FROM CLUSTER – LEAD TIME ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±12.6 Constants: Algorithm spin-up time = 10 minutes Range limit = 120 km from LMA Center Forecast (warning) time: 45 minutes Flash rate threshold = 10 Report cut off time after lightning data ends = 10 minutes 2 sigma jump algorithm Constraints: Cell must exist in domain for >30 minutes Reports are not counted during cell spin up time. Scale Lead time between 21 and 23 minutes with a standard deviation of 12.5 minutes. Maximum Report Distance for Verification

SCALE AND FLASH RATE THRESHOLD – POD:FAR : : : : : : : : : : : : : : : : : : : : : : : : 0.71 Constants: Algorithm spin-up time = 12 minutes Range limit = 120 km from LMA Center Forecast (warning) time: 45 minutes Report cut off time after lightning data ends = 10 minutes Report distance from feature = 10 km 2 sigma jump algorithm Constraints: Cell must exist in domain for >30 minutes Reports are not counted during cell spin up time. Scale POD increases at larger scale sizes. POD decreases with increasing flash rate threshold. FAR decreases with increasing flash rate threshold. Minimum Flash Rate Threshold for Jump Occurrence

SCALE AND FLASH RATE THRESHOLD – LEAD TIME ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±12.6 Constants: Algorithm spin-up time = 12 minutes Range limit = 120 km from LMA Center Forecast (warning) time: 45 minutes Report cut off time after lightning data ends = 10 minutes Report distance from feature = 10 km 2 sigma jump algorithm Constraints: Cell must exist in domain for >30 minutes Reports are not counted during cell spin up time. Scale Lead time between 21 and 23 minutes with a standard deviation of 12 minutes. Minimum Flash Rate Threshold for Jump Occurrence

SUMMARY AND CONCLUSIONS  This study assessed the LJA using the GLM Proxy data set and automated, objective cell tracking techniques.  Explored the affect of tracked scale size, storm report location associated to tracked cluster and lightning flash rate threshold for the LJA.  THIS STUDY: For larger tracked scales, POD (0.6 – 0.7) and lead time (21-23 ±12 minutes) similar to recent Schultz et al. but FAR is noticeably higher (0.7 – 0.8)  Update to Schultz et al. (2011) database:  1290 cells (405 severe, 885 non severe), 1368 reports  65% POD, 46% FAR  lead time 27 ±12 minutes  Hypotheses for differences between the two studies:  Automated and objective WDSSII tracking (this study) vs. subjective corrections to TITAN tracks (Schultz et al. 2011)  Some differences in lightning flash rates: native LMA vs. GLM proxy  Storm sample: Ratio of severe to non severe storms (at smaller scales for tracking)

ONGOING AND FUTURE WORK  Total lightning and dual-polarization radar data fusion for severe weather situational awareness  Tornado: environment + total flash rate + LJA + Doppler mesocyclone/rotation + dual-polarization  Hail: environment + LJA + MESH/dual-polarization  NWS forecaster feedback on LJA performance, visualization and best practices in nowcasting  NASA SPoRT  Continued refinement and improvement of the LJA algorithm, including tracking optimization

EXTRA SLIDES

CELL TRACKING Track values where VILFRD ≥ 20, using increments of 20, with anything over 100 set to 100. Tracker (WDSSII w2segmotionll) builds cells until a minimum size threshold is met. Several size scales tested ranging from ~30 to ~300 km 2. First see if values exceeding 100 cover large enough area (e.g., cells 26, 42, 72, 83) If not, include values exceeding 80 (e.g., cells 66, 89), and so on for decreasing values of 60, 40, and 20. Tracking uses maximum overlap approach for associating cells from one time step to the next. Cells are projected forward from time t to t+1 (1-minute increments, so projected motion has very little effect) If an observed cell at t+1 matches a cell location projected forward from t, within (5 km) or (1 x Size of Cell), then it is associated with that previously identified cell’s history. If a cell disappears in one time step, it cannot re- appear later. Outside WDSSII, “broken tracks” are objectively merged. If WDSSII has a new cell begin at t+1 within 15 km of where a previous cell track ended at time t, those cell histories are tied together UTC Flct5: 5-minute GLM proxy flash count, updated every min. VIL: Vertically Integrated Liquid (radar) VILFRD: VIL combined with 5-minute Flash Rate Density VILFRD = 100 * ( ((VIL/45) ≤ 1) + (sqrt(Flct5/45) ≤ 1) )

REPORT ASSOCIATION AND VERIFICATION RULES Based on Schultz et al and 2011  Jumps within 6 minutes of each other are only 1 jump  All reports are grouped every 6 minutes starting from initial report time associated with cell  Reports are associated within a WDSSII tracked cluster or within X distance of the cluster  Reports can only confirm one forecast/warning.  Each report (after grouping) that occurs within a forecast period is considered a hit.  When subsequent jump occurs during a forecast, only the time after the first forecast is expired is the subsequent forecast (jump/warning) allowed to be verified. Otherwise it is a false alarm.  Forecast/warning time is currently set at 45 minutes