Third International Conference on Early Warning · S. Habib/EWC III-rev1.ppt (3/27/06) 1 Third...
Transcript of Third International Conference on Early Warning · S. Habib/EWC III-rev1.ppt (3/27/06) 1 Third...
S. Habib/EWC III-rev1.ppt (3/27/06)1
Third International Conference on Early Warning
Bonn, GermanyMarch 27-29, 2006
Third International Conference on Early Warning
Bonn, GermanyMarch 27-29, 2006
Acquiring Comprehensive Observations using an integrated Sensor Web for Early Warning
Shahid Habib, D.Sc., P.E.NASA Goddard Space Flight Center
Greenbelt, MarylandSteve Ambrose
NASA HeadquartersWashington, DC
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ContentsContents
• Background • Science and Natural Disasters• Observing Systems• Applications• Summary
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What Drives Our Planet?What Drives Our Planet?
2 1.4 ± 0.2
0
1
-1
-2
F ( W
/ m
2 )
Greenhouse Gases Other Anthropogenic Forcings Natural Forcings
0.6 ± 0.20.7 ± 0.2
0.35 ± 0.05 0.15 ± 0.05
-1.0 ± 0.4
- 0.1 ± 0.2- 0.2 ± 0.2- 0.2 ± 0.1
0.4 ± 0.2
(0.2, - 0.5)
0.8 ± 0.4
- 1.1 ± 0.5
- 0.2 ± 0.2
CO2 CH4
CFCs
TroposphericOzone
N2O Sulfate+
Nitrate
BlackCarbon
OrganicCarbon
BiomassBurning
SoilDust
VolcanicAerosols(range of Decadal
mean)LandCover
Alterations
ForcedCloud
ChangesSun
(nitrate)
(indirect viaO3 and H2O)
Tropospheric Aerosols
(indirect viastratospheric
ozone)
(semi-direct,dirty cloud
&snow effects) (indirect via O3)
Re: J. Hansen/NASA GISS
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Sun and Earth Science Sun and Earth Science
Weather Phenomena•Precipitation •Cloud cover•Sea surface winds•Tropospheric winds•Hurricanes
Models
Heliosphere•Magnetic Field•Atmosphere•Plasma•Radiation•Solar Winds•Energetic Particle
Contribute to Natural or
Anthropogenic Disaster
Phenomena
Biosphere changes•Land cover Land use•Coastal zone erosions•Carbon cycle
Atmospheric chemistry•Pollutants•Transport phenomena•Aerosols
Climate system• Transient climate
– El Nino– Soil Moisture– Sea surface winds
• Long term – Radiation balance
Oceans•Productivity and ocean color•Carbon sequestering•Salinity•Sea Surface Temperature•Global water cycle
Solid Earth•Magnetic field•Gravity•Surface topography•Surface transformation
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Natural DisastersNatural Disasters
Direct
• Earthquakes• Volcanoes• Tsunamis• Hurricanes or Typhoons• Landslides• Floods• Tornadoes• Severe Weather• Drought• Fires• Dust Storms • High Winds
Indirect• Public Health• Air Quality• Invasive Species
Consequential
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Occurrence of Floods
Contaminated Fresh Water
Supply
Spread of Multiple
Infectious Diseases
Volcanic Eruption
Aerosol and Dust deposition and suspension
Breathing problems
Natural Disasters can Feed Other DisastersNatural Disasters can Feed Other Disasters
Air Quality
Public Health
Earthquake/Tsunami
FiresSevere
Weather Electric Grid
Outages Shutdown CityAnthropogenic or
Technological
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Preparedness to Prediction to PreventionPreparedness to Prediction to PreventionGovernmental Operational Agencies, NGOs
& Social Scientists
Science
Observations, Data Ingestion and Integration
Deploy Observing Systems
Disasterous Situation
No
Pro-active ApproachReactive Approach
Science
Models, Learning Tools and Intelligent Processing
Assist Decision Making Process
Situation understood and control measures in
place
Impact Mitigated
Generated Timely Alert
Models, Learning Tools and Intelligent Processing
Assist Decision Making Process
Yes
Governmental Operational Agencies & NGOs
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Venn Diagram of SensorVenn Diagram of SensorIn situAirborneSpace based
Remote Sensing TechnologiesPassive (UV, Visible, IR, Microwave)Active (Radar, SAR, Lidar) High temporal resolution
High spatial resolutionHigh spectral resolution
DSSConstellations
FormationsVirtual Platforms
Virtual Apertures
Clusters
Sensor Webs
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100 - 10,000 km
1 - 100 kmStudy Areas
~ 1 kmFlux Tower Sites
1 - 100 m Process Study PlotsValidation Sites
