GLM Lightning Val Plans Monte Bateman, Doug Mach, Rich Blakeslee, Bill Koshak & Steve Goodman.

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

Transitioning unique NASA data and research technologies to operations SPoRT Total Lightning Activities and Updates Geoffrey Stano NASA SPoRT / ENSCO,
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.
Cell Tracking Algorithm (R3) Dan Cecil, UAH
Lightning Imager and its Level 2 products Jochen Grandell Remote Sensing and Products Division.
Total Lightning Collaborations with NASA SPoRT and the National Weather Service Southern Thunder Workshop July, 2011 Norman, OK Christopher Darden,
Transitioning unique NASA data and research technologies to operations GOES-R Proving Ground Activities at the NASA Short-term Prediction Research and.
ABI Fire Detection and Characterization Algorithm 2 nd Validation Workshop Chris Schmidt Jay Hoffman Wilfrid Schroeder Yunyue Yu.
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.
1 GOES-R AWG Product Validation Tool Development Lightning Detection Application Team Steve Goodman (NOAA/NESDIS) Richard Blakeslee (NASA-MSFC), William.
NASA/SPoRT GOES-R PG update 1.Transition and Use of RGB Imagery 2.UAH CI Evaluation at WFOs 3.PG Related Presentations at NWA/GUC 4.QPE 5.Pseudo-GLM /
Geostationary Lightning Mapper (GLM) 1 Near uniform spatial resolution of approximately 10 km. Coverage up to 52 deg latitude % flash detection day.
Bob Iacovazzi Jr. September 20, 2011 GOES-R GLM Calibration GLM Science Meeting 2011.
Inter-comparison of Lightning Trends from Ground-based Networks during Severe Weather: Applications toward GLM Lawrence D. Carey 1*, Chris J. Schultz 1,
GOES-R Proving Ground NOAA’s Hazardous Weather Testbed Chris Siewert GOES-R Proving Ground Liaison OU-CIMMS / Storm Prediction Center.
May 15, 2013 Space Weather Product Team (SWPT) Making Sense of the Nonsensical.
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.
1 Steven Goodman, 2 Richard Blakeslee, 2 William Koshak, and 3 Douglas Mach with contributions from the GOES-R GLM AWG and Science Team 1 GOES-R Program.
V. Chandrasekar (CSU), Mike Daniels (NCAR), Sara Graves (UAH), Branko Kerkez (Michigan), Frank Vernon (USCD) Integrating Real-time Data into the EarthCube.
An automated image prescreening tool for a printer qualification process by † Du-Yong Ng and ‡ Jan P. Allebach † Lexmark International Inc. ‡ School of.
SCAN SCAN System for Convection Analysis and Nowcasting Operational Use Refresher Tom Filiaggi & Lingyan Xin
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.
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.
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,
GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.
GOES-R Proving Ground Storm Prediction Center and Hazardous Weather Testbed Chris Siewert 1,2, Kristin Calhoun 1,3 and Geoffrey Stano OU-CIMMS, 2.
1 CIMSS Participation in the Development of a GOES-R Proving Ground Timothy J. Schmit NOAA/NESDIS/Satellite Applications and Research Advanced Satellite.
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,
Richard Blakeslee, NASA/MSFC with a host of partners: NASA, NOAA, NMT, UAH, UF, UMD, GATech July 2011 Southern Thunder (ST11) Workshop Norman, OK.
1 GOES-R AWG Product Validation Tool Development Lightning Application Team Steve Goodman (NOAA, GOES-R System Program) Richard Blakeslee (NASA-MSFC) Monte.
Chapter 21 R(x) Algorithm a) Anomaly Detection b) Matched Filter.
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.
USE AND EVALUATION OF TOTAL LIGHTNING DATA IN THE GOES-R PROVING GROUND AND EXPERIMENTAL WARNING PROGRAM Kristin Kuhlman (CIMMS/NSSL) Geoffrey Stano (NASA/SPORT)
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.
SIMULATION AND ANALYSIS OF GOES-R GEOSTATIONARY LIGHTNING MAPPER (GLM) DETECTION ALGORITHM PERFORMANCE Loren Sadewa Clark, Tom Dixon, Pete Armstrong, Ruth.
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.
 Rapidly developing convection is a known source of CIT  Satellite derived cloud top infrared (IR) cooling rate, overshooting tops (OT)/enhanced-V and.
Investigating the use of Deep Convective Clouds (DCCs) to monitor on-orbit performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging.
Douglas Mach Science and Technology Institute, Universities Space Research Association, Huntsville, AL, USA GOES-R GLM Post Launch Product Tests (PLPTs)
Evaluation of the Pseudo-GLM GLM Science Meeting Huntsville, Alabama September 2013 Geoffrey Stano – NASA SPoRT / ENSCO Inc. Kristin Calhoun – NOAA.
2015 GLM Annual Science Team Meeting: Cal/Val Tools Developers Forum 9-11 September, 2015 DATA MANAGEMENT For GLM Cal/Val Activities Helen Conover Information.
The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Rapid Prototyping of NASA Next Generation Sensors for the SERVIR System.
GLM Val Tool Overview Monte Bateman. Introduction GLM is an optical instrument Closest analog is LIS Have several ground-based, 24x7 networks; all are.
Geoffrey Stano – ENSCO / SPoRT David Hotz and Anthony Cavalluci– WFO Morristown, TN Tony Reavley – Director of Emergency Services & Homeland Security of.
An Object-Based Approach for Identifying and Evaluating Convective Initiation Forecast Impact and Quality Assessment Section, NOAA/ESRL/GSD.
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.
GOES-R GLM Lightning-Aviation Applications GOES-R GLM instrument will provide unique total lightning data products on the location and intensity of thunderstorms.
GLM Annual Science Team Meeting Cal/Val Tools Developers Forum 9-11 September 2015 GLM PLT & Cal/Val Status William Koshak, NASA/MSFC.
Total Lightning AWIPS II Operational Demonstration Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Matt Smith, and.
Assimilation of Pseudo-GLM Observations Into a Storm Scale Numerical Model Using the Ensemble Kalman Filter Blake Allen University of Oklahoma Edward Mansell.
1 GOES-R AWG Product Validation Tool Development Hydrology Application Team Bob Kuligowski (STAR)
 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.
Rusty Billingsly and Brian Motta National Weather Service NOAA Satellite Science Week May 2012 Rusty Billingsly and Brian Motta National Weather Service.
Total Lightning Applications Sixth Meeting of the Science Advisory Committee 28 February – 1 March 2012 National Space Science and Technology Center, Huntsville,
1 Creating Situational Awareness with Data Trending and Monitoring Zhenping Li, J.P. Douglas, and Ken. Mitchell Arctic Slope Technical Services.
Holle | NWA Annual Meeting | October 06 Cloud Lightning from the National Lightning Detection Network (NLDN) Ronald L. Holle, Nicholas Demetriades, and.
Operational Use of Lightning Mapping Array Data Fifth Meeting of the Science Advisory Committee November, 2009 Geoffrey Stano, Dennis Buechler, and.
Kristin M. Kuhlman (CIMMS/NSSL) Christopher Siewert (CIMMS/SPC) Geoffrey Stano (NASA/SPORT) Eric Bruning (TTU) GLM in the GOES-R Proving Ground and HWT.
NWA Annual Meeting Joint Satellite Workshop
Paper Review Jennie Bukowski ATS APR-2017
User Preparation for new Satellite generations
Presentation transcript:

