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

“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.

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

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 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.”

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) 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

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

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.

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

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

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

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)

Multiple-Doppler Analysis

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.

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

Thank You

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