Third International Conference on Early Warning Bonn, Germany March 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, Maryland Steve Ambrose NASA Headquarters Washington, DC 1
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Background Contents Science and Natural Disasters Observing Systems Applications Summary 3
What Drives Our Planet? F ( W / m 2 ) 2 1.4 ± 0.2 1 0-1 -2 0.7 ± 0.2 CO 2 CH 4 (indirect via O 3 and H 2 O) CFCs 0.35 ± 0.05 (indirect via stratospheric ozone) Greenhouse Gases 0.6 ± 0.2 Tropospheric Ozone N 2 O 0.15 ± 0.05 (nitrate) Sulfate + Nitrate Black Carbon 0.8 ± 0.4 (semi-direct, dirty cloud Organic Carbon Biomass Burning -0.2± 0.1-0.2± 0.2 Soil Dust -0.1± 0.2 Forced Land Cloud Cover ChangesAlterations -0.2± 0.2 0.4 ± 0.2 Volcanic Aerosols (range of Decadal mean) -1.1± 0.5-1.0 ± 0.4 &snow effects) (indirect via O 3 ) Tropospheric Aerosols Sun (0.2, - 0.5) Other Anthropogenic Forcings Natural Forcings Re: J. Hansen/NASA GISS 4
Sun and Earth Science 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 Contribute to Natural or Anthropogenic Disaster Phenomena Weather Phenomena Precipitation M Cloud o cover d Sea surface e winds Tropospheric l winds Hurricanes s Heliosphere Magnetic Field Atmosphere Plasma Radiation Solar Winds Energetic Particle Oceans Productivity and ocean color Carbon sequestering Salinity Sea Surface Temperature Global water cycle Solid Earth Magnetic field Gravity Surface topography Surface transformation 5
Natural Disasters Direct Earthquakes Volcanoes Tsunamis Hurricanes or Typhoons Landslides Floods Tornadoes Severe Weather Drought Fires Dust Storms High Winds Consequential Indirect Public Health Air Quality Invasive Species 6
Natural Disasters can Feed Other Disasters Earthquake/ Tsunami Occurrence of Floods Contaminated Fresh Water Supply Spread of Multiple Infectious Diseases Volcanic Eruption Aerosol and Dust deposition and suspension Breathing problems Severe Weather Fires Anthropogenic or Technological Air Quality Electric Grid Outages Shutdown City Public Health 7
Preparedness to Prediction to Prevention Governmental Operational Agencies, NGOs & Social Scientists Science Observations, Data Ingestion and Integration Models, Learning Tools and Intelligent Processing Assist Decision Making Process Pro-active Approach Impact Mitigated Generated Timely Alert Yes Situation understood and control measures in place Governmental Operational Agencies & NGOs No Assist Decision Making Process Models, Learning Tools and Intelligent Processing Deploy Observing Systems Disasterous Situation Reactive Approach Science 8
Venn Diagram of Sensor In situ Airborne Space based Remote Sensing Technologies Passive (UV, Visible, IR, Microwave) Active (Radar, SAR, Lidar) High temporal resolution High spatial resolution High spectral resolution Formations Sensor Webs DSS Constellations Clusters Virtual Platforms Virtual Apertures 9
Why Multiple Sensors and Vantage Points Satellites 100-10,000 km Airplanes 1-100 km Study Areas Towers ~ 1 km Flux Tower Sites In situ platforms 1-100 m Process Study Plots Validation Sites Validation Sites Process Study Plots 10
Mekong Malaria in Tak, Thailand Aqua Landsat Terra 4000 3500 3000 2500 2000 Tak Pf cases Temperature (deg C) x 100 Rainfall (mm) x 5 + 1000 Temperature--satellite Rainfall--satellite Pf Parasite field observations TRMM 1500 1000 500 0 5000 4000 2-Year Prediction of Malaria Cases Based on Environmental Parameters (temperature, precipitation, humidity, vegetation index) CASES Satellite Data Models CASES Satellite Vegetation Data used for Insecticide Planning Field data 3000 FITTED PREDICTED Surface Hydrology Climate Prediction 2000 1000 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Vector Habitat Transmission Risk Prediction 11
Near-Earth Space Weather Effects Solar Wind Electromagnetic Radiation Magnetosphere SOHO, ACE and EPRI Ground Sensors Energetic Charged Particles Ionosphere 12
Thermal Anomaly prior to Earthquake Occurrence 13
Multi-Sensor Detection.. Thermal, Electric and magnetic fields GOES, MODIS, METEOSAT, GMS, NOAA GPS 9-12µ IR DEMETER L2 L1 TEC Changes Atmospheric Electrical Field Ionosphere VLF, HF, UHF High E-field Gradients EQ Increased Lights Air Conductivity Radon Heating SLHF GPS Rcvr f0f2 Ionosonde ULF, ELF Magnetometers Seismic Focus Re: D. Ouzounov / NASA GSFC 14
Source Regions Asian Dust & microbes? Long Range Transport Dust Front 2001 Perfect Dust Storm Dust Particles 50 µm TOMS Aerosol Index - time series Dun-Huang, China 4/6/2001 Dust layer Sea of Japan 4/9/2001 Dust layer Dust layer NASA/GSFC 4/14/2001 Lidar Profiling 15
Floods a serious threat Aqua Terra (NOAA) (BOR) SMOS Land Models (NASA) Floods Hydros Regulating Reservoirs D. Boyle/DRI 16
Drought in Mozambique Satellite Sensor Observations/ Products Terra/Aqua MODIS NDVI, Fire prod, H 2 O Vapor TRMM Precip.Radar Precipitation Data POES AVHRR AMSU-B NDVI data from 1981 Humidity fields JASON-1, TOPEX/ Altimeter Reservoir and Lake levels Model Organization Products Standardized Precip. Index 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) 17
Dedicated Zonal Observers Other Pilots Data production facility Ultra Long Duration Balloons (ULDBs) 40,000m for > 6 months Transportation Industry Local wireless networks Individual users 18
Notional Integrated Observing System Sensor Nodes Communications Fabric Computing Nodes Information Storage Nodes Data Assimilation & Forecast Models Data Warehouses & Data Mining 19
Summary 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 20