1. A Linear Approach to Cloud Clearing for Hyperspectral Sounders
Arun Ravindranath
Michael Chalfant
Robert Lindsay

2. Background
Successful retrieval of temperature, moisture soundings and ozone profiles and trace gases using hyperspectral instruments is enhanced by cloud clearing of measured radiances
Several candidate polynomial regression models that are linear in the parameters are used to model the calculated channel absorption coefficient for each channel
Modeling 14 channels in total; regression parameter estimates retrieved

3. Background (con’td)
Most effective cloud parameters: (1) cloud top pressure and (2) pressure at which the channel peaks
Two other potentially significant parameters: tropopause pressure and forecast surface pressure
Best model is determined by standard regression skill metrics
Estimated regression parameters of the best model are used to calculate the channel absorption coefficient, which is used to calculate the cleared radiances

4. Motivation
Measured radiances are contaminated when clouds are present in the readings
Variational analysis techniques are cumbersome and not optimal for cloud clearing when there is an invariant sample
Present a simpler and more elegant technique to cloud clear; our concern here is with modeling the channel absorption
This effort is essentially a "proof-of-concept" for NPP and NOAA-20 NUCAPS and MetOp-B and MetOp-C IASI sounding product accuracy improvement.

5. Materials: The Instruments Used
HIRS/4 instrument (
AMSU-A instrument (
MHS instrument (

6. Materials: The Instruments Used
CrIS
ATMS

7. Materials: The Instruments Used

8. Materials: The Instruments Used
The Biggest Stars of the Show…
Actually, I used an hp Z420 workstation model, not a DELL, but details, details…

9. Methods and Data Data: MetOp-B(1) data set (ASCII)
18 variables: orbit number, frame number, channel number, spot number, surface type, terrain height, latitude, longitude, radiance, and various pressure data, some of which were used in the modeling
Method: Clean data set of any “bad” values, filter data of contaminants (i.e. land surface types and very high and low brightness temperatures), perform regression to obtain optimal channel absorption coefficient, then calculate cleared radiances

10. Method Implementation
Remove “bad numbers” from data (NaNQ)
Filter out data from sea ice and snow covered terrain
Filter out excessively high terrains (> 1600 m)
Frame-by-Frame elimination of a single channel with excessively high or low brightness temperatures
Iteratively select other candidate models and compare on the basis of skill metrics
Perform regression on cloud parameters for channel absorption coefficient

11. Results
The cloud amount is overvalued for water vapor in some regions (yellow and orange colors), but reduced in most other areas (purple, blue colors)
The retrieval of CTP, which is largest in value over oceanic swaths between mb and nearly 1000 mb

12. Results
Percent contamination is always between 0.4 and 1.0, with 1.0 being nearly achieved almost everywhere for HIRS channel 8, prior to model
Measured brightness temperatures are too low where blue is seen, and too high where orange/red are seen. This means that there is still contamination
The DELPRS variable (predictand) from HIRS channel 8, or absorption coefficient shows some evidence of poor estimation where blue and red are seen

13. Results
First column: visual of pressure predictor variable (DELPRSC), in both its squared and cubic forms from the polynomial regression equation
Retrieval of channel absorption coefficient after parameters generated from a “very good guess”

14. Results Top left: ideal image of cleared radiances for HIRS channel 8
Bottom left: cleared limb-adjusted radiances for HIRS channel 8

15. Results Top left: Ideal image of cleared radiances
Top right: Actual image of cleared radiances; a fair amount of contamination still exists
Bottom left: NUCAPS cleared radiances retrieval
Bottom right: Metop-B cleared radiances retrieval

16. Conclusions & Future Work
There is tremendous potential in using simpler, linear regression approaches to modeling channel absorption and cloud clearing radiances
The scientific/mathematical machinery is elegant and much less cumbersome
Future: retrieve the coefficients from the proposed model (after finishing the debugging) and test this model against various alternates

17. I would like to take this opportunity to thank the following people:
Thank You!
Acknowledgements
I would like to take this opportunity to thank the following people:
My mentor, Michael Chalfant, for his excellent guidance and patience working with me
Robert Lindsay, who generously provided me with data and was there to answer any questions I may have had with regards to the data and programming in Fortran
Michael Wilson for his help and support; always willing to answer questions
Murty Divakarla for managing this program so well
NOAA-CREST, CCNY for sponsoring me
Dr. Shakila Merchant for encouraging me to apply and for her dedicated guidance