casa: a new paradigm for end user driven data collection brenda philips director, industry, gov’t,...

CASA: A NEW PARADIGM FOR END USER DRIVEN DATA COLLECTION

Brenda PhilipsDirector, Industry, Gov’t, and End User

PartnershipsERC for Collaborative Adaptive

Sensing of the Atmosphere

AMS Corporate Forum 2007March 22, 2007

http://www.nsf.gov/start.htm

http://www.nsf.gov/start.htm

Outline

Motivation and Background User Driven Adaptive Scanning End User Policy in Test bed Next Steps

Engineering Research Centers

Research and develop technologies to generate a well-defined class of engineered systems with social and economic impacts.

Faculty, students and industry/practitioners work in a multi-disciplinary environment reflecting real-world technology.

CASA’s Focus: New weather observation system paradigm based on low-power, low-cost networks of radars.

Year 4 of a 10-year research project

10/27 F1 tornado illustrates limitations of current observation technology

Source: NWS Tallahassee Forecast Office http://www.srh.noaa.gov/tlh/Oct27event/

Watch Issued. No Warning.

“…did not have the correct rotational characteristic we expect from a tornadic storm. It’s tough to see small tornados…It’s a problem at large distances.”

Mismatch: technology vs. temporal and spatial scales for decision making

10,000 ft

tornado

wind

earth surface

snow

3.05

km

0 40 80 120 160 200 240RANGE (km)

Horz. Scale: 1” = 50 kmVert. Scale: 1” -=- 2 km

5.4

km

1 km

2 km

4 km

gap

NEXRAD 250 km spacing Horizon problem

causes coverage gap

~ 2 km resolution

5 min. updates Function

Autonomously “Sit and spin”

surveillance with “data push”

Mismatch: technology vs. temporal and spatial scales for decision makingNEXRAD 250 km spacing Horizon

Problem: Middle to upper troposphere coverage

~ 2 km resolution

5 min. updates Function

Autonomously “Sit and spin”

surveillance with “data push”

10,000 ft

tornado

wind

earth surface

snow

3.05

km

0 40 80 120 160 200 240RANGE (km)

Horz. Scale: 1” = 50 kmVert. Scale: 1” -=- 2 km

5.4

km

1 km 2

km

4 km

gap

CASA addresses “mismatch” with DCAS (distributed collaborative adaptive sensing)

10,000 ft

tornado

wind

earth surface

snow

3.05

km

3.05

km

0 40 80 120 160 200 240RANGE (km)

Short range (~ 30 km) radars

Lower troposphere coverage

100’s meter resolution

Avg. 30 second updates

Adaptive Scanning based on user needs, “data pull”

“Sense the Atmosphere where and when user needs are greatest”

End User Integration links technology with warning and response

streamingstorage

storage

queryinterface

data

Resource planning,optimization

data policy

resource allocation

SNR

Meteorological DetectionAlgorithms

1 2 3 4 5 6 7 8 9A G3G3G3G3G3G3G3G3G3B G3G3G3G3G3G3G3G3G3C G3G3G3G3G3G3G3G3G3DG3G3G3G3G3G3G3G3G3E G3G3G3G3G3G3G3G3G3F G3G3G3G3G3G3G3G3G3GG3G3G3G3G3G3G3G3G3HR1R1R2R2R1G3C2G3G3I R1F1F2, R1F2,H2R1G3C2G3G3J R1H1,F1H1,F1T2,R1R1G3C2G3G3KR1H1T2,H1T2,R1R1G3G3G3G3

Feature Repository

MC&C: Meteorological command and control

DCAS User Driven Adaptive Scanning

User Interaction with new technology for decision

makingImpact of DCAS Technology on Communications, Public Response, and Vulnerability

Social and Economic Value of CASA Data

We interact with practitioners in the public and private sectors

streamingstorage

storage

queryinterface

data


datapolicy

resource allocation

SNR


1 2 3 4 5 6 7 8 9A G3G3G3G3G3G3G3G3 G3B G3G3G3G3G3G3G3G3 G3C G3G3G3G3G3G3G3G3 G3D G3G3G3G3G3G3G3G3 G3E G3G3G3G3G3G3G3G3G3F G3G3G3G3G3G3G3G3G3G G3G3G3G3G3G3G3G3G3H R1R1R2R2R1G3C2G3G3I R1F1F2, R1F2,H2R1G3C2G3G3J R1H1,F1H1,F1T2,R1R1G3C2G3G3K R1H1T2,H1T2,R1R1G3G3G3G3

