Application of PCA Using Hyperspectral Infrared Sounder Data to Climate Research

1. Application of PCA Using Hyperspectral Infrared Sounder Data to Climate Research Yibo Jiang1, Hartmut H. Aumann1 Yuk L. Yung2, Fai Li2 1. JPL, 4800 Oak Grove Dr, Pasadena, CA 91009, United States 2. Caltech, 1200 E California Blvd, Pasadena, CA 91125, United States

2. Abstract The application of Principle Component Analysis (PCA) to cloud-free hyperspectral sounder data provides insight into climate changes which may otherwise be masked by noise in the data. We evaluate the sensitivity of the PCA eigenfunctions and eigenvalues to the training data set under typical clear and cloudy conditions. However, the most obvious change or variability in the climate will be seen in clouds. This analysis provides insight into climate changes which may otherwise be masked by noise in the data. We do this by creating nominally equivalent training sets by randomly selecting different number of near nadir spectra from the total 500,000 representative spectra within 0.5 degrees of nadir collected by the Atmospheric Infrared Sounder for a month. This numerical experiment of climate (NEC) shows the natural variability of the atmosphere may dominate the climate change signal, and you may need to average over one year to smooth the natural variability. But the rapidly convergence for the first few PCA components as compared to that of the spectra average (zero component) indicates that much less spectra needed in order to identify the true climate change signal.

3. Random Selected Spectra Data Locations (2007/01) 5 Days of Random Selected Data Locations (black) & Random Selected 2000 Data Locations (red)

4. Mean Bright Temperature Differences between Different Number of Random Selected Spectra and Averaged Spectra (True) of All Data

5. The Standard Deviation of Different Number of Random Selected Spectra

6. PCA Components and the Amplitude of Each Different Number of Random Selected Spectra #1 Component #2 Component #3 Component #4 Component #5 Component

7. Standard Deviation of the First Two PCA Components for Each Different Number of Random Selected Spectra #1 Component #2 Component

8. PCA Components and Their Differences for 5000 of Random Selected Spectra #1 Component #2 Component #3 Component #4 Component

9. Mean Bright Temperature Differences between 2000 Random Selected Spectra and Averaged Spectra of All Data

10. Mean STD and BT Differences between 2000 Random Selected Spectra and Averaged Spectra of All Data BT Averaged Globally STD Averaged Globally STD Averaged over Land STD Averaged over Ocean

11. Summary • Averaged brightness temperature over random selected AIRS spectra in a month shows the variations which may affect the climate signal retrieval • Spectra over one year need to be averaged in order to smooth the atmospheric variations • PCA components on globally representative spectra converge rapidly, may need much less spectra as compared to the averaged brightness temperature to retrieval the climate change signals