Presentation on theme: "Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University."— Presentation transcript:
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. Large- scale 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 Prognostic Clouds Radiative heat budgets Cloud radiative forcing 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.
Information Derived Temperature profile Water vapor profile Multiple channels in the CO 2 absorption band Uniform CO 2 concentration Weighting functions peak at different heights Multiple channels in the H 2 O absorption band Coupled with temperature retrievals Low vertical resolution Broad weighting function
Information Derived (Continued) Clouds 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
Microwave Channels Emission and absorption in microwave spectrum Long wavelength Capable of penetrating through clouds
Information Derived Temperature profile Water vapor profile Multiple channels in an absorption line Uniform CO 2 concentration Weighting functions peak at different heights Multiple channels in a H 2 O 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 Three IR window channels (3.7, 10, and 11 μ m) 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 Single microwave channel Unaffected by clouds and water vapor Rain (?) Sub-surface temperature (?)
Microwave Technique (Cont.) ε: estimated from surface wind T s : SST T b : Satellite measured brightness temperature For Ts=300 K and ε=0.5, we have T b =150K and If ∆ε=0.001, ∆T s =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: α sat > α th ;T sat < T th Threshold albedo ( α th ) and brightness temperature (T th ) 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 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 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: 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 μ m)