Travis SmithNSSL / OU / CIMMS

The Hazardous Weather Testbed /

Experimental Warning Program

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Experimental Warning Program

• one half of the Hazardous Weather Testbed, focused on short-fused severe weather hazards

• EWP “Research” (NSSL / WRD / SWAT + friends) – develop severe weather warning applications and techniques to enhance warning decision-making

• EWP “Operations” – collaborative evaluation of new techniques, applications, observing platforms, and technologies

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010

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EWP Research

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010

We work at the crossroads of nearly everything in warning decision-making research.

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Example: Multi-sensor data fields Show physical

relationships between data fields from multiple sensorsStorm tracks and trends can be generated at any spatial scale, for any data fieldsFuture state predicted through extrapolation shows skill out to about an hour

Near-surface reflectivity

Reflectivity @ -20 C

(~6.5 km AGL)

Total Lightning Density

Max Expected Size of Hail

4

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EWP OperationsForecaster / researcher collaboration

• 60-70 participants

• All NWS regions

• International visitors

• Valuable feedback!

Science and Technology showcase

Currently 6 weeks annually – could expandWarn-on-Forecast Kickoff Workshop – Feb 18, 2010

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EWP Research / Operations and Warn-on-

Forecast• What are the best approaches for radar data QC for assimilation into models?

• How well are storm-scale processes depicted by data assimilation and model forecasts?

• How will WoF information be used/visualized in NWS operations?

• How will it be conveyed to the many different types of end-users?

• How do we manage this paradigm shift? (deterministic versus probabilistic warning guidance)

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Radar QC: current “best practices” and

future capabilitiesExamples, not an exhaustive list:

• 2D velocity dealiasing (Zhongqi and Wiener)

• Staggered PRT range/velocity ambiguity reduction (Torres et al.)

• Reflectivity QC neural network (Lakshamanan)

• Dual-Pol Clutter Mitigation Decision algorithm (Hubbert et al.)

First step: human-QC’d data set of case studies for evaluation.

Bef

ore

Afte

r

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Collaborative evaluation and

feedbackDo models accurately depict:

• current storm structure?

• range of possible predicted storm evolutions?

How to best visualize the data?

Help forecasters understand the data

10 m/s updraft

Ice and liquid water

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Evaluation Example: SHAVE

Phone calls to conduct surveysStudent-run, student-ledRemote high resolution verification of:

HailWind damageFlash floods

SHAVE VERIFICATION

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Integration into NWS operations

Integrate:

• new science

• new technologies

With:

• new concepts of operations

• new products and services

HWT Collaboration(early and often!)

Operational Implementation

Warn-on-Forecast Kickoff Workshop – Feb 18, 2010

The meteorologist is the expert on interpreting the hazard and its uncertainty.

The meteorologist cannot anticipate everyone’s exposure and response time

How can weather hazard information be made more adaptable to those that do know their own exposure and response time?

Sociology of Probabilistic Warning Guidance

Little Sioux Camp

Concrete Dome Home

Getting “there” from “here”

Statis

tics-

base

d un

certa

inty

/

hum

an-a

ssist

ed &

aut

omat

ed

extra

pola

tion

“War

n on

det

ectio

n” (d

eter

min

istic)

Blend

ed s

tatis

tics

/

extra

pola

tion

w/ dat

a

assim

ilatio

n

NWP “W

arn

on fo

reca

st”

Existing stormsNewly initiated convection

Present

Forecast convection (doesn’t yet exist)

WSR-88D Dual-Pol Radar Phased Array RadarGap-filling radarFuture

GOES-R

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The EWP “ résumé ”• Radar interpretation / analysis / visualization

• Severe weather warning applications

• Multi-sensor data blending and extrapolation-based nowcasting

• Radar data QC / Human QC of data

• Data mining of large data sets

• Building enhanced verification data sets

• Good relationships with many operational NWSFOs / regions (MICs, SOOs, WCMs, forecasters)

• Live where research and operations meet.