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Pam Heinselman NOAA National Severe Storms Laboratory Warn on Forecast Workshop 8 Feb 2012 Exploring the Impact of Rapid-scan Radar Data on NWS Warnings.

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Presentation on theme: "Pam Heinselman NOAA National Severe Storms Laboratory Warn on Forecast Workshop 8 Feb 2012 Exploring the Impact of Rapid-scan Radar Data on NWS Warnings."— Presentation transcript:

1 Pam Heinselman NOAA National Severe Storms Laboratory Warn on Forecast Workshop 8 Feb 2012 Exploring the Impact of Rapid-scan Radar Data on NWS Warnings Daphne LaDue OU CAPS Heather Lazrus NCAR

2 Motivation 62% Stakeholders’ needs: Faster Updates Source: Radar Operations Center

3 Motivation

4 “To enhance the ability to monitor rapid tornadogenesis, the NWS should develop and implement additional Volume Coverage Pattern strategies that allow for more continuous sampling near the surface (e.g., 1-min lowest elevation sampling).” Motivation

5 JDOP (Burgess et al. 1979; Whiton et al. 1998) Performance improvement resulting from adding Doppler capability Motivation Benefit Assessment JPOLE (Scharfenberg et al. 2005) Case examples illustrating how polarization aided operations Understanding of storm severity, warning decisions, wording in follow-up statements, confidence

6 Explore how improvements in depiction of storm development from rapid sampling may benefit forecasters’ decision making process. Objective

7 Andra et al. (2002) Description of 5 warning decision factors exemplified during 3 May 1999 tornado outbreak Background: NWS Decision Making 1. Scientifically based conceptual models 2. Two primary data sets: Doppler radar & ground truth 3. Workstations and software 4. Strategy 5. Expertise

8 Background: NWS Decision making Morss and Ralph (2007) Participant observation & structured interviews during PACJET and CALJET Assessed operational benefit of forecaster use of off-shore, gap-filling observations provided during CALJET Use of additional data appeared to help forecasters -- In some cases, data improved specificity of forecast -- When initial forecast was fairly accurate, use of data increased forecaster confidence

9 Tuesday Afternoon Introduction to PAR & WDSS-II training Tuesday Evening and Wednesday Gain experience interrogating PAR data and issue warnings using WDSS-lI WARNGEN Thursday 12 forecasters, April 2010 Temporal Resolution Experiment

10 Paired forecasters w/ similar radar analysis skills Worked tropical supercell event that produced EF1 tornado (unwarned ) Pair 1: 43-s updates Pair 2: 4.5-min updates Temporal Resolution Experiment

11 43-s Updates 4.5-min Updates 19 Aug 2007

12 Data We Collected Audio of the teams working through situation awareness and the case What they did Video of computer screens Products issued Two observers took notes in each room What we saw

13 What they thought they did Teams debriefed individually Joint debrief to compare across teams Each individual ranked factors in their warning decision Each individual completed a confidence continuum Data We Collected

14 Understanding decision process Cognitive ActionsEmotions Experiment Design & Software Data used Coding and Thematic Analysis

15 Example Analysis: 43-s Team Decision Process 01:37:09 01:40:01 01:38:35 01:40:44 01:41:17 01:42:10 Interrogate SRM to “make sure there is nothing as pertinent as far as rotation there” before issuing SVS on south storm’s warning; identify circ on south storm w/ strong inbound but weak outbound velocity Video=22:29 – 22:53 Examine shape of reflectivity Video=22: :04 Issue SVS Video=23:05–24:08 No changes are made to the warning Notices increase in reflectivity magnitude in north storm (0.5) Interrogate base velocity & detect mesos in both storms; vel in south storm stronger aloft ) Interrogate top height and vertical reflectivity structure of both storms Video=25:52 – 27:58 01:42:53 Interrogate base velocity of north storm: “We’ve got that persistent meso. I’ve got 34 kts inbound now on this, 25 outbound” Video=28:01 – 28:10 Interrogate reflectivity for notch signature Video=28:11 – 29:06 01:43:36 01:44:19 Interrogate base vel “usually at 35 kts you need to start considering tor.” Video=29:07 – 29:17 Compares location of vel couplet w/ location of refl. notch 01:45:02 Opens WarnGen at Video = 30:26 Video=30:12 Issue Warning at Video = 32:38 01:45:45 01:46:28 01:47:11 01:45:02 Video=30:12 At end statates, “41 inbound, 64 out…I don’t think I can ignore that.” Video=29:45 -30:19 PAR ~01:47:28 Video=24: :27 Video=24: :41 Video=29:23 – 29:44 Interrogates updated SRM and finds “41 inbound, 64 out…I don’t think I can ignore that.” Video=29:45 -30:19 Issue TOR Warning

16 43-s Team Warnings EF1 TOR N. Storm 01:13:2901:50:0301:27:0601:40:0101:17:4701:35:4301:44:19 01:22:05 01:31:24 TOR S. Storm TOR N. Storm 01:50:0301:22:0501:27:0601:31:2401:40:0101:17:4701:35:4301:44:19 SVR S. StormSVR N. Storm TOR N. Storm Negative Lead TimePositive Lead Time 01:13:2901:50:0301:40:0101:17:4701:35:4301:44:19 01:22:05 01:31:24 01:27:06 TOR N. Storm min min -3.2 min 01:56:13 01:13:29 Comparison A Comparison B Comparison C 0.5° LLSD Azimuthal Shear (s -1 ) Time (UTC) +6 min

17 4.5-min Team Warnings EF1 TOR N. Storm 01:13:2901:50:0301:27:0601:40:0101:17:4701:35:4301:44:1901:22:0501:31:24 TOR N. Storm 01:50:0301:22:0501:27:0601:31:2401:40:0101:17:47 01:35:43 01:44:19 TOR N. Storm Negative Lead TimePositive Lead Time 01:13:2901:50:0301:40:0101:17:4701:35:43 01:22:05 01:31:24 01:27:06 TOR N. Storm +4.6min -0.7 min 01:56:13 01:13:29 Comparison A Comparison B Comparison C Time (UTC) 0.5° LLSD Azimuthal Shear (s -1 ) -1.6 min 01:44:19

18 What we’ve learned 6 teams interrogated similar radar signatures Came to different conclusions about whether and when to warn Experience Conceptual models Confidence Tolerance for Missing Event Perceived Threats Software Decision (Confounding) Factors (Hahn et al. 2003; Hoffman et al. 2006; Pliske et al. 1997)

19 Forecasters’ Conceptual Model During Case 43-s Team4.5-minTeam Weaker Couplet Strength 66%83% Trend in Circulation Strength 100% Update Time Detrimental 0%100% Environment 66% Reflectivity Notch 100%

20 Understanding of Supercell in Tropical Environment B C A A A A B B C B C C More ConfidentLess Confident Understanding of NWRT PAR Data A B B C C BB C C Usual Confidence More ConfidentLess Confident Usual Confidence

21 What we’ve learned *43-s Teams 4.5-min Teams 0 min20 min Warning Lead Times 11.5 min 4.6 min 18.6 min 6 min *Issued 50% more warnings: 3 hits, 1 miss, 2 false alarms Data analysis was time intensive; exploring other methods Warning decision process is complex Some decision factors were similar across groups, others were not Update time likely had a positive impact on warning lead time


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