Feature Repository


Data

IntermediariesValue Added Data Users

Private Sector: Value-Added Products

Products and Services, Decision Support Systems

Federal Government

Institutions/Industry

Academic Institutions/Researchers

Research

Emergency Response

Public

State Government Public

We have a multidisciplinary team

Brenda Philips, MBA, UMass Ben Aguirre, Sociology, UDelEllen Bass, Human Factors, UVaWalter Diaz, Political Science, UPRM Kevin Kloesel, Meteorology, OUDave Pepyne, ECE, UMassHavidan Rodriguez, Sociology, UDel

Roman Krzysztofowicz, Decision Sciences, UVa,

Oklahoma Test Bed: Severe Storms

4-node mechanically scanned radar network

36 km range 25 – 100 m resolution Adaptive: Multi-

elevation sector scans, pinpointing

Pilot User Group: WFO Norman, EMs, Researchers, (Media)

7000 sq. km (90 km long)

2 tornados

4 tornado warnings

50 severe storm warnings

resource allocation


1 2 3 4 5 6 7 8 9A G3 G3 G3 G3 G3 G3 G3 G3 G3B G3 G3 G3 G3 G3 G3 G3 G3 G3C G3 G3 G3 G3 G3 G3 G3 G3 G3D G3 G3 G3 G3 G3 G3 G3 G3 G3E G3 G3 G3 G3 G3 G3 G3 G3 G3F G3 G3 G3 G3 G3 G3 G3 G3 G3G G3 G3 G3 G3 G3 G3 G3 G3 G3H R1 R1 R2 R2 R1 G3 C2 G3 G3I R1 F1 F2, R1 F2,H2 R1 G3 C2 G3 G3J R1 H1,F1 H1,F1 T2,R1 R1 G3 C2 G3 G3K R1 H1 T2,H1 T2,R1 R1 G3 G3 G3 G3

Feature Repository

query

optimization

meteorologicaltask

generation

userrules,

Weights

End users: NWS,emergencyresponse

streamingstorage

detection algorithms

resource allocation



Feature Repository

query

optimization

meteorologicaltask

generation

userrules,

Weights


streamingstorage

detection algorithms

resource allocation



Feature Repository1 2 3 4 5 6 7 8 9

A G3 G3 G3 G3 G3 G3 G3 G3 G3B G3 G3 G3 G3 G3 G3 G3 G3 G3C G3 G3 G3 G3 G3 G3 G3 G3 G3D G3 G3 G3 G3 G3 G3 G3 G3 G3

1 2 3 4 5 6 7 8 9A G3 G3 G3 G3 G3 G3 G3 G3 G3B G3 G3 G3 G3 G3 G3 G3 G3 G3C G3 G3 G3 G3 G3 G3 G3 G3 G3D G3 G3 G3 G3 G3 G3 G3 G3 G3E G3 G3 G3 G3 G3 G3 G3 G3 G3F G3 G3 G3 G3 G3 G3 G3 G3 G3G G3 G3 G3 G3 G3 G3 G3 G3 G3H R1 R1 R2 R2 R1 G3 C2 G3 G3I R1 F1 F2, R1

E G3 G3 G3 G3 G3 G3 G3 G3 G3F G3 G3 G3 G3 G3 G3 G3 G3 G3G G3 G3 G3 G3 G3 G3 G3 G3 G3H R1 R1 R2 R2 R1 G3 C2 G3 G3I R1 F1 F2, R1 F2,H2 R1 G3 C2 G3 G3J R1 H1,F1 H1,F1 T2,R1 R1 G3 C2 G3 G3K R1 H1 T2,H1 T2,R1 R1 G3 G3 G3 G3

Feature Repository

query

optimizationoptimization

meteorologicaltask

generation

meteorologicaltask

generation

userrules,

Weights

userrules,

Weights


streamingstorage

streamingstorage

detection algorithmsdetection algorithmsdetection algorithms

1. Radar network scans atmosphere (sector, pinpointing, 360 degree) and sends data to MC&C

2. Detection algorithms identify weather features in radar data.

3. Weather features are “posted” in Feature Repository, a 3-d grid of radar coverage area.

4. Tasks are generated based on clustering similar weather features.

5. Optimal radar scan configuration developed based on:

• How Important is a task to users?

