Applications of principal component analysis (PCA) on AIRS data

Mitchell D. Goldberg; Lihang Zhou; Walter W. Wolf; Chris Barnet; Murty G. Divakarla

doi:10.1117/12.578939

20 January 2005 Applications of principal component analysis (PCA) on AIRS data

Mitchell D. Goldberg, Lihang Zhou, Walter W. Wolf, Chris Barnet, Murty G. Divakarla

Proceedings Volume 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II; (2005) https://doi.org/10.1117/12.578939
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States

Abstract

Observations from the high spectral resolution Atmospheric InfraRed Sounder (AIRS) on the NASA EOS AQUA platform are providing improved information on the temporal and spatial distribution of key atmospheric parameters, such as temperature, moisture and clouds. These parameters are important for improving real-time weather forecasting, climate monitoring, and climate prediction. Trace gas products such as ozone, carbon dioxide, carbon monoxide, and methane are also derived. High spectral resolution infrared radiances from AIRS are assimilated into numerical weather prediction models. The soundings and radiances are provided in near real-time by NOAA/NESDIS to the NWP community. A significant component of the NOAA/NESDIS AIRS processing is to apply Principal Component Analysis (PCA) to the original AIRS 2000+ channel radiances. PCA is used for monitoring of the AIRS detectors, dynamic noise estimation and filtering, errant channel recovery, radiance reconstruction, and deriving an initial guess for profiles of temperature, moisture, ozone and other geophysical parameters. Since PCA has the ability to reduce the dimensionality of a dataset while retaining the significant information content, investigations are being done on its applications to AIRS data compression and archiving. Data compression is one of the key issues for the new generation of high spectral resolution satellite sensors. Our current AIRS research will allow us to provide valuable information and real-time experience to the generation of products for future sensors, such as the EUMETSAT IASI and NPOESS CrIS advanced infrared sounders. Examples of each application, along with details on the generation and application of eigenvectors are presented in this paper.

Citation Download Citation

Mitchell D. Goldberg, Lihang Zhou, Walter W. Wolf, Chris Barnet, and Murty G. Divakarla "Applications of principal component analysis (PCA) on AIRS data", Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, (20 January 2005); https://doi.org/10.1117/12.578939

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