1. Colorado State University
NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA)
V.Chandrasekar
Colorado State University
MPAR Symposium

2. “There is insufficient knowledge about what is actually happening (or is likely to happen) at the Earth’s surface where people live.” [NRC, 1998]
5.4 km
1 km
2 km
4 km
10,000 ft
3.05 km
snow
gap
wind
tornado
Horz. Scale: 1” = 50 km
Vert. Scale: 1” -=- 2 km
earth surface
TODAY’S WEATHER RADAR NETWORKS ARE COMPRISED OF PHYSICALLY LARGE, WIDELY SEPARATED RADARS THAT DO A VERY EFFECTIVE JOB MAPPING THE MIDDLE AND UPPER PARTS OF THE TROPOSPHERE, BUT THE CURVATURE OF THE EARTH PREVENTS THESE SYSTEMS FROM SEEING DOWN LOW WHERE STORMS FORM AND IMPACT US. AS A CONSEQUENCE OF THIS INABILITY TO SEE DOWN LOW, THERE IS INSUFFICIENT KNOWLEDGE ABOUT WHAT IS ACTUALLY HAPPENING -- OR IN TERMS OF FORECASTING - WHAT IS LIKELY TO HAPPEN - AT THE EARTH’S SURFACE WHERE PEOPLE LIVE. 40 80
120
160
200
240
RANGE (km)
gap - earth curvature prevents 72% of the troposphere below 1 km from being observed.

3. Radar Network Coverage Fractions
% of CONUS covered at different heights versus radar spacing
Closer spacing is needed to overcome curvature blockage, observe below 2 km.
CASA OTG
CASA 30km
Nexrad/SoA/MPAR

4. 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.

5. “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 year scope of the report

6. Guiding Systems Vision
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.
Distributed, adaptive
computation
End users
Distributed
radars
DCAS is our acronym for reconfigurable networks of small radars capable of focusing their resources onto specific sub-volumes of the atmosphere in response to changing weather & changing user needs.
… and control
“Sample atmosphere when, where end-user needs are greatest.”

7. 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

8. NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA)
Dense networks of low power radars:
collaborating radars:
improved sensing
improved detection, prediction, warning, response
responsive to multiple end-user needs
10,000 ft
tornado
wind
earth surface
snow
3.05 km 40 80
120
160
200
240
RANGE (km)
CASA core (NSF) :
Weather application with multiple users
Initial focus was < 3 km; now looking > 3 km

9. Deployment Numbers
National (3000km)
Regional (500km)
Urban (75km)

10. Deployment Numbers National (3000km) Regional (500km) CASA 30km
Urban (75km)
CASA OTG
IP1
IP5
We will do this next.
Nexrad/SoA
We’re doing this now.

11. 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

12. The IP1 Testbed and the CASA-DCAS Concept

13. IP1 Location Covers an area of 7000 square km
The deployment of this 4-node network represents a unit-cell of a larger deployment.

14. IP1 System Architecture
2. Weather Detection algorithms run on data
1. Radars Scan atmosphere and send data to repository (initially centralized, later distributed)
3. Detections and other data are “posted” in Feature Repository, a 3-d Grid of test bed region
streaming
storage
query
interface
data
Resource planning,
optimization
policy
resource allocation
SNR
Meteorological
Detection
Algorithms 1 2 3 4 5 6 7 8 9 A G3 B C D E F G H R1 R2 C2 I 2,
2,H2 J H1 , F1 T
2,R1 K
2,H1
Feature Repository
MC&C: Meteorological
command and control
Task
Generation
End users: NWS,
emergency
response
5. Optimal Radar Scans are configured to complete as many tasks as possible
User Utility
(user priority and rules)
Quality of the scan
4. Tasks are generated based on detections and User Rules

15. Adapt to weather, user preferences
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)

16. Multiple-Doppler Analysis

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

18. Summary Dense networks = Super MPAR
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

19. Thank You

20. Price Per Node vs. Spacing Assumes $1B Budget for CONUS Deployment
Nexrad/SoA
IP1
IP5 Target
CASA 30km Class