• What is the quality of the scan?

ResearchersMedia

user policy

Data Pull: system optimally collects data based on user needs, evolving weather and radar capabilities.

User Policy: How Important is the data to users? User Needs User Needs translated into rules for system

What detected weather feature? How many radars? What horizontal and vertical coverage? How often should it be scanned?

“ Needs” evolving from subjective to more objective measures

Stated preferences Space/Time variablity of weather Socio-economic impacts

USER RULE FOR EMERGENCY MANAGERS: If rotation is detected, then scan the lowest two elevations radars every 30 seconds.

Goal: Geographically specific information for public notification, responder deployment

User Policy: How Important is the data to users? User Weights Determines the relative importance of

different user groups in case of resource conflict.

Mechanism established, Policy for setting weights not established

Still understanding the level of resource contention in the system

Weighting could be set for socio-economic benefit, profits, for specific weather events, etc.

ttasksCionsconfigurat

CtQktUC,,

,,maxarg*

End User Policy – How important is task t to the users?

ggroups

gg ktUwktU,

,),(

User Weights User Rules


ggroups

gg ktUwktU,

,),(


Another view of optimization

Challenges for developing user rules (preferences) Eliciting preferences

from users for a new sensing paradigm Iterative process Subject Matter Experts Use qualitative approaches

initially

Getting system designers (computer scientists, engineers) to understand user decision process and translate that into code.

Initial approach to user rules focuses on weather features

Feedback from NWS: “That’s not how we make decisions…”

Goal: Issue warnings, communicate expertise save lives, property

Radar data increases or reduces forecaster confidence

“mental movie” focus on areas of uncertainty; not always

determined by radar data

Training

Expectations

Staffing Issues

Coordination

Conditions

observed

outside

Location,

expected

impact

Ongoing

Satellite

Model

1,2,3,etc

guidance

Radar

1,2,3

Data

Conceptual

models

Equipment status

Gut feeling

Warn

Training

Expectations

Staffing Issues

Don’t Warn

Communications

Conditions

observed

outside

Location,

expected

impact

Ongoing

Mesoanalysis

Satellite

Model

1,2,3,etc

guidance

Radar

1,2,3,

Data

Equipment status

Ground truth

“NWS Warning Process”, Liz Quoetone, NWS Warning Decision Training Branch, presentation at 2005 CASA workshop

Changes to rules

Incorporation of interval-based scanning

Expansion of the definition of a storm

cell

Introduction of contiguous scans Dynamic Data Requests

Current rules focus on “time since last scanned”

Rules Ruletrigger

SectorSelection

Elevations #Radars

Contiguous Samplinginterval

NWS

N1 time 360 Lowest two 1 Yes 1 / min

N2 storm task size full volume 1 Yes 1 / 2.5 min

Researcher

R1 rotation task size full volume 2+ Yes 1 / 30 sec

R2 reflectivity task size Full volume 2 Yes 1 / min

R2 velocity task size lowest two 2+ Yes 1/ min

R3 time 360 Full volume 1 No 1/ 5 min

EMs

E1 time 360 lowest 1 Yes 1 / min

E2 reflectivity over AOI

task size lowest 1 Yes 1 / 2.5 min

E3 velocity over AOI

task size lowest 2 Yes 1/ 2.5 min

OS

O1 time 360 lowest two 1 No 1 / 5 min

Table 1. User Rules, End User Policy, Version 1

User WeightsFigure 3.2aFigure 3.2a

User Weights

August 15 frontal boundary with isolated storm cells

3 radars operational Single radar attenuation correction Clutter, velocity, and networked attenuation alg.

not yet installed DCAS running based on reflectivity rules and

detections.

