1. In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven useful in monitoring and predicting severe weather such as tornadic outbreaks, tropical cyclones, and flash floods in the short term, and climate trends indicated by sea surface temperatures, biomass burning, and cloud cover in the longer term. This has become possible first with the visible and infrared window imagery of the 1970s and has been augmented with the temperature and moisture sounding capability of the 1980s. The imagery from the NOAA satellites, especially the time continuous observations from geostationary instruments, dramatically enhanced our ability to understand atmospheric cloud motions and to predict severe thunderstorms. These data were almost immediately incorporated into operational procedures. Use of sounder data in the operational weather systems is more recently coming of age. The polar orbiting sounders are filling important data voids at synoptic scales. Applications include temperature and moisture analyses for weather prediction, analysis of atmospheric stability, estimation of tropical cyclone intensity and position, and global analyses of clouds. The Advanced TIROS Operational Vertical Sounder (ATOVS) includes both infrared and microwave observations with the latter helping considerably to alleviate the influence of clouds for all weather soundings. The Geostationary Operational Environmental Satellite (GOES) Imager and Sounder have been used to develop procedures for retrieving atmospheric temperature, moisture, and wind at hourly intervals in the northern hemisphere. Temporal and spatial changes in atmospheric moisture and stability are improving severe storm warnings. Atmospheric flow fields are helping to improve hurricane trajectory forecasting. Applications of these NOAA data also extend to the climate programs; archives from the last fifteen years offer important information about the effects of aerosols and greenhouse gases and possible trends in global temperature.
This talk will indicate the present capabilities and foreshadow some of the developments anticipated in the next twenty years.
Investigations with High Spectral Resolution Data from AIRS Lectures in Maratea 22 – 31 May Paul Menzel NOAA/NESDIS/ORA

2. AIRS 2378
IASI 8461
HIRS 19
CrIS 1400

3. Moisture Weighting Functions
These water vapor weighting functions reflect the radiance sensitivity of the specific channels to a water vapor % change at a specific level (equivalent to dR/dlnq scaled by dlnp).
Moisture
Weighting
Functions
Pressure
Weighting Function Amplitude
Wavenumber (cm-1)
UW/CIMSS
The advanced sounder has more and sharper weighting functions

4. AIRS radiance changes (in deg K) to atm & sfc changes

5. AIRS Spectra from around the Globe
20-July-2002 Ascending LW_Window
This shows the ascending data (day-time) for the current AIRS “focus day” of 20 July The image is of a longwave window channel brightness temperature (using a GOES colormap), along with various sample spectra.
We’ve had global observations of high spectral resolution before (IRIS and IMG), but they were not really atmospheric profile sounders. The AIRS data is very clean, and the applications are numerous. This is what many of us have been waiting for, and working towards for many years. There are many un-tapped opportunities: SO2 detection from volcanoes, the blue-spike fire detection, new cloud detection techniques, low level inversion detection for severe weather, new spectroscopy investigations, climate change signatures, …

6. AIRS over Europe on 6 Sep 02

7. RTE for IR measurements

8. A correct description of the error structure in the IR spectral measurements is important for the solution of the atmospheric remote sensing inverse problem because:
* errors, reinforced by the instability of the inverse problem, can drastically reduce the accuracy of the retrievals;
* first guess moisture-temperature profiles are already quite accurate thus AIRS measurements must be of high quality to add new information; thus to make satellite soundings effective, measurement error structures have to be properly described;
* to improve the relative signal to noise ratio of the AIRS measurements to the existing background first guess, the spatial structure of the measurements errors has to be correctly modeled, and measurements errors have to be filtered.

9. Spectral distribution of spatial smoothing filter (half-size given in pixel number)

10. Diagnostic tests for new data set
Spatial variability of radiances
Is upper atmosphere smoother than lower atmosphere?
Spectral variability of radiances
Are SW and LW channels with similar weighting
functions showing similar scenes?
(3) Noise of radiances
Can the noise be reduced with spatial averaging?

11. 1 - StDev of bb measurement error [K] (RED), 2 - StDev of earth measurements [K] (BLUE); 3 - total atmospheric transmittance (BLACK) 2 3 1

12. Spatial distribution of 944.1 [1/cm] measurements [K]

13. Spatial distribution of 944.1 [1/cm] measurements [K]

14. Spatial distribution of 2555 [1/cm] measurements [K]

15. Spatial distribution of 2555 – 944.1 [1/cm] measurements [K]

16. Optical properties of cloud particles: imaginary part of refraction index
SW & LW channel differences are used for cloud identification
{4 m - 11m}, {4.13 m m}, and {4.53 m m}

