Community Environmental Networks for

Risk Identification & Management

Paul J. Croft, Feng Qi, Patricia Morreale

(Meteorology, GIS, Computer Science)

Preparing an Interactive Decision-Making System…

School of Environmental and Life Sciences

Undergraduate Meteorology Majors

CENRIM: Intent is to make decisions…

• Environmental & Related Monitoring

• Real-time inquiry/query

• Wireless Sensors• Automated• Adaptable (movable)• Multi-layered data

Collect data Display data GIS mapping Animation Integrated analysis Scenario

developmentSELS – CNAHS

Given sufficient

information

Identify developing

hazards(and/or useful applications)

Systems Monitoring, Modeling, & Prediction

SELS – CNAHS

Climate Study, Impacts, Change as well…

What sort of hazards/applications?

• High impact• Short duration• Limited area• Population• Energy• Economy• Health• Welfare

Rapid Response SELS – CNAHS

SCENARIO DEVELOPMENT – EXAMPLES

Engineering of heating/cooling zones & timing Internal & External microclimate used as guide to green technology Seasonal variations & insulation strategies Alert to maintenance and/or physical discomfort or hazards Source/Sink and automated response system

Environmental Feature Parameters to Sample

Heat Distribution CO2, CO, habitationEnergy consumption internal temperature, Wind/Alternative Energy external temperature

Environmental Feature Parameters to Sample

Air Quality CO2, CO, traffic volumeTransport Contaminants wind speed, directionLocal Flooding or Severe water floats, rainfall rates

Provide real-time monitoring, automated prompts Increased traffic volume; flow rates; pollutant

pooling Alerts to authorities; traffic re-routing as needed Pre-alerts to authorities for advancing system or

as forecast

SELS – CNAHS

Prototype – Apply to KU Campus

• Relevant problems• Real applications• Student participation• Prototype deployment

Techno-Interactive

SELS – CNAHS

Wireless Sensor Network & more…

ALTERNATIVELY CLIMATE DATA = EMPIRICAL PROB

(VULNERABILITY/SUSCEPTIBILITY DEFINED)

• Composed of low-cost, embedded sensors• IRIS Mote 2.GHz (shown), 500 meter

range with 250 Kbps data rate• www.crossbow.com• www.sunspotworld.com• Cross-check with others• Develop/Create sensors

SELS – CNAHS

Prototype Development

OUTDOOR OBSERVATIONS

Environmental Information Network

GIS Integration…SELS – CNAHS

GIS Mapping PreparationSensor measurements

2006 Real Color Ortho imagery NJDEP GIS Data Warehouse

ESRI GIS Data, USA

Pressure

Temperature

Imagery campus

Flowline

Roads

Water Body

Airports

Railroads

Hospitals

Cemeteries

Churches

Golf Courses

Parks

Farms

Schools

Clip

Imagery 7x7

NJDEP Land Use Database

Geocoding

Land Use Map7X7 Digitize

Campus Buildings

Campus Sidewalks

Campus Fields

Campus Trees

Campus Sites

Roads

Water Body

Flowline

Clip

Point maps

Spatial interpolation

Contour maps

Clip Clip Clip

Carbon Dioxide

Humidity

SELS – CNAHS

Examining the “Local Neighborhood”

• NYC – Metropolitan area (most populated and urbanized location)• Sampling 7x15 mile area for data & observations/collection• Area selected for its diversity in landscapes (i.e. urban, rural, et cetera)

• Goal of Research: Visualization for decision-making and scenario building– Visualize and analyze relationships between variables (Atmosphere, Land Use)– Seek understanding as to why the patterns exist/change– Examine local data versus WRF Model data for real time operations– Predict variations in space and time for application to decision-making process

= weather platforms

= sensors

SELS – CNAHS

Site Selection – Specify Characteristics

Key locations selected to study modification of air temperature were based on

– Land Use Types

– Elevation

– City Population

– Imperviousness

– Satellite Images

– Churches

– CemeteriesSELS – CNAHS

Data variations as related to the local CWA landscapes

These are forecast locations of interest for verification by the user & apply decision-making process locally

Sensor variations v. model v. verification

NDFD applications?

Sensor Deployment• HOBO data loggers used to record temperature 1.5 meters

above the ground, at the chosen sites• Radiation shields constructed to reduce radiative effects• Calibration in time/space of sensors

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Shield Temp No Shield TempTime

Tem

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C)

SELS – CNAHS

Scenario = “Hot/Dry Summer”

• Data collected August 3 – 5, 2010

• Temperature data every 5 min

• Data from 3 local stations used for comparison

• Data from CWOP/Other sites in the region

• Model data from the WRF EMS platform in 6 hour increments collected for comparative analysis and for combining data sets for decision-making purposes

SELS – CNAHS

Interactive Decision-Making System

Tie-in with demographic information…

Median Household Income

75001 - 116088

10232 - 25000

25001 - 40000

40001 - 55000

55001 - 75000

Elderly/sq. mile

1-650

651-1208

1209-1875

1876-3036

3036-4394

Data Integration

GRIB to GIS shapefileusing deGRIB program

Putting the Two Data Sets Together

Model Processing

GIS shapefile to raster

Convert netCDF to GRIBwithin model program

SELS – CNAHS

Specify parameters and domain of Desired

Location

7x15 mile study area collecting

Temperature Data.

Create Shapefile of station locations

Feed data collected from stations and sensors

Creation of .dbf file for ArcGIS to read data and

relating each set of readings to station location

Interpolation of data to create isotherms

Convert maps to same cell size as model data.

Convert maps to same cell size as model data.

WRF EMS Methodology

Real Time ObservationsMethodology

Subtract model Data from Real Time Observations with raster

calculator (or CDC data: NCAR Re-Analysis and other datasets)

Specify Domain and Parameters

.5’ Spatial Resolution using NAM SPoRT

data

Pinpoint forecast errors in model

Identify hotspots

Relation of hotspots to Land Use

Prioritize emergency services based on demographic map

Low Income

Elderly

How does it all “fit” together?

= Decision-Making

Climate Impacts

Sensor WRF Difference

5:00 am

11 :00 am

5:00 pm

11:00 pm

Verification: Compare, Contrast, Establish “Truth”

• The temperature difference map identifies weaknesses in operational model by showing cool or warm spots; or by showing discrepancies in forecast conditions

• Identifying areas of warmer temperature essential for risk management of emergency services to environments based on a scale of high or low priority

SELS – CNAHS

• Data mining and analysis for spatial and temporal pattern recognition & correlations; time series analysis

• Visualization for data discovery techniques, possible “CAVE” use (supercomputer) to explore interactions

• Contour and additional map analyses for operational and risk management use; planning and management

• Flash animation, uncertainty visualization, additional user-defined scenarios and tie-in socio-economic systems

• Societal risk factors, emergency management, empirical-climatic investigations for resource allocation/expectation

• Vulnerabilities evaluation, natural and human systems coupling/modeling; cost-effectiveness

SELS – CNAHS

What else may be done?A

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azar

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nlin

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Contour Maps: Patterns & Features of

Interest; Source/Sink

Day 1: Carbon Dioxide for 07/14/2009 Day 2: Carbon Dioxide for 07/15/2009

Day 3: Carbon Dioxide for 07/21/2009 Day 4: Carbon Dioxide for 07/22/2009

Day 5: Carbon Dioxide for 08/04/2009 Day 6: Carbon Dioxide for 08/05/2009

Data• Carbon Dioxide• Temperature• Pressure• Humidity• Light• Sound• Water

Quality

For example…

Operational =>

SELS – CNAHS

Kean “We Map It” for Operations (or Climate Impacts)[Weather and Ecosystem Monitoring, Assessment, and Prediction for Integration and Training]

Search querySearch results

Identify button shows details

Map contents show basic layers and contour maps of different variables

Rapid Response

SELS – CNAHS

Thank you for your time…

AcknowledgementsKean Departments of Geology & Meteorology and Computer Science: Faculty & Staff, Students and Majors, Alumni and Student Volunteers

Dean and/or College of Natural, Applied, and Health Sciences and the School of Environmental and Life Sciences

Office of Research & Sponsored Programs through the RIA & SpF Programs at Kean University

Geology & Meteorology: Matt Albanese, Tom Skic, Tom Giordano, Seth Docherty, and Will Moore, Alicja

Trzopek

Computer Science: Jhon Espin, Nick Doell, Ryan Suleski, Justin Czarnik, Marvin Andujar, Frank Kendall,

and Brian Sinnicke

SELS – CNAHS