Hyperspectral data noise characterization using principle component analysis: application to the atmospheric infrared sounder

Tobin, David C.; Antonelli, Paolo; Revercomb, Henry E.; Dutcher, S.; Turner, David D.; Taylor, Joe K.; Knuteson, Robert O.; Vinson, K.

doi:10.1117/1.2757707

Cited by 28 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [7] applied a PCA-based noise filter, which eliminates the higher order principal components, to reduce the random noise present in the simulated and real hyperspectral infrared observations. The authors in [8] showed that the PCA is a powerful technique for diagnosing and filtering Atmospheric Infrared Sounder (AIRS) noise and other variable artifacts in hyperspectral data. The author in [9] also used the PCA approach to remove the subtle distortions in the AIRS instrument line shape introduced by nonuniform scene effects.…”

Section: Fengyun-3b Microwave Humidity Sounder (Mwhs) Data Noise Charmentioning

confidence: 99%

Fengyun-3B MicroWave Humidity Sounder (MWHS) Data Noise Characterization and Filtering Using Principle Component Analysis

Zou

2012

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

MicroWave Humidity Sounder (MWHS) onboard both Fengyun-3A (FY-3A) and FY-3B satellites have three channels (channels 3-5) near the 183-GHz water-vapor absorption line. These channel frequencies are also used in other instruments such as Advanced Microwave Sounding Unit-B (AMSU-B) and Microwave Humidity Sounder (MHS) onboard MetOp and NOAA satellites. Both MWHS and MHS are cross-track scanners.In this paper, a comparison between the simulated brightness temperatures with MWHS measurements clearly shows that MWHS observations from the three sounding channels contain a scanangle-dependent cohesive noise along the instrument scanline. This noise does not cancel out when a large amount of data over a sufficiently long period of time is averaged, which eliminates the possibility of such a noise to arise from the natural variability of the atmosphere and the surface. The noises are around 0.3, 0.2, and 0.2 K for channels 3-5, respectively. A principle component analysis is used for the characterization of this cohesive noise using one-month FY-3B MWHS data. It is shown that the MWHS cohesive noise is primarily contained in the first principal component (PC) mode, which mainly describes a scan-angle-dependent brightness temperature variation, i.e., a unique feature of the cross-tracking instrument. The first PC accounts for more than 99.91% total variance in the three MWHS sounding channels. A five-point smoother is then applied to the first PC, which effectively removes such a data noise in the MWHS data. The reconstruction of the MWHS radiance spectra using the noise-filtered first PC component is of good quality. The scan-angle-dependent bias from the reconstructed MWHS data becomes more uniform and is consistent with the NOAA-18 MHS data. Index Terms-Fengyun-3B (FY-3B), MicroWave Humidity Sounder (MWHS), Microwave Humidity Sounder (MHS), principal component analysis (PCA).

show abstract

Section: Fengyun-3b Microwave Humidity Sounder (Mwhs) Data Noise Charmentioning

confidence: 99%

Fengyun-3B MicroWave Humidity Sounder (MWHS) Data Noise Characterization and Filtering Using Principle Component Analysis

Zou

2012

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

show abstract

“…An example of the