Transcript Advanced Remote Sensing

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?
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. Largescale circulations and physical
environment can be best observed from
satellite.?
Some Examples for
Application of Satellite Data
 Model
Initialization/Assimilation/Reanalysis
 Validation
 Improvements on model physics
Model:
Initialization/ 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
climate simulations
 What parameters?
 Diagnostic
 Clouds
 Radiative heat budgets
 Cloud radiative forcing
 Prognostic
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Temperature
Humidity
SST
Ice and snow cover
Others
Model 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 Retrievals
 Solar Spectral Channels
 Thermal Infrared Channels
 Microwave Channels
Solar Spectral Channels
 Measurement of reflection at narrow channels
 Lack of vertical information
Information Derived
 Clouds
 Fractional cover (visible channel)
 Article size (multiple channels)
 Cloud water amount (multiple channels)
 Aerosols
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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 (Continued)
 Ozone
 Total ozone amount (multiple channels)
 Land reflectivity
 Spectral variation
 Vegetation cover
 NDVI (Normalized Difference Vegetation Index);
 Reflection (albedo) difference of two channels
 Sudden albedo jump across green light
 Ice/snow cover
 Cloud contamination problem
 Multiple channels to differentiate clouds and ice/
Thermal Infrared Channels
 Rationale: emission and absorption of thermal IR
Information Derived
 Temperature profile
 Multiple channels in the CO2 absorption band
 Uniform CO2 concentration
 Weighting functions peak at different heights
 Water vapor profile
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Multiple channels in the H2O absorption band
Coupled with temperature retrievals
Low vertical resolution
Broad weighting function
Information Derived (Continued)
 Clouds
 Fractional cover
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Cloud-surface temperature contrast
High spatial resolution
Window channel
 Cloud height
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Opaque clouds in thermal IR
Emission at cloud top
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Unreliable
 Particle size
 Cloud water amount
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Unreliable
Microwave Channels
 Emission and absorption in microwave
spectrum
 Long wavelength
 Capable of penetrating through clouds
Information Derived
 Temperature profile
 Multiple channels in an absorption line
 Uniform CO2 concentration
 Weighting functions peak at different heights
 Water vapor profile
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Multiple channels in a H2O absorption line
Coupled with temperature retrievals
Low vertical resolution
Broad weighting function
Information Derived (Continued)
 Precipitation
 Multiple channels
 Polarization (particle size)
 Long wavelength; sensitive to large particles
 Vertical distribution of precipitation
SST Retrievals
 IR Technique
 Microwave Technique
IR Technique
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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 Technique
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Single microwave channel
Unaffected by clouds and water vapor
Rain (?)
Sub-surface temperature (?)
Microwave Technique (Cont.)
 Ts  Tb
ε: estimated from surface wind
Ts: SST
Tb: Satellite measured brightness temperature
T 
Ts   b2 
 
For Ts=300 K and ε=0.5, we have Tb=150K and Ts  600 
If ∆ε=0.001, ∆Ts=0.6 K……VERY SENSITIVE!
 Bias among MODIS-, AVHRR-, and TRMM-derived SST is
large, reaching 0.5-1.0 °C
Clouds 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 cloud covered or totally cloud free
 Cloud detection:
αsat > αth;
Tsat < Tth
Threshold albedo (αth) and brightness
temperature (Tth) are empirically determined
Clouds Retrieval (cont.)
 Zonally-averaged cloud cover of NASA/ISCCP,
NASA/MODIS, and NOAA/NESDIS could differ
by 30-40%
 Uncertainties of cloud optical thickness,
particle size 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.
Aerosols
 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 satelliteretrieved, 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 and global climate.
Thin Cirrus Clouds
Upper Tropospheric Water Vapor
 Climatically very important
 Thin cirrus clouds are wide spread, but too thin to be reliably
detected
 Upper tropospheric water vapor is too small to be reliably
retrieved
 Thin cirrus clouds:
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Weak absorption visible channel (0.55 μm)
Strong absorption near-IR channel (1.36 μm)
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Strong absorption water vapor channel (6.3 μm)
 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.