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Science and Technology Infusion Plan for Severe Weather. Daniel Meléndez. NWS S&T Committee September 17, 2002. Outline. Team Composition Vision / Benefits Goals / Targets Key Information Gaps Key Solutions Outstanding R & D Needs Summary. Severe Weather Team Composition.

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Presentation Transcript
slide1
Science and Technology Infusion Plan

for

Severe Weather

Daniel Meléndez

NWS S&T Committee

September 17, 2002

outline
Outline
  • Team Composition
  • Vision / Benefits
  • Goals / Targets
  • Key Information Gaps
  • Key Solutions
  • Outstanding R & D Needs
  • Summary
severe weather team composition
Severe WeatherTeam Composition
  • Daniel Meléndez (NWS/OST)
  • Richard Okulski (NWS/OS)
  • John Weaver (NESDIS)
  • Don Burgess (OAR/NSSL)
  • Robert Saffle (OST)
  • Steve Weiss (SPC)
  • Ron Przybylinski (WFO/STL)
  • Dan Smith (SRH)
  • Liz Quoetone (WDTB)
  • John Ferree (WDTB)
  • David Sharp (WFO/MLB)
  • Terry Schuur (OAR/NSSL)
  • Brian Motta (NWS/OCWWS)
  • Bard Zajac (U. No. Co.)
severe weather vision benefits
Severe WeatherVision / Benefits
  • 2025 Vision
  • Tornado Warning Lead Times Beyond Tornadic Lifetimes ( 30 min) at 1-km resolution
  • Save Lives
  • Increased Lead Times Enables Necessary Actions to Minimize Impact of Severe Local Storms
  • Millions in Savings to Transportation & Similar Industries
severe weather key information gaps
Severe WeatherKey Information Gaps
  • Higher Resolution and DensityStorm-Scale Data
  • Improved Specification and Forecasting of Pre-Storm Environment
  • Improved Specification and Forecasting of Boundaries
  • Improved Understanding and Specification of Severe Weather Signatures
  • Improved Verification
severe weather key s t solutions current programmatic phase

10

02

05

06

07

08

04

09

11

12

03

Severe Weather Key S&T SolutionsCurrent Programmatic Phase

MDCRS Water Vapor/EDR

SCAN+

*Dual Pol

*ORPG/Finer and FasterVCPs/ORDA/TDWR+

Deployment

Observations

OTE

Satellite Remote Sensing

WRF

Ensembles

DTE

DA/Models

R&D

Enabling Process

*Severe Weather R&D

*Training PDT

WES

Training

severe weather outstanding r d needs
Severe WeatherOutstanding R&D Needs
  • Improved Understanding of Tornado Formation
  • Improved Understanding of Severe Weather Meteorology
  • Objective Verification
  • Improved Cloud-Scale Models
  • Improved Situational Awareness Tools and Training
  • Improved Understanding of Total Lightning Data in Severe Weather Forecasting
  • Improved Understanding of Radar Polarimetry in Severe Weather Forecasting
  • Improved Understanding of Predictability Limits
  • Improved Understanding of Socioeconomic Impact
severe weather summary
Severe WeatherSummary

Vision

  • Tornado Warning Lead Times Beyond Tornadic Lifetimes
  • ( 30 min) at 1-km resolution
  • R&D Needs
  • Tornadogenesis
  • R&D on severe weather
  • Objective verification
  • Cloud-scale models
  • Situational awareness tools and training
  • R&D on total lightning data and radar polarimetry data
  • Predictability Limits
  • Improved Understanding on Socioeconomic Impact
  • Implement WRF
  • Deploy Advanced Ensemble Techniques
  • Dual Polarization
  • New Satellite Remote Sensing
  • Enhanced Training

Increasing Performance

  • WSR88D Radar Upgrades
  • TDWR integration
  • WES/Training
  • MDCRS

R&D

2002

2007

2012

2020

severe weather summary13
Severe WeatherSummary
  • Severe weather warning and detection FY07 improvements will be driven by observational (radar) increases in resolution and coverage
  • Need continued training and severe weather research as part of threshold progress
  • Improved verification is critical to overall progress
  • FAR is a consequence of verification accuracy so emphasis should be on detection
  • Synoptic forecasting models on track
severe weather
Severe Weather
  • BACKGROUND SLIDES
severe weather why far may be at high risk
Severe WeatherWhy FAR May Be at High Risk?
  • WSR-88D Lesson: New technologies temporarily raise POD at the expense of FAR
  • Long-term FAR reduction trails POD increase
slide18

U95

Trend

Actual

L95

Descriptive Statistics:

Constant = -131.1915

Coefficient = 0.0708

Rsqr = 0.053

T-value for slope = 0.53

2-tailed t-test 95% CI w/ 5 degrees of freedom = 2.57

slide19

U95

Trend

Actual

L95

Descriptive Statistics:

Constant = 10.0956

Coefficient = -0.0047

Rsqr = 0.127

T-value for slope = -0.85

2-tailed t-test 95% CI w/ 5 degrees of freedom = 2.57

slide20

U95

Trend

Actual

L95

Descriptive Statistics:

Constant = -31.8362

Coefficient = .0163

Rsqr = 0.623

T-value for slope = 2.87

2-tailed t-test 95% CI w/ 5 degrees of freedom = 2.57

severe weather s t roadmap
Severe WeatherS & T Roadmap
  • (Insert Spreadsheet)