22:08:33 22:08:53 22:09:59 22:10:56

22:11:58 22:12:59 22:12:47

20060815-222919

20060815-222906

20060815-222924 20060815-222928

20060815-222911 20060815-222915

3.00 4.00 6.00

8.00 11.00 14.00

20060815-231941 20060815-231945 20060815-231950

20060815-231955 20060815-232000 20060815-232005

Next Steps Study user behavior in test bed with actual data Study impact of weights on system function Develop revisions to end user policy with “ Needs”

evolving from subjective to more objective measures

Stated preferences, observed preferences Space/Time variability of weather Socio-economic impacts

Expand user base to include public, private companies

Launch Decision Sciences Project integration socio-economic factors into adaptive scanning Creating an end-to-end decision model of network

Thank you

System Parameters

Operating frequency 9.3 GHz

Wavelength 0.03 m Antenna Diameter 1.20 m Antenna Beamwidth 1.8

deg Antenna Gain 38 dB Max radar scanning speed

35 deg/sec Max radar acceleration 50

deg/sec2 Maximum range 36 km

Range resolution 26 m Effective Transmitter

Power 12.5 kW Average Transmitter Power

25 W Dual Pulse Repetition Frequency 1.6kHz, 2.4 kHz Noise Figure 5.5 dB System Losses -20 dB Mean Sensitivity 2.8 dBZ

Extra Slides

Test beds instantiate end-to-end system concepts

Rain, Urban Flooding (Houston)

Rain mapping, distributed hydro. modeling, flood predicting & response in an urban zone.

Wind, storm prediction (Oklahoma)

Wind mapping (100’s m resolution, 10’s second update) for detecting, pinpointing, forecasting wind events; 30 km node spacing.

Rain, mountainous terrain (Puerto Rico – student led)

Off-the-Grid Radar Network for quantitative precipitation estimation (QPE) over complex terrain, student-led project

http://www.nws.noaa.gov/

http://www.nws.noaa.gov/

User Rules and Weights

rule trigger

sector selection

elevations # radars contiguous sample frequency

data quality

NWS Rule 1 time 360 lowest 1 Yes 1 / min High Rule 2 storm task size full

volume 1 Yes 1 / 2.5

min High

Researcher Rule 1 rotation task size full

volume 2+ **** Yes 1 / 30

sec? High

Rule 2 reflectivity task size lowest two

1 Yes 1 / min High

Rule 2 velocity task size lowest two

2+ **** Yes 1/ min High

Rule 3 time 360 to get all 7 every 15 min

1 No 1/ 5 min High

EMs Rule 1 time 360 lowest 1 Yes 1 / min High Rule 2 reflectivity

over AOI task size lowest 1 Yes 1 / min High

Rule 3 velocity over AOI

task size lowest 2+ **** Yes 1/ 2.5 min

OS Rule 1 time 360 lowest

two 1 No 1 / 5 min Low

End User Policy Rules, Version 1End User Policy Control

Tasks (t) (blue) Scans (C) (green)

• Mechanism in place for adjusting weights (Wg)

• Default: all weights = 1.0

• Rules based on scan frequency and/or feature

• Utility based on time since the rule was last scanned (Ug)

ttasks

CionsconfiguratCtQktUJ

,,

),(,max


ggroups

gg ktUwktU,

,),(


ttasks

CionsconfiguratCtQktUJ

,,

),(,max


ggroups

gg ktUwktU,

,),(


ttasksCionsconfigurat

CtQktUC,,

,,maxarg*

How important is task t to the users?

Research Organization

Sensing

Distributing

Analysis & Prediction

Education

TechnicalIntegration

End-userIntegration

We interact with practitioners in the public and private sectors

streamingstorage

storage

queryinterface

data


datapolicy

resource allocation

SNR


1 2 3 4 5 6 7 8 9A G3G3G3G3G3G3G3G3 G3B G3G3G3G3G3G3G3G3 G3C G3G3G3G3G3G3G3G3 G3D G3G3G3G3G3G3G3G3 G3E G3G3G3G3G3G3G3G3G3F G3G3G3G3G3G3G3G3G3G G3G3G3G3G3G3G3G3G3H R1R1R2R2R1G3C2G3G3I R1F1F2, R1F2,H2R1G3C2G3G3J R1H1,F1H1,F1T2,R1R1G3C2G3G3K R1H1T2,H1T2,R1R1G3G3G3G3

Feature Repository


Data

IntermediariesValue Added Data Users

Private Sector: Value-Added Products

Products and Services, Decision Support Systems

Federal Government

Institutions/Industry

Academic Institutions/Researchers

Research

Emergency Response

Public

State Government Public

August 15 Storm East-to-west warm

frontal boundary with isolated storm cells; second area of stratiform rain with embedded convection

NWS issued one thunderstorm warning within the network for Grady County at 2130 UTC

Several severe wind reports were recorded just south of IP1 at approximately 0000 UTC.

