Community Environmental Networks for Risk Identification & Management

1. Community Environmental Networks forRisk 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 Undergraduate Meteorology Majors

2. 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 development SELS – CNAHS

3. Given sufficient information Systems Monitoring, Modeling, & Prediction Identify developing hazards (and/or usefulapplications) Climate Study, Impacts, Change as well… SELS – CNAHS

4. What sort of hazards/applications? • High impact • Short duration • Limited area • Population • Energy • Economy • Health • Welfare SELS – CNAHS Rapid Response

5. 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 SELS – CNAHS Scenario development – examples Environmental Feature Parameters to Sample Heat Distribution CO2, CO, habitation Energy consumptioninternal temperature, Wind/Alternative Energy external temperature Environmental Feature Parameters to Sample Air Quality CO2, CO, traffic volume Transport Contaminants wind speed, direction Local 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

6. Prototype – Apply to KU Campus • Relevant problems • Real applications • Student participation • Prototype deployment Techno-Interactive SELS – CNAHS

7. Wireless Sensor Network & more… • 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 Alternatively climate data = empirical prob (Vulnerability/susceptibility defined) SELS – CNAHS

8. Prototype Development Environmental Information Network Outdoor observations GIS Integration… SELS – CNAHS

9. GIS Mapping Preparation SELS – CNAHS

10. Examining the “Local Neighborhood” = weather platforms = sensors • 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 SELS – CNAHS

11. 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 • Cemeteries • 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? SELS – CNAHS

12. 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 SELS – CNAHS

13. 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 Interactive Decision-Making System SELS – CNAHS

14. Tie-in with demographic information… Data Integration

15. Putting the Two Data Sets Together How does it all “fit” together? Real Time Observations Methodology WRF EMS Methodology Specify Domain and Parameters Specify parameters and domain of Desired Location .5’ Spatial Resolution using NAM SPoRT data Low Income Elderly 7x15 mile study area collecting Temperature Data. = Decision-Making Model Processing Create Shapefile of station locations Prioritize emergency services based on demographic map Convert netCDF to GRIB within model program Relation of hotspots to Land Use Feed data collected from stations and sensors GRIB to GIS shapefile using deGRIB program Pinpoint forecast errors in model Creation of .dbf file for ArcGIS to read data and relating each set of readings to station location Identify hotspots GIS shapefile to raster Climate Impacts Interpolation of data to create isotherms Subtract model Data from Real Time Observations with raster calculator (or CDC data: NCAR Re-Analysis and other datasets) Convert maps to same cell size as model data. Convert maps to same cell size as model data. SELS – CNAHS

16. Verification: Compare, Contrast, Establish “Truth” Sensor WRF Difference 5:00 am 11 :00 am 5:00 pm 11:00 pm • 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

17. What else may be done? • 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 Alternative Hazards Online System SELS – CNAHS

18. For example… Contour Maps: Patterns & Features of Interest; Source/Sink Day 1: Carbon Dioxide for 07/14/2009 Day 2: Carbon Dioxide for 07/15/2009 Operational => • Data • Carbon Dioxide • Temperature • Pressure • Humidity • Light • Sound • Water Quality 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 SELS – CNAHS

19. Kean “We Map It” for Operations (or Climate Impacts)[Weather and Ecosystem Monitoring, Assessment, and Prediction for Integration and Training] Search query Search results Map contents show basic layers and contour maps of different variables Identify button shows details Rapid Response SELS – CNAHS

20. Thank you for your time… AnyQuestions??? Acknowledgements Kean 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, AlicjaTrzopek Computer Science: JhonEspin, Nick Doell, Ryan Suleski, Justin Czarnik, Marvin Andujar, Frank Kendall, and Brian Sinnicke SELS – CNAHS