17. AIRS 2378
Transmittance
within H20
absorption
band

18. Atmospheric transmittance in H2O sensitive region of spectrum 

19. Spatial distribution of 1370.2 [1/cm] measurements [K]

20. Spatial distribution of 1370.7 [1/cm] measurements [K]

21. Spatial distribution of 1370.2 – 1370.7 [1/cm] measurements [K]

22. Atmospheric transmittance in H2O sensitive region of spectrum 

23. Spatial distribution of Ch 1534 at 1375.27 [1/cm] measurements [K]

24. Spatial distribution of 1385.02 – 1375.27 [1/cm] measurements [K]

25. Atmospheric transmittance in H2O sensitive region of spectrum 

26. 1365 to 1440 cm-1earth emitted spectrum 
1372.5
1380
1387.5
1395
1402.5
1410
1417.5
1425
1432.5

27. Spatial distribution of Ch 1552 at 1385.02 [1/cm] measurements [K]

28. Spatial distribution of Ch 1553 at 1385.57 [1/cm] measurements [K]

29. Spatial distribution of Ch 1554 at 1386.11 [1/cm] measurements [K]

30. Spatial distribution of Ch 1555 at 1386.66 [1/cm] measurements [K]

31. Spatial distribution of Ch 1556 at 1387.21 [1/cm] measurements [K]

32. Spatial distribution of 1386.11 – 1386.66 [1/cm] measurements [K]

33. Spatial distribution of 1386.66 – 1387.21 [1/cm] measurements [K]

34. Spatial distribution of 1386.66 – 1387.21 [1/cm] measurements [K]

35. H2O spectral bands receive radiation from overlapping
100
Advanced Sounder
(3074)
H2O spectral bands receive
radiation from overlapping
layers of the atmosphere
Pressure (hPa)
1000
Moisture Weighting Functions
High spectral resolution advanced sounder will have more and sharper weighting functions compared to current GOES sounder. Retrievals will have 2 to 3 x better vertical resolution.
UW/CIMSS

36. AIRS 2378
Transmittance
within CO2
absorption
band

37. Atmospheric transmittance in CO2 sensitive region of spectrum 

38. Earth emitted spectrum in CO2 sensitive 705 to 760 cm-1
CO2 Lines

39. Earth emitted spectrum 740 to 755 cm-1
CO2 Lines       
745
746.25
747.5
748.75
750 

40. Spatial distribution of 747.4 1/cm measurements [K]

41. Spatial distribution of 747.8-747.4 1/cm measurements [K]

42. Spatial distribution of 747.8-747.4 1/cm measurements [K]

43. Earth emitted spectrum 740 to 755 cm-1
CO2 Lines       
745
746.25
747.5
748.75
750 

44. Spatial distribution of Ch 338 747.60 1/cm measurements [K]

45. Spatial distribution of Ch 341 748.60 1/cm measurements [K]

46. Difference {747.6 - 748.6 [1/cm]} [K] original data

47. Difference {747.6 - 748.6 [1/cm]} [K] filtered data

48. Temperature Weighting Functions
0.1
Advanced Sounder
(3074)
CO2 spectral bands receive radiation from overlapping layers of the atmosphere
Pressure (hPa)
1000
Temperature Weighting Functions
High spectral resolution advanced sounder will have more and sharper weighting functions compared to current GOES sounder. Retrievals will have 3 to 4 x better vertical resolution.
UW/CIMSS

49. UW
CIMSS

50. Mt Etna eruption
28 October 2002 ISS photo
28 October MODIS Aqua

51. AIRS 2378
667.1 cm-1
image
within CO2
absorption
band

52. Spatial distribution of 667.1 [1/cm] measurements [K]
Assymetry observed is not physical - problems with instrument?

53. Spatial distribution of 667.4-667.1 [1/cm] measurements [K]

54. Evolving to Future Satellites
Future remote sensing will address all four key remote sensing areas
* spatial resolution – what picture element size is required to identify feature of interest and to capture its spatial variability;
* spectral coverage and resolution – what part of EM spectrum at each spatial element should be measured, and with what spectral resolution, to analyze an atmospheric or surface parameter;
* temporal resolution – how often does feature of interest need to be observed; and
* radiometric resolution – what signal to noise is required and how accurate does an observation need to be.

55. Example Spectra

56. Day or night?

57. Day, night, desert, or ice/snow?

58. Day or night?

59. Land or ocean?

60. Desert, ocean, or cloudy?

61. Day, night, desert, or ocean?

62. Ocean, cloudy, snow/ice, or desert?

63. Day, night, desert, or cloudy?

64. Cloudy, desert, or ocean?

65. Land, desert, ice/snow, or ocean?

66. Day, night, desert, or cloudy?

67. Day, night, ocean, or cloudy?