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CASA: A NEW PARADIGM FOR END USER DRIVEN DATA COLLECTION

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Presentation on theme: "CASA: A NEW PARADIGM FOR END USER DRIVEN DATA COLLECTION"— Presentation transcript:

1 CASA: A NEW PARADIGM FOR END USER DRIVEN DATA COLLECTION
Brenda Philips Director, Industry, Gov’t, and End User Partnerships ERC for Collaborative Adaptive Sensing of the Atmosphere AMS Corporate Forum 2007 March 22, 2007 NSF Multidisciplinary research center. We’re creating a new paradigm for weather observation systems one that’s networked based vs. current focus on one radar and that is user needs are considered in the design and operation of the network.

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

3 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 Over the first five years CASA will have a total of $40million dollars, anchored by a grant from NSF and supplemented by univeristy and industry funding. We’ve been operating for just over a year now.

4 10/27 F1 tornado illustrates limitations of current observation technology
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.” In October 27 a tornado, that started out as a water spout came ashore Apalachicola florida Source: NWS Tallahassee Forecast Office

5 Mismatch: technology vs
Mismatch: technology vs. temporal and spatial scales for decision making NEXRAD 250 km spacing Horizon problem causes coverage gap ~ 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 40 80 120 160 200 240 RANGE (km) Horz. Scale: 1” = 50 km Vert. Scale: 1” -=- 2 km 5.4 km 1 km 2 km 4 km gap What we have here is a mismath of the technology and the decisions. Here’s the path of the tornado that lasted touched down for 5 min. It would have been useful know of a street leve where it was headings, It would have been good to observe it.

6 Mismatch: technology vs
Mismatch: technology vs. temporal and spatial scales for decision making NEXRAD 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 40 80 120 160 200 240 RANGE (km) Horz. Scale: 1” = 50 km Vert. Scale: 1” -=- 2 km 5.4 km 1 km 2 km 4 km gap What we have here is a mismath of the technology and the decisions. Here’s the path of the tornado that lasted touched down for 5 min. It would have been useful know on a street level where it was headings, It would have been good to observe it.

7 CASA addresses “mismatch” with DCAS (distributed collaborative adaptive sensing)
Short range (~ 30 km) radars Lower troposphere coverage 100’s meter resolution Avg. 30 second updates Adaptive Scanning based on user needs, “data pull” 10,000 ft tornado wind earth surface snow 3.05 km 40 80 120 160 200 240 RANGE (km) New observing capability has great potential to improve our ability to detect, track, forecast, warn, and ultimately respond to low level hazards. “Sense the Atmosphere where and when user needs are greatest”

8 End User Integration links technology with warning and response
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 DCAS User Driven Adaptive Scanning User Interaction with new technology for decision making Impact of DCAS Technology on Communications, Public Response, and Vulnerability Social and Economic Value of CASA Data

9 We interact with practitioners in the public and private sectors
Value Added Data Users Intermediaries Public State Government Emergency Response 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 Data Federal Government Public Private Sector: Value-Added Products Products and Services, Decision Support Systems Institutions/Industry Research Academic Institutions/ Researchers

10 We have a multidisciplinary team
Brenda Philips, MBA, UMass Ben Aguirre, Sociology, UDel Ellen Bass, Human Factors, UVa Walter Diaz, Political Science, UPRM Kevin Kloesel, Meteorology, OU Dave Pepyne, ECE, UMass Havidan Rodriguez, Sociology, UDel Roman Krzysztofowicz, Decision Sciences, UVa,

11 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) I’d like to shift to the oklahoma test bed 7000 sq. km (90 km long) 2 tornados 4 tornado warnings 50 severe storm warnings

12 Data Pull: system optimally collects data based on user needs, evolving weather and radar capabilities. 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. user policy ResearchersMedia Optimal radar scan configuration developed based on: How Important is a task to users? What is the quality of the scan? 4. Tasks are generated based on clustering similar weather features.

13 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

14 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.

15 Another view of optimization
End User Policy How important is task t to the users? ( ) å = g groups k U w , User Weights User Rules

16 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.

17 Initial approach to user rules focuses on weather features
This is V0 of the rules. As you can see they are based on specific feature, such as a tornado detection and it’s importance to users with reference to geography. Th

18 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 Don’t Communications Mesoanalysis 1,2,3, Ground truth After developing those rules we continued discussions with users. Our researchers were concerned that they wouldn’t get the data to feed their model; and that if a tornado developed they wouldn’t necessarily get the multi-radar information they wanted. This slide shows how we learned about version 0 from the NWS. We used lo-res emulator out put to engage them in a discussion about scanning strategies and rules. Their deicions making depends on visual examination of the data and create a “mental movie” of an evolving event. So they wanted regular scans at 360, they’re also interested in dynamic data imput. “NWS Warning Process”, Liz Quoetone, NWS Warning Decision Training Branch, presentation at 2005 CASA workshop

19 Changes to rules Incorporation of interval-based scanning
Expansion of the definition of a storm cell Introduction of contiguous scans Dynamic Data Requests

20 Current rules focus on “time since last scanned”
trigger Sector Selection Elevations # Radars Contiguous Sampling interval NWS N1 time 360 Lowest two 1 Yes 1 / min N2 storm task size full volume 1 / 2.5 min Researcher R1 rotation 2+ 1 / 30 sec R2 reflectivity Full volume 2 velocity lowest two 1/ min R3 No 1/ 5 min EMs E1 lowest E2 reflectivity over AOI E3 velocity over AOI 1/ 2.5 min OS O1 1 / 5 min Table 1. User Rules, End User Policy, Version 1

21 User Weights Figure 3.2a

22 User Weights

23 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.

24 22:08:33 22:08:53 22:09:59 22:10:56 22:12:59 22:12:47 22:11:58

25 3.00 4.00 6.00 8.00 11.00 14.00 Data collected from KSAO on , between 23:19:45 and 23:20:15 UTC.

26 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

27 Thank you

28 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

29

30 Extra Slides

31 Rain, mountainous terrain (Puerto Rico – student led)
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

32 End User Policy Control
User Rules and Weights 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) How important is task t to the users? End User Policy Control Tasks (t) (blue) Scans (C) (green) End User Policy Rules, Version 1 Rule is a combination of feature and timing

33 Research Organization
Sensing Distributing Analysis & Prediction Education Technical Integration End-user Integration

34 We interact with practitioners in the public and private sectors
Value Added Data Users Intermediaries Public State Government Emergency Response 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 Data Federal Government Public Private Sector: Value-Added Products Products and Services, Decision Support Systems Institutions/Industry Research Academic Institutions/ Researchers

35 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

36 Phases of Response: High Level Decisions
Understanding, believing, confirming, personalizing, action necessary, action feasible Protective Action/ No Action Impact Public (Streets) Emergency Managers (Towns/Streets) Spotter, Responder Resource Assessment Spotter, Responder Alert Spotter/ Resp Deployment Public Notification Emerg. Response NWS–WFO (Counties/ Towns) Hazardous Weather Outlook Short Term Forecast Special WX Statement Sht. Term Forecast WX Statement WARNING Severe Weather Watch Mesoscale Discussion 1 Day Convective Outlook 2 Day 3 Day NWS–SPC (Regions) CASA Researchers (Rgn, Cty, Town, Sts.) DCAS: Ensemble Forecasts Clear-Air Sensing DCAS: Nowcasting Sht. NWP, Storm Genesis Boundary Sensing DCAS: Feature Detection, Severe WX Sensing, Nowcasting Pre storm Environment Watch Warning Event 3 days 2 days 24 hrs 4 hrs. 1 hr. ~18 min. Event

37 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. 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.

38 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. What we have here is a model of the end to end system based on the core decisions of the system, and the information and uncertainties. The goal would be to create mathematical models linking these By creating this influence diagram and developing the mathematical model of these relationship, we be able to model how a change in a decision flows impact the other parts of the system through to impact, and the value of that system.


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