1. Remote Sensing
Satellite Systems

2. Satellite Remote Sensing
Lasted just 78 days but proved the theory…
TIROS-1 was an aluminum-and-stainless-steel drum measuring 42 inches in diameter, 19 inches high and weighing 270 pounds. An array of 9,200 solar cells powered its two TV cameras: one high-res, one low-res. One antenna received control signals from ground stations, and another four transmitted TV images back to Earth. Two video recorders stored images when the satellite was out of range of ground stations.
The polar-orbiting craft was not constantly pointed at earth and could only operate in daylight, so coverage was not continuous. It functioned for just 78 days, but it sent back thousands of pictures of cloud patterns forming and moving across the face of the planet. And it proved the theory that satellites could effectively survey global weather from space.
The April 1st launch was no joke…
Cartooning Images at the start of my career in 1976…

3. How is satellite data used?
To monitor cloud systems in real time
To infer atmospheric structure from cloud types
As a tool in short range forecasting
Some satellite data is assimilated into model fields
Numerous derived products
precipitation estimates
sea surface temperature
atmospheric motion vectors
Moisture
Icing
Turbulence
etc
The Analysis and Diagnosis of the Satellite
Atmosphere is the next best things to real atmosphere

4. Objectives Satellite systems Radiation principles Sensing channels
Image enhancement
Sounding output
NWP ingesting satellite data

5. Satellite Systems Geostationary and Polar Orbiting
Imaging and sounding instruments
Characteristics
Advantages
Limitations

6. Geostationary Polar Animation of Orbits Current GOES Status
Parked over the equator
Eclipses in spring and fall
Orbit is earth synchronous (24 hour orbit)
Orbit roughly over the poles
Sun synchronous – no eclipse
14 orbits/day
Animation of Orbits
Current GOES Status
Current NOAA POES Status

7. Geostationary Met Satellites around the world
Examples other geostationary met satellites
Europe (Meteosat)
India (KALPANA formerly INSAT)
Japan (MTSAT)
China (FY2C-Feng Yun 2)

8. MetOp-A
European Space Agency
Click Here

9. Earth Observing Systems - EOS
MODIS – NPOES

10. GOES Scanning Strategy
Back and forth sweeps
15 minute global scan

11. POES – NOAA Series
Orbital and sensor characteristics

12. Imaging and vertical sounders
GOES
GOES imager- 5 channels, VIS – IR
GOES sounder – 18 thermal channels, one low-res visible channel ~ 50 km
POES
POES imager (AVHRR) - 6 channels
POES sounder (ATOVS)
HIRS (IR) – similar to GOES IR sounder
AMSU (microwave), AMSU-A 15 channels, AMSU-B 5 channels – in-cloud soundings possible

13. GOES and POES Communication Paths
GOES Satellite -> Wallops Island, West Virginia for some data processing ->then back to satellite -> ground stations
POES Satellite directly to ground station
Line of sight reception for 2-16 minutes

14. Image Resolution GOES: Wavelength and latitude dependant
At equator: VIS=1km, Shortwave and IR=4km, WV=8km
POES: 1.09 km at all wavelengths and latitudes

15. Factors affecting image interpretation
Sensor resolution
Coarser resolution of GOES sensors can lead to cloud image “merging” relative to a higher resolution image from POES
Curvature of earth
Except near the equator, slant angle of GOES scans can lead to parallax views, an effect absent from POES

16. RESOLUTION
The smallest area that the satellite sensor (radiometer) can distinguish from the surrounding area

17. Image size and image resolution
The smallest area that the satellite sensor (radiometer) can distinguish from the surrounding area
Enlarging an image only
makes larger pixels

18. GOES Resolution from subpoint4

19. Image resolution depends on:
closeness of satellite to earth
distance of pixel from satellite subpoint
satellite optics
satellite sensor resolution
image processing techniques
quality of final display device

20. Image resolution (km)

21. GOES vs POES Advantages and Disadvantages
One Sensor – preserves relativity
Multiple Sensors – relativity is less certain
GOES vs POES Advantages and Disadvantages

22. Radiation Principles

23. Radiation Principles
50% of incoming solar reaches the surface

24. Atmospheric Absorption
Window with Nil Absorption 4u
Window

25. Weighting Functions
Pick and Choose wavelengths to take advantage of atmospheric components and absorption bands and windows - complex
Distribution
Dominate Source of Radiation to Satellite
Transmittance is the fraction of incident light at a specified wavelength that passes through a sample
Absorbance is the fraction of incident light that neither transmits nor reflects and is proportional to the concentration of a substance in a solution

26. Imager Channels
Type Ident Satellite Wavelength

27. Limitations and errors
Earth and clouds are not perfect black bodies – thin clouds may allow considerable radiation to pass through (upwelling), so satellite-derived brightness temperatures may be in error
Other atmospheric constituents are not black bodies, but absorb/emit radiation at preferred wavelengths (e.g. water vapour at ~ 6 microns)
Must use “window channel” radiation for Tb determinations (e.g microns)

28. Limitation - Subpixel Effect
Cloud elements smaller that the resolution cannot be resolved but still contribute to the radiation
Different wavelengths are also affected differently due to “dirty” vs clean windows and emissivity
Cities as subpixel hot spots

29. Subpixel Example

30. The Satellite Channels

31. Colour-enhanced visible GOES 12 image: T.S. Gustav 29 August 2008
Visible Imagery
Colour-enhanced visible GOES 12 image: T.S. Gustav 29 August 2008

