1. Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University

2. Why Satellite Observation?  Other than cloud images, why do we need satellite data for regional weather and climate studies in Taiwan?

3. 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.?

4. Some Examples for Application of Satellite Data  Model Initialization/Assimilation/Reanalysis  Validation  Improvements on model physics

5. Model: Initialization/ Assimilation/Reanalysis  Initialization for weather forecast  Assimilation  Reanalysis (model + satellite observation) Accurate and long-term Description of the earth-atmosphere system.

6. 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

7. 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.

8. Satellite Retrievals  Solar Spectral Channels  Thermal Infrared Channels  Microwave Channels

9. Solar Spectral Channels  Measurement of reflection at narrow channels  Lack of vertical information

11. Information 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)

13. Information Derived (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/

14. Thermal Infrared Channels  Rationale: emission and absorption of thermal IR

16. 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

17. 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

18. Microwave Channels  Emission and absorption in microwave spectrum  Long wavelength  Capable of penetrating through clouds

19. 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

20. Information Derived (Continued)  Precipitation  Multiple channels  Polarization (particle size)  Long wavelength; sensitive to large particles  Vertical distribution of precipitation

21. SST Retrievals  IR Technique  Microwave Technique

22. 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

23. Microwave Technique  Single microwave channel  Unaffected by clouds and water vapor  Rain (?)  Sub-surface temperature (?)

24. 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

25. 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

26. 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.

27. 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.

28. 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)