CASA Test Bed 3 radars operational Single radar attenuation

correction Clutter, velocity, and

networked attenuation alg. not yet installed

DCAS running based on reflectivity rules and detections.

CASA data Frederick Radar

Phases of Response: High Level Decisions

3 days 2 days 24 hrs 1 hr. 4 hrs. Event~18 min.

PublicNotification

Spotter/Resp

Deployment

Spotter, ResponderAlert

Emerg.Response

Emergency Managers (Towns/Streets)

ProtectiveAction/

No Action

Understanding, believing,confirming, personalizing,

action necessary,action feasible

ImpactPublic (Streets)

Severe Weather Watch

MesoscaleDiscussion

1 Day Convective

Outlook

2 Day Convective

Outlook

3 Day Convective

Outlook

NWS–SPC (Regions)

DCAS: NowcastingSht. NWP, Storm Genesis

Boundary Sensing

DCAS: Feature Detection,Severe WX Sensing,

Nowcasting

DCAS: Ensemble ForecastsClear-Air Sensing

CASA Researchers (Rgn, Cty, Town, Sts.)

NWS–WFO (Counties/ Towns) WARNING

Short Term Forecast

Special WX Statement

Sht. Term ForecastWX Statement

Pre storm Environment Watch Warning Event

Spotter, ResponderResource Assessment

Hazardous Weather Outlook

Key Influencers

Existing practices

Existing sources of information and their perceived uncertainty: existing weather data and models, media info, ground truth

Organizational Issues: procedures, culture, training, evaluation metrics.

3 days 2 days 24 hrs 1 hr.4 hrs. Event~18 min.

PublicNotification

Spotter/Resp

Deployment


Emerg.Response


ProtectiveAction/

No Action




Severe Weather

Watch

MesoscaleDiscussion

1 Day Convective

Outlook

2 Day Convective

Outlook

3 Day Convective

Outlook

NWS–SPC (Regions)


Boundary Sensing


Nowcasting




Short Term Forecast






3 days 2 days 24 hrs 1 hr.4 hrs. Event~18 min.

PublicNotification

Spotter/Resp

Deployment


Emerg.Response


ProtectiveAction/

No Action




Severe Weather

Watch

MesoscaleDiscussion

1 Day Convective

Outlook

2 Day Convective

Outlook

3 Day Convective

Outlook

NWS–SPC (Regions)

Severe Weather

Watch

MesoscaleDiscussion

1 Day Convective

Outlook

2 Day Convective

Outlook

3 Day Convective

Outlook

NWS–SPC (Regions)


Boundary Sensing


Nowcasting




Short Term Forecast






Societal Issues: access to information and training; differences based on education, gender, race, income.

Risk Communication: tone and content of message, Multi-directional communications, social networks, etc.

NWS, EM, ResearcherObservation/Data Needs

NWS Warning

Socio-economicImpacts

ActualWeather

Socio-economic

Vulnerability

OtherWeather

InformationSources

PublicResponseDecisions

DCASResource

Optimization

DCAS Sensing

Capabilities

GISInformation

RiskCommunication/

PerceptionLead

Time/WarningArea

DCASScanningStrategy

DCASUser

Interface

DCASDetectingPredicting

EM Decisions

3 yr project for end-to-end decision model

• To develop an end-to-end integrated decision model for DCAS systems (Integrated System Model) from targeted observation, detection, forecast, warning, risk perception and response to socioeconomic impact.

• Use socioeconomic measures to drive CASA’s resource allocation and optimization.

• Implement decision model in an expanded DCAS system emulator that simulates warning, response, and impact.

casa: a new paradigm for end user driven data collection brenda philips director, industry, gov’t,...

Documents

data push slide

greatest slide

user needs

background user

new technology

year research project

control dcas user

end user partnerships