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Applications and Limitations of Satellite Data. Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University. Why Satellite Observation?. Other than cloud images, why do we need satellite data for regional weather and climate studies in Taiwan?. - PowerPoint PPT Presentation
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Applications and Limitations Applications and Limitations of Satellite Dataof Satellite Data
Professor Ming-Dah ChouProfessor Ming-Dah Chou
January 3, 2005January 3, 2005Department of Atmospheric SciencesDepartment of Atmospheric Sciences
National Taiwan UniversityNational Taiwan University
Why Satellite Observation?Why Satellite Observation? Other than cloud images, why do we
need satellite data for regional weather and climate studies in Taiwan?
A short answer is…A short answer is… For extended weather and climate
forecasts, large-scale circulations and physical environment (e.g. SST, snow/ice cover) become very important. Large-scale circulations and physical environment can be best observed from satellite.?
Some Examples for Some Examples for Application of Satellite DataApplication of Satellite Data
Model Initialization/Assimilation/Reanalysis
Validation Improvements on model physics
Model:Model:Initialization/ Assimilation/ReanalysisInitialization/ Assimilation/Reanalysis
Initialization for weather forecast Assimilation Reanalysis (model + satellite observation) Accurate and long-term Description of the earth-atmosphere system.
Validation of weather forecast and Validation of weather forecast and climate simulationsclimate simulations
What parameters? Diagnostic
Prognostic Clouds Radiative heat budgets Cloud radiative forcing
Temperature Humidity SST Ice and snow cover Others
Model improvementModel improvement Interaction between dynamical and physical
processes (intra-seasonal and inter-annual variations)
Tropical disturbances and air-sea interaction (momentum and heat fluxes)
Interaction between monsoon dynamics, precipitation, and radiation.
Satellite RetrievalsSatellite Retrievals Solar Spectral Channels Thermal Infrared Channels Microwave Channels
Solar Spectral ChannelsSolar Spectral Channels Measurement of reflection at narrow channels Lack of vertical information
Information DerivedInformation Derived Clouds
Aerosols
Fractional cover (visible channel) Article size (multiple channels) Cloud water amount (multiple channels)
Cloud contamination problem especially thin cirrus clouds. Mostly over oceans. Large uncertainty over land especially over deserts Optical thickness; spectral variation (multiple channels) Single scattering albedo (large uncertainty) Asymmetry factor (large uncertainty)
Information Derived Information Derived (Continued)(Continued) Ozone
Land reflectivity
Vegetation cover
Ice/snow cover
Total ozone amount (multiple channels)
Spectral variation
NDVI (Normalized Difference Vegetation Index); Reflection (albedo) difference of two channels Sudden albedo jump across green light
Cloud contamination problem Multiple channels to differentiate clouds and ice/
Thermal Infrared ChannelsThermal Infrared Channels Rationale: emission and absorption of thermal IR
Information DerivedInformation Derived
Temperature profile
Water vapor profile
Multiple channels in the CO2 absorption band Uniform CO2 concentration Weighting functions peak at different heights
Multiple channels in the H2O absorption band Coupled with temperature retrievals Low vertical resolution Broad weighting function
Information Derived Information Derived (Continued)(Continued) CloudsClouds
Fractional cover
Cloud height
Particle size
Cloud water amount
Cloud-surface temperature contrast High spatial resolution Window channel
Opaque clouds in thermal IR Emission at cloud top
Unreliable
Unreliable
Microwave ChannelsMicrowave Channels Emission and absorption in microwave
spectrum Long wavelength Capable of penetrating through clouds
Information DerivedInformation Derived Temperature profile
Water vapor profile
Multiple channels in an absorption line Uniform CO2 concentration Weighting functions peak at different heights
Multiple channels in a H2O absorption line Coupled with temperature retrievals Low vertical resolution Broad weighting function
Information Derived Information Derived (Continued)(Continued)
Precipitation Multiple channels Polarization (particle size) Long wavelength; sensitive to large particles Vertical distribution of precipitation
SST RetrievalsSST Retrievals
IR Technique Microwave Technique
IR TechniqueIR Technique Three IR window channels (3.7, 10, and 11 μm) Differential water vapor absorption Regression Satellite measurements vs buoy measurements Sub-surface temperature Clear sky only NOAA/AVHRR, NASA/MODIS NOAA NCEP claims SST retrieval accuracy is ~0.2-0.3 C
Microwave TechniqueMicrowave Technique
Single microwave channel Unaffected by clouds and water vapor Rain (?) Sub-surface temperature (?)
Microwave Technique (Cont.)Microwave Technique (Cont.)
2b
sT
T
bs TT ε: estimated from surface windTs: SSTTb: Satellite measured brightness temperature
For Ts=300 K and ε=0.5, we have Tb=150K andIf ∆ε=0.001, ∆Ts=0.6 K……VERY SENSITIVE!
600sT
Bias among MODIS-, AVHRR-, and TRMM-derived SST is large, reaching 0.5-1.0 °C
Clouds RetrievalClouds Retrieval Day: Use both solar and thermal IR channels Night: Use only thermal IR channels High spatial resolution of satellite measurements A field-of-view picture element (pixel) is either totally c
loud covered or totally cloud free Cloud detection: αsat > αth; Tsat < Tth
Threshold albedo (αth) and brightness temperature (Tt
h) are empirically determined
Clouds Retrieval (cont.)Clouds Retrieval (cont.) Zonally-averaged cloud cover of NASA/ISCCP, NAS
A/MODIS, and NOAA/NESDIS could differ by 30-40% Uncertainties of cloud optical thickness, particle s
ize and water content are even larger than that of cloud cover
Regardless of the large uncertainties of cloud retrievals, global cloud data sets could be useful depending on applications.
AerosolsAerosols Various sources/types of aerosols: Fossil fuel combustions, dust, smoke, sea salt Large temporal and regional variations Short life time, ~10 days Difficult to differentiate between aerosols and thin cirrus Difficult to retrieve aerosol properties over land high surface albedo Differences between various data sets of satellite-retrieved, as
well as model-calculated aerosol optical thickness are large. Impact of aerosols on thermal IR is neglected. Potentially, aerosols could have a large impact on regional an
d global climate.
Thin Cirrus CloudsThin Cirrus CloudsUpper Tropospheric Water VaporUpper Tropospheric Water Vapor Climatically very important Thin cirrus clouds are wide spread, but too thin to be reliably detec
ted Upper tropospheric water vapor is too small to be reliably retrieve
d Thin cirrus clouds:
Upper tropospheric water vapor
Although difficult to retrieve from satellite measurements, there are no other alternatives.
Key to understand feedback mechanisms in climate change studies.
Weak absorption visible channel (0.55 μm) Strong absorption near-IR channel (1.36 μm)
Strong absorption water vapor channel (6.3 μm)