2. Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of remote sensing techniques – that is, measurements of atmospheric properties and processes at a distance, using radiation sensors placed in space, on aircraft, and/or on the earth surface.” Grant W. Petty

3. Satellite images Cloud streetCloud bands Storm Hurricane Because of satellites we have seen many phenomena that we were unable to see before, in particular over ocean!

4. Geostationary vs. Polar-orbiting Two major different types of satellites

5. GOES SATELLITES (8 and 10) It is difficult to get data beyond 70 o north and south.

6. POLAR-ORBITING SATELLITES An inclination of 90 o Sun-Synchronous Orbit The orbit is a special case of polar orbiting (Satellites passing the same area approximately at the same local times every day) - Twice a day - To eliminate diurnal signal in data

7. POLAR-ORBITING SATELLITES Ascending Descending Denser data over polar regions!

8. POLAR-ORBITING SATELLITES gap

9. Satellite Temporal resolution Spatial resolution GeostationaryVery high Depending on: Frequency, satellite height, and the size of antenna Polar orbiting Usually twice a day The height of satellite orbit above the Earth Geostationary~ 35,000 - 40,000 km Polar orbiting~ 700 - 800 km Resolutions

10. Electromagnetic Spectrum

11. Spectral Ranges of Atmospheric Radiation

12. Absorption of the Atmosphere The absorption of the atmosphere as a function of wavelength (microns). 100 means atmosphere totally opaque, 0 means atmosphere totally transparent

13. Visible channel  Satellite receivers receive the reflectance of sunlight.  Strong reflection - cloud and snow  Brighter - thicker cloud  No data during the night time. Sun glint (also 3.9 um – short IR)

14. Short InfraRed A window region to the atmosphere

15. Thermal InfraRed (IR)  Detect the temperature (i.e., energy) of the various surface and cloud features visible from space.  Used to estimate the height of cloud tops (problem with cirrus clouds)  Not a perfect estimation.

16. GOES Visible and IR images IR VIS Detecting cloud top (the higher (colder) the cloud top, the brighter the image) Detecting cloud depth Low level clouds Deep clouds

17. GOES Visible and IR images IR VIS

18. GOES Visible and IR images Enhanced IR VIS

19. Deep cloud? ? ?

20. More satellites and Products

21. SSM/I  Special Sensor Microwave/Imager  Sun-synchronous  Mean altitude : 830 km  Width of swath : 1400 km  4 frequencies, 7 channels  19.3, 22.2, 37.0 and 85.5 GHz  22.2 GHz received in vertical polarization, the remaining frequencies received in dual polarization (19V, 19H, 22V, 37V, 37H, 85V, and 85H)  Resolutions  25 Km : 19V, 19H, 22V, 37V, 37H  12.5 Km : 85V, 85 H  Data over cloud region can be retrieved, but not over land and heavy rainfall areas (a problem of microwave).  Retrieved data  Integrated total precipitable water (TPW)  Wind speed at 10-m height

22. QuickSCAT  SeaWinds instrument, a microwave radar, aboard the QuikSCAT measuring sea surface wind vectors.  Sun-synchronous  Max altitude : 800 km  Width of swath : 1800 km  Resolutions – 25 km  Data over cloud region can still be retrieved, but not over land and heavy rainfall areas.  Measure wind vectors at 10-m height  Information saturated above 30 m/s (so no data can be detected above 30 m/s), same for SSMI

23. Satellite winds at 10 m height QuikSCAT SSM/I Wind overestimated Data available over ocean only!!!

24. Latin for Launched 2000 Launched 2002 Latin for water NASA's Earth Observing System satellites Launched 2004 Latin for air land

25. Launched July, 2004 Courtesy NASA

26. MODIS  Moderate Resolution Imaging Spectroradiometer  Aboard Terra (2000) and Aqua (2002)  36 spectral bands: 0.405-14.387 μm  Resolutions: 250m, 500m, 1000m (radiances)  Sun-synchronous  Mean altitude : 705 km (equator)  Width of swath : 2300 km (Terra), 2330 (Aqua)

27. MODIS IR TPW Derived from bands 24 to 36 (between 4.47 to 14.24 μm), excluding band 26 (data available day and night). Used a statistical regression algorithm, with an option of a subsequent non- linear physical retrieval (Seemann et al. 2003) => 101-level atmospheric T, dew points, O3 profiles, and skin T 101-level vertical T and dew point profiles = > TPW InfraRed (IR) – 5 km resolution

28. MODIS nIR TPW near InfraRed (nIR) – 1 km resolution Used 2 water vapor absorption bands, 0.905 and 0.94 μm and 3 water vapor window bands, 0.865, 0.936, and 1.24 μm. Used a ratio of reflected solar radiances between an absorption channel to a window channel => water vapor transmittance A pre-calculated water vapor transmittance look-up table = > TPW The quality relies on water vapor attenuation of nIR solar radiation reflected by surfaces and clouds. (daytime only)

29. MODIS nIR TPW near InfraRed (nIR) – 1 km resolution Over ocean, only sun glint regions can have data. The accuracy of the data is strongly related to the estimation of surface reflection. (=>larger error over ocean)

30. Satellite derived winds Vapor Cloud Problem: The estimation of the height

31. Satellite derived TPW and SST SSMI, column integrated water vapor TMI SST Cold SST after Katrina passed Katrina

32. Dust, Aerosol

33. LOW EARTH ORBIT (LEO) RECEIVER Radio occultation geometry COSMIC project launched 6 LEOs in 2005 Taiwan was the primary sponsor. Data are not contaminated by heavy precipitation.

34. LOW EARTH ORBIT RECEIVER Ray distribution in the occultation plan

35. LOW EARTH ORBIT RECEIVER

36. Observations  When using satellite data, need to know What kinds of data? The frequency of data The resolution of data The quality (or error) of data  What is the advantages and disadvantages of in situ and remote sensing data?