32. Albedo by Wavelength VIS GOES Channel 1 VIS AVHRR Channel 1 520-720nm1u
The visible channel (channel 1) is a collection of reflected light in the mm wavelength. These images are obtained only during daylight hours. They are used to show clouds,
haze, severe storms, snow cover, volcanic activity, and other visible features. Many surface features are also apparent.
The infrared (IR) imagery represents the measurement of energy emitted by the Earth in a variety of wavelengths, the above image is a channel 2 image from the mm wavelength. This short wave IR is helpful in observing ground fog, fires, volcanoes, sea surface temperatures, and clouds. Scientists access other IR images depending upon their interest. A long wave IR, channel 4 ( mm) is used to show jet stream features, surface temperatures and frost/freeze forecasts. Channel 5 ( mm) is commonly used to observe daily temperature changes, cold cloud tops, dust, and ash. The water vapor imagery, channel 3, ( mm) is an infrared image which allows meteorologists to observe upper-level moisture sources and the presence of humidity within the atmosphere. This will enhance their ability to forecast the development and motion of weather systems.
Contrast
VIS AVHRR Channel 2 nm

33. Near Infrared (1 micron)
POES
Similar to visible
Most radiation is reflected solar with a small IR contribution
Good for land/water differentiation since vegetation is a better scatterer of 1u radiation
No use at night like visible
Sun
Glint
What time of day is it?

34. 4 Micron (4u) Thermal Radiation << than 11u
Not much radiation – both solar and thermal at 4 micron
Not much atmospheric absorption in 4 micron window though

35. Solar Dominates Thermal
Solar Pollution

36. 4 Micron (4u) GOES and POES Considered an IR channel
low radiation mapped to white
high mapped to black
Contribution from both albedo and temperature
Solar pollution
Noisy signal at cold temperatures

37. 4 Micron (4u) Daytime interpretation Water cloud is dark
Cloud droplets are more reflective to solar portion of 4u signal – lots of radiation = dark thermal representation
Ice cloud is bright – Why?
Fog/stratus is dark over bright snow
Mid cloud (ice/water mix) is mottled
Nighttime interpretation
Thermal channel
Noisy at cold temperatures (cold cloud tops)

38. Water Vapour Imagery
Water vapour is a strong absorber-emitter – upper 3 mm of water vapour
Water vapour distribution varies greatly in the horizontal and vertical = weather!
This is why Water Vapour imagery should be your new best friend…

39. Water Vapour (6.7 micron) Displays high and mid level moisture
No need for cloud
White = moist & cold = low flux
Black =dry & warm = high flux
Coarse 8 km resolution but signal very clean - not noisy
Only on GOES
Animation displays circulation patterns
Hurricane Edouard

40. GOES Limb Darkening
Limb
NADIR
The point is that one senses the 3 mm of water vapour at a higher and colder level.

41. Infrared (11 micron) Displays temperature White = low flux = cold
Used day or night
Thin cloud may be contaminated from upwelling radiation from below
Stratus/fog poorly displayed
Usually requires enhancement

42. Infrared (11 micron) In atmospheric absorption “window”
Absorption/emissivity near 1
(except for thin cirrus)
Radiative temperature is close to the effective blackbody temperature
Thin Cirrus E<1 =.4
CB E=1
The point is that the thin cirrus is as cold as the thick cirrus (blue-green) but
Upwelling radiation contaminates the observation (yellow)

43. Infrared (12 micron) The Dirty Window
Looks almost like the 11u
Displays temperature
White = low flux = cold
Used day or/and night
More sensitive to low level water vapour than the 11u
Used for SST’s
Derived products like Tsfc and Precipitable Water
Volcanic Ash

44. Infrared (12 micron) The Dirty Window
More
Sensitive to low level water vapour than the 11 micron

45. Image Enhancement
The process whereby meteorologically significant parts of an image are made easier to discriminate. Enhancement involves reassigning new color or brightness values to a specific range of pixel elements.

46. Enhancement Curves
Increased low light
Linear

47. Visible image enhancement
This is my work from the early 1980’s.
The image on the left has been enhanced to increase its brightness

48. Enhanced Visible Imagery
Correction to visible imagery to enhance areas with low sun angle
Correction across image near terminator
Correction both diurnally and seasonally
“Black step” for high albedo clouds – possible precipitation
Effort from 1980 … never published
E series of visible enhancements

49. Chadwick family of enhancement curves named by seasons and purpose…
IR image enhancement
Chadwick family of enhancement curves named by seasons and purpose…
the IR image on the right has been enhanced to bring out the high (cold) tops west of James Bay

50. Q6 Curve on IR Imagery White Black linear
Uses a 'range' of temperature to discriminate
features (e.g. low, mid, high, convective cloud etc.)
linear
Black

51. Color Enhancement
In this 11 micron GOES-8 image of hurricane Fran, the coldest tops are colored in red so that they stand out more clearly.

52. More Satellite Data for NWP
Temperature and moisture (Sounding data)
Drift Winds – both direction and speed
Southern hemisphere benefits!

53. Questions? Summary Satellite systems Radiation principles
Future satellites will have even greater capabilities
Spatial and temporal resolution will be amazing!
Satellite systems
Radiation principles
Sensing channels
Image enhancement
Sounding output
Questions?