Validation Sites Process Study Plots
Satellites
Airplanes
Towers
In situ platforms
Why Multiple Sensors and Vantage Points Why Multiple Sensors and Vantage Points
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2-Year Prediction of Malaria Cases Based on Environmental Parameters (temperature, precipitation, humidity, vegetation index)5000
4000
3000
2000
1000
0
20001999199819971996199519941993199219911990
CASES
FITTED
CASES
PREDICTED
4000
3500
3000
2500
2000
1500
1000
500
0
Pf casesTemperature (deg C) x 100Rainfall (mm) x 5 + 1000
Tak Temperature--satelliteRainfall--satellitePf Parasite—field observations
Satellite Vegetation Data used for
Insecticide Planning
Mekong Malaria in Tak, ThailandMekong Malaria in Tak, Thailand
• Vector Habitat • Transmission • Risk Prediction
• Surface Hydrology • Climate Prediction
Satellite DataModels
Field data
Aqua
TRMM
TerraLandsat
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Near-Earth Space Weather EffectsNear-Earth Space Weather Effects
Ionosphere
MagnetosphereSolar Wind
ElectromagneticRadiation
Energetic Charged Particles
SOHO, ACE and EPRI Ground
Sensors
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Thermal Anomaly prior to Earthquake OccurrenceThermal Anomaly prior to Earthquake Occurrence
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Multi-Sensor Detection .. Thermal, Electric and magnetic fieldsMulti-Sensor Detection .. Thermal, Electric and magnetic fields
GPSGOES, MODIS, METEOSAT,
GMS, NOAA
DEMETER
9 - 12µ IRL2 L1
GPSRcvr
ULF, ELFMagnetometers
EQLights
Seismic Focus
High E-fieldGradients
IncreasedAir
Conductivity
Ionosonde
VLF, HF, UHFf0F2
TECChanges
SLHFRadon
Atmospheric Electrical Field
Heating
Ionosphere
Re: D. Ouzounov / NASA GSFC
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TOMS Aerosol Index - time series
“2001 Perfect Dust Storm”
Asian Dust & microbes? Long Range TransportAsian Dust & microbes? Long Range Transport
Sea of Japan4/9/2001Dust layer
NASA/GSFC4/14/2001
Dust layer
Dun-Huang, China
4/6/2001Dust layer
Source Regions
Dust Front
50 µm
Dust Particles
Lidar Profiling
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Floods – a serious threatFloods – a serious threat
D. Boyle/DRI
Aqua Terra
(NOAA)
(BOR)
(NASA) Regulating ReservoirsHydros
SMOS
Floods
Land Models
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Drought in MozambiqueDrought in Mozambique
Standardized Precip. Index
Satellite Sensor Observations/ Products
Terra/Aqua MODIS NDVI, Fire prod, H2O Vapor
TRMM Precip.Radar Precipitation Data
POES AVHRRAMSU-B
NDVI data from 1981Humidity fields
JASON-1, TOPEX/ Altimeter Reservoir and Lake levels
Model Organization Products
NCEP ETA model,NCEP/NCAR Reanalysis I/II
NCAR Predicted Precipitation,Winds, pressure, relative humidity
TRMM Multiple Precip. Analysis
NASA Tropical Precipitation
Rainfall Estimate, CMORPH
NOAA Precipitation over Africa, Central America, Asia
ENSO phase and predictions
IRI/Columbia University
SST anomalies and ENSO forecast
Re: June 9-15, 2005, Famine Early Warning System Network (FEWSNET)
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Dedicated Zonal ObserversDedicated Zonal Observers
Data production facility
Local wireless networksIndividual users
Transportation Industry
Other Pilots
Ultra Long Duration Balloons (ULDBs)40,000m for > 6 months
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Notional Integrated Observing SystemNotional Integrated Observing System
Data Assimilation& Forecast Models Data Warehouses
& Data Mining
CommunicationsFabric
SensorNodes
ComputingNodes
InformationStorageNodes
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SummarySummary
• Observing systems should be science driven --- closely coupled with natural disasters
• No single entity can take on this responsibility --- pooling of resources is critical
• Must capitalize on using existing sensors to solve critical societal problems
• Both observing platforms/sensors and models are integral to forming a sensor web
• Complex phenomena e.g., earthquakes, floods, drought and more can benefit from additional observations and assimilation techniques to further our understanding