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)