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

S. Habib/EWC III-rev1.ppt (3/27/06)2

S. Habib/EWC III-rev1.ppt (3/27/06)3

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