GLM Lightning Val Plans Monte Bateman, Doug Mach, Rich Blakeslee, Bill Koshak & Steve Goodman

 Proxy products: GLM events, groups, and flashes  Pseudo-GLM (pGLM)  Validate Lightning Data (Valid) tool  User engagement activities – NWS comments  Proving Ground – 2013 Spring program used pGLM  National Lightning Jump Demonstration uses pGLM data I. Product Generation & Assessment

 “I can see value in this product during the summer in helping with decision support during big outdoor events where lightning is a concern.”  “Strengths to me were trends in the flash density products.”  “The strength of the lightning data was being able to visualize the lightning jumps related to updraft intensification. The most notable weakness was the data dropouts, which made it difficult to find consistency with respect to time.”  “The strengths were a good correlation with "lightning jumps", ie rapid increases in lightning density and increases in storm severity in terms of large hail.”  “There were very few weaknesses in the products today as we had storms in good coverage areas. The lightning data was very beneficial in linear modes to decipher which storms were the most severe within the line.” User Comments compiled by Geoffrey Stano 2013 Spring Program

 “The lightning data did well picking up on jumps in intensity. This helped signify areas were storms were likely to intensify or already doing so.”  “It definitely helped in detecting where storms were strengthening or weakening quickly.”  “I found the flash extent density to be extremely useful, especially with the sub-severe convection it offered a glimpse in the storms’ intensity between volume scans and offered a way to monitor their growing intensity.”  “The flash extent density was the best one because it’s the simplest to use and process in a rapidly developing warning situation.” User Comments compiled by Geoffrey Stano, Spring Program

 VALID – overall framework; ingests lightning data from all available sources (other satellites & ground-based networks)  Overview: Data Match tool (shown later) Evaluates performance of GLM relative to one or more sources (automatic or user selected)  Data Match “product” is a “stoplight chart” map  UI will allow user to click on a problem area, invoking the deep-dive tool, displaying histograms and time-series behind the warning Val Tools

 CHUVA 2012 Multi-sensor field campaign, centered in Sao Paulo See plot next  IPHEX 2014 Multi-aircraft field campaign, based in SW Georgia  Working on an airborne GLM simulator Field Programs

 CHUVA 2012 Field Programs Sample lightning data from CHUVA LIS (grey squares) simulate GLM

 No enhancements to date  Prepared to implement L2 filters if needed e.g., an artifact passing through the L1b filters  Any modifications or enhancements will be transitioned to operations as needed II. Algorithm Enhancements Beyond Baseline

 Detection Efficiency Validation Tests comparison with other sources  Threshold Change Tests − Decrease & Increase  Laser Beacon Test (if available) INR, geolocation, energy cals (events & background) III. Post-Launch Test (PLT) & Post-Launch Validation From “Sample PLT Requests for GLM” 10 Jun 2013

 Background Signal Validation Test LIS, Vicarious targets: DCC, Deserts, glint, etc.  Lightning Signal Validation Tests − LIS, TARANIS  Continuing Current Detection Tests Post-Launch Test (PLT) & Post-Launch Validation, 2 From “Sample PLT Requests for GLM” 10 Jun 2013

 Underflights: Use data from airborne radiometers/spectrometers and other instruments  Comparison with LIS & TARANIS  Laser beacon cals Spatial & Spectral Calibration

 Check for errors, artifacts: ghosting, cross-talk, glint L1b filter assessment  Statistical analysis of events Assess the uniformity of the CCD & degradation over time QA Tools

 Monitor housekeeping data; alarm for low/high  Background radiance trending & analysis  Glint prediction and monitoring  Log: threshold values, instrument commands & responses, spacecraft attitude changes Instrument Health

Example of Data Match Tool Data: GLM proxy: blue boxes ENI flashes: red dots NLDN flashes: purple Xs Match product: Green: all saw Yellow: GLM + 1 other saw red: ground saw; GLM missed This GLM proxy is generated from LMA data, so it is range-limited. Past that range, it’s all red. This is a good test for this tool.