v.chandrasekar colorado state university mpar symposium nsf engineering research center for...

V.ChandrasekarColorado State University

MPAR Symposium

NSF Engineering Research Center for Collaborative Adaptive Sensing of the

Atmosphere (CASA)

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

October 12, 2007 2MPAR

“There is insufficient knowledge about what is actually happening (or is likely to happen) at the Earth’s surface

where people live.” [NRC, 1998]

gap - earth curvature prevents 72% of the troposphere below 1 km from being observed.

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


Radar Network Coverage Fractions

0

10

20

30

40

50

60

70

80

90

100

1 10 100 1000

Spacing (km)

Co

vera

ge

(%)

500m

1000m

2000m

3000m

CASA OTG

CASA 30km

Nexrad/SoA/MPAR

% of CONUS covered at different heights versus radar spacing

Closer spacing is needed to overcome curvature blockage, observe below 2 km.


Spatial and temporal resolution

• Several high impact phenomena are of much smaller spatial and temporal scales, such as space-time variability of tornadoes, downbursts and urban flooding.

• Several urban flood warning systems require reports at 100m spatial scales.

• Current space time sampling is insufficient.


“Weather Radar Technology Beyond NEXRAD”National Research Council, National Academies Press, 2002 Chair: Prof. Paul Smith

Recommendation – Far Term:“The potential for a network of short-range radar systems to provide enhanced near-surface coverage and supplement (or perhaps replace) a NEXRAD-like network of primary radar installations should be evaluated thoroughly.”

Far-term ~ available within the 25-30 year scope of the report


Guiding Systems Vision

Distributedradars

End users

… and control

Distributed, adaptivecomputation

“Sample atmosphere when, where end-user needs are greatest.”.”

Revolutionize our ability to observe, understand, predict and respond to weather hazards by creating DCAS networks that sample the atmosphere where and when end-user needs are greatest.


CASA Program selection process

CASA: Year 4 of a 10 year ERC program

High Stakes:

2002 Competition: 136 letters of intent; 100 pre-proposals

20 invited full proposals; 8 site visits;

3 centers remaining at Year 4


NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA)

CASA core (NSF) :•Weather application with multiple users•Initial focus was < 3 km; now looking > 3 km

Dense networks of low power radars:

collaborating radars:improved sensingimproved detection,

prediction, warning, response

responsive to multiple end-user needs

10,000 ft

tornado

wind

earth surface

snow

3.05

km

3.05

km

0 40 80 120 160 200 240RANGE (km)


Deployment Numbers

1

10

100

1,000

10,000

100,000

1 10 100 1000

Spacing(km)

# o

f R

ad

ars

National (3000km)

Regional (500km)

Urban (75km)


Deployment Numbers

1

10

100

1,000

10,000

100,000

1 10 100 1000

Spacing(km)

# o

f R

ad

ars

CASA OTG

CASA 30km

Nexrad/SoA

IP1

IP5

National (3000km)

Regional (500km)

Urban (75km)

We’re doing this now.

We will do this next.


Low Cost EScan Panels

10W's to 100 W peak power per panel 2° pencil beam, 1m X-band array (9 GHz) Dual linear polarization # array panels per installation: 3 or 4 Azimuth scan range: ±450 to ±600

Elevation scan range: 0-200(low level coverage, < 3 km) 0-560 (full coverage, to 22 km)

Cost: ~ $10k per panel

Additional specifications are a work-in-progress


The IP1 Testbed and the CASA-DCAS Concept

Users

MC&C

Data Fusion / Algorithm

Radar Network


IP1 Location

• Covers an area of 7000 square km• The deployment of this 4-node network represents a unit-cell of a

larger deployment.


End users: NWS,emergencyresponse

streamingstorage

storage

queryinterface

data

Resource planning,optimization

data policy

resource allocation

SNR

Meteorological DetectionAlgorithms

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

MC&C: Meteorological command and control

Meteorological Task

Generation

1. Radars Scan atmosphere and send data to repository (initially centralized, later distributed)

2. Weather Detection algorithms run on data

3. Detections and other data are “posted” in Feature Repository, a 3-d Grid of test bed region

4. Tasks are generated based on detections and User Rules

5. Optimal Radar Scans are configured to complete as many tasks as possible• User Utility(user priority and rules) • Quality of the scan

IP1 System Architecture


IP1 Capabilities:

500 m resolution

Multiple-Doppler

200 m coverage floor

Rapid (~1 min) update

Adapt to weather, user preferences

Enablers:

Rapid scan radars

Real-time processing

MCC

IP1 NEXRAD (WSR-88D)


Multiple-Doppler Analysis


Dual Doppler Wind Retrievals for June 10, 07. Please note the low level coverage as well as storm top.


Summary

• CASA / DCAS vision is a very compelling concept, that is economically viable.

• The preliminary results from the first test-bed in Oklahoma is a proof of concept.

• This is the only current solution available, to satisfy the gaps in low level coverage and space time sampling needs.

• CASA systems yield full 3D vector winds, that are critical to drive models.

• It has generated national and international interest.

• Dense networks = Super MPAR


Thank You


Price Per Node vs. SpacingAssumes $1B Budget for CONUS Deployment

0.01

0.1

1

10

1 10 100 1000

Spacing (km)

Co

st (

$M)

CASA 30km Class

Nexrad/SoA

IP1

IP5 Target

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