1. Hyperspectral Cloud Boundary Retrievals
Robert E. Holz Steve Ackerman, Paolo Antonelli, Wayne Feltz, Fred Nagle, and Ed Eloranta

2. Overview Motivation Cloud level algorithms S-HIS cloud top retrieval
AERI cloud base retrieval

3. Why Investigate Cloud Base-Top Retrievals?
Over 70% of the earth is covered with clouds (Wylie)
Knowledge of the cloud level is fundamental for understanding the cloud radiative forcing
Cloudy infrared temperature and water vapor retrievals require knowledge of cloud levels
How do active lidar cloud top-base retrievals compare to hyperspectral infrared retrievals

4. Cloud Level Determination
MLEV (Minimum Local Emissivity Variance)
Strength: Accurate for optically thick clouds
CO2 Slicing
Strength: Insensitive to cloud fraction and capable of detecting thin clouds
Forward model required to simulate upwelling radiances
Problem: Optimal channels are a function of cloud top pressure
CO2 Sorting
A new algorithm to pick the optimal CO2 slicing channel pairs. Also has the potential to independently retrieve cloud top pressure
What is optically thin? Optically thick? Emissivity .15 to much error
Give overview of these two alg talk about there strenghts and weeknesses.
Should I referecne papers on MLEV and co2 sliceing?
MLEV also requires clear sky model?
Lead into the new alg
Talk about co2 sorting

5. CO2 Channel Selection Algorithm (CO2 Sorting)
The selected clear sky CO2 spectrum is sorted according to brightness temperature
The Sorted Clear Sky Spectrum
Sorted Index
These figures show the channels used for the CO2 sorting (Left figure) and the effect of sorting the channels (right figure) for clear scene case
-Sort from coldest to warmest BT
-creat the sorted index which his aplied to all scene including clouds

6. CO2 Sorting: Sensitivity to Brightness Temperature
High and Thick Cloud
Thinner Cloud
Low Cloud
High and Thick Cloud
Thinner Cloud
High and Thick Cloud
Major points to talk about:
Effect of adding clouds to the sorted spectrum
Information about the cloud height (inflection point) and optical thickness (slope)
This figure demonstrates the signature of different cloud scenes. The first channel that “sees” the cloud will start to diverge from the sorted clear sky scene. For the high thick cloud this occurs at approximately 230 K. The slope of the sorted spectrum also has information about the cloud optical thickness. A sorted spectrum that quickly diverges from the clear sky scene represents a thick cloud. In contrast, a thin high cloud will have a more gradual slope is demonstrated in the figure. The cloud high algorithm looks to see if there is a divergence from the clear sky spectrum. The brightness temperature of the first channel that diverges beyond a pre-set threshold is determined the cloud top temperature. Using a pressure and temperature profile the cloud pressure level can be determined. Will talk about the problems with this approach.

7. February

8. S-HIS vs CPL

9. S-HIS Cloud Top Retrievals Dec 5th 2003

10. S-HIS CPL vs S-HIS Dec 5th

11. AERI Cloud Base Retrievals

12. Conclusions
MLEV is the consistent performer out of the three cloud top algorithms
The combined CO2 slicing + sorting cloud top retrieval can be an improvement compared to fixed pair CO2 slicing
CO2 sorting improves the up looking AERI cloud base retrieval