operational urban mesonet-driven model for homeland defense applications situational awareness for...

Operational Urban Mesonet-Driven Model for Homeland Defense Applications

Situational Awareness for Emergency Services ...

“Accurate environmental information is critical to operational success”

Mark C. Beaubien, Sr. Engineer

mcb@yesinc.com

2

The Problem

• Weather conditions affect plume dispersion

• Metropolitan scale met prediction capability limited both in training and real time data

• Real time data not in a usable format for command and control purposes (METAR)

• Legacy weather systems inadequate for real time decision making (e.g. evacuation)

3

Current Situation: Insufficient granularity of measurements

2D Satellite Data feeds 3D Numerical Weather Models“sparse matrix problem, poorly bounded”

4

Multi-Sensor Real Time Profiling of the Battlespace Atmosphere

GPS radiosonde•upper air winds•PTU•1000-100,000’

Total Sky Imager•cloud motion•% cloud cover •visibility•500-5000’

Wind Lidar •surface winds•outside wake•0-1000’

5

Weather Web Core Sensor Technology

Joint Battlespace Infosphere “Sensor Cloud”

6

YESDAQ

Fixed-Base Network of Multiple Sensor Types

7

Weather Web Network Data Architecture

Application-Specific Sensors Deployed in the field

ACQUIRE

Remote Database Stores in the Field Provide Redundancy

STORE

Data Links to Numerical Weather Prediction and other Downstream Command and

Decision ApplicationsDECIDE AND ACT

Centralized Replicated Database at Command Center

COMMUNICATE

8

Logistics for Metropolitan Scale Networks

Who are the data stakeholders?

Who owns the site real estate?

Who owns the equipment?

Who maintains the equipment?

Who physically controls the sites?

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10

Planning the Network

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Weather Web Test Bed - 2000

Mt Greylock3491 ft

S1

S2

High School925 ft

S2

Taconic SP1540 ft

S2

Mt Ramier2560 ft

S2Landfill610 ft

S2

Clarksburg SF2283 ft

Hariman-WestAirport653 ft

S1

S2

Notch Road1870 ft

Adams

North Adams

Williamstown

SouthWilliamstown

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Met Sensor Node - 2001

YES TSI-880

Sonic Anemometer

YES MET-2010DCsupply

(Present weather,sky, precip, visibility)

(PTH)

(Surface winds)

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Sensor Node 2004

CPUEEPROM A/D Converter

FM FSK transmitter

RF Amplifier

Xmit RF Antenna

Wind speeddirectionSensor

Pressure,Temperature, %Humidity,Sensors

GPS Engine(time/position)

GPS Satellites

1.5 GHz GPS recv Antenna

Optional RAD-7001 Nuclear Radiation Detector

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TMS-7200

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Real Time Display

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Total Sky Imager

Animated Panoramic Horizon View

Animation Controls

Raw Image View

National Weather ServiceNational Weather ServiceAlbany, NYAlbany, NY

Model TSI-880 imager

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Inferring winds from cloud motion

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ASRC “REAL TIME” MOHAWK

0:00

0:01

0:02

0:03

1 mile separation

19

ASRC “TIME SHIFT -3 min” MOHAWK

0:00

0:01

0:02

0:03

20

In-situ Upper Air GPS Radiosonde

MERV ARL “FORCE PROTECTION”

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Optical Fiber Wind Lidar

Lidar: Light Detection and Ranging– Same as Radar only done at Light frequencies

– Light Source is typically a monochromatic Laser

– When Sensing Environmental Parameters: LIDAR

– When Sensing or Imaging “Hard Targets”: LADAR(Laser Detection and Ranging)

Laser Light Source

EnvironmentalElement (Smoke, Cloud, etc.)

"Hard Target"

Scattered/Reflected Light

Optics(Telescope)

OpticalDetector

ElectronicAmplification

Data Reduction and Display

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Soft Target Tracking: Vector-Resolved Winds

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1 4 7

10 13

16

19

22

25S

1 S5 S

9 S13 S

17 S21 S

25 S29 S

33-0.01

-0.005

0

0.005

0.01

0.015

0.02

0.025

0.03

Time, minutes

Signal Intensity

Time Evolution of Small Agricultural Fire3/26/02

0.025-0.03

0.02-0.025

0.015-0.02

0.01-0.015

0.005-0.01

0-0.005

-0.005-0

-0.01--0.005

Range, ft

1800ft

2400ft

Fire Center at a range of 2160ft

Hard Target Tracking: of Smoke Plumes

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Lidar Data: Hard and Soft Target Data Examples

Target Returns, Diode/EDFA MOPA120ns Nominal Pulse Width, 7.5KHz PRF

Peak Pulse Power 5 - 10 W

Range, relative units

Sign

al A

mpl

itud

e, r

elat

ive

unit

s

100m Deep Cloud Layer Centered at 150m150m

1.8km2.04km

Light Pole behind Bush

House thru Tree Screen2.1km

Power Wire over Hillside Grass306m

486m

Double Pulse WaveformTwo Tree Lines

2.1km1.2km

Tree Covered Mountain Top 4.32km

Smoke Column fromAgricultural Fire

585m 830m

Aluminum Calibration Target873m

Field Treeline

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Lidar Data: Tracking Precipitation and Hydrosols

Snow Velocity TrackFalling Snow 0.25in/hr, Vertical Axis, 14 ft Range, 10mw

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 50 100 150 200 250 300

Time, s

Fa

ll V

elo

cit

y, m

/s

Frozen Precipitation Velocity Spectra, Time History - 2/14/03, Corrected to Vertical

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0.055

0 1 2 3 4 5 6 7 8

Velocity, m/s

Rel

ativ

e S

pec

tral

Inte

nsi

ty, v

olt

s

Snowflake, 02:36:35.20PMSnow Column, 02:45:50.38PMMixed Fog-Frozen Precipitation, 03:00:45.29PMMixed Fog-Frozen Precipitation, 03:00:48.29PMMixed Frozen Precipitation, 03:10:52.09PM

1.5-2mm Snow Flakes, <1in/hr

Fine Snow/Sleet

Fog/Frozen Fog

Raindrop Spectra 1mw laser, 14ft Range, Rain Rate < 0.25in/hr (light and variable)

-0.005

-0.003

-0.001

0.001

0.003

0.005

0.007

0.009

0.011

0.013

0.00E+00 1.00E+00 2.00E+00 3.00E+00 4.00E+00 5.00E+00 6.00E+00 7.00E+00 8.00E+00Velocity, m/s

Inte

ns

ity, re

lati

ve

sc

ale

10:43:26.10AM11:04:07.79AM11:04:08.34AM11:05:25.17AM11:10:11.26AM11:12:01.50AM

Spectral Signature Extraction & Distribution Analysis

Fog/Drizzle/Rain Snow/Sleet

Vertical Velocity - Mixed Drizzle/Fog

0

0.5

1

1.5

2

2.5

3

3.5

0 50 100 150 200 250

Time, s

Velo

city

, m/s

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RF link advantages vs. telco

• Rapid setup

• No long term recurring costs

• Reliability

• Flexibility if stations must move

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Summary

•Yankee develops and produces precision

meteorological instrumentation and systems

•Weather Web - A rapidly deployable network of

expendable meteorological sensors

•Weather Web Test Successful in Boston @DNC

•TMS Met sensor linked with HPAC

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Single Point Access to Environmental Data