Applications and Limitations of Satellite Data

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 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

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 • 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 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

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 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

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 Ts: SST Tb: Satellite measured brightness temperature For Ts=300 K and ε=0.5, we have Tb=150K and 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

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: αsat > αth; Tsat < Tth Threshold albedo (αth) and brightness temperature (Tth) 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 CloudsUpper 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 μm)