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
20
Embed
Community Environmental Networks for Risk Identification & Management
Community Environmental Networks for Risk Identification & Management. Paul J. Croft, Feng Qi, Patricia Morreale (Meteorology, GIS, Computer Science). Preparing an Interactive Decision-Making System…. Meteorology Research Team. School of Environmental and Life Sciences. - PowerPoint PPT Presentation
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
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
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
6/23
/201
0 15
:59
6/23
/201
0 18
:10
6/23
/201
0 20
:20
6/23
/201
0 22
:30
6/24
/201
0 0:
40
6/24
/201
0 2:
50
6/24
/201
0 5:
00
6/24
/201
0 7:
09
6/24
/201
0 9:
20
6/24
/201
0 11
:29
6/24
/201
0 13
:40
6/24
/201
0 15
:50
6/24
/201
0 18
:00
6/24
/201
0 20
:10
6/24
/201
0 22
:20
6/25
/201
0 0:
30
6/25
/201
0 2:
39
6/25
/201
0 4:
50
6/25
/201
0 6:
59
6/25
/201
0 9:
10
6/25
/201
0 11
:20
6/25
/201
0 13
:30
6/25
/201
0 15
:40
6/25
/201
0 17
:50
6/25
/201
0 20
:00
6/25
/201
0 22
:09
6/26
/201
0 0:
20
6/26
/201
0 2:
29
6/26
/201
0 4:
40
6/26
/201
0 6:
50
6/26
/201
0 9:
00
54
58
62
66
70
74
78
82
86
90
94
98Shield vs No Shield
Shield Temp No Shield TempTime
Tem
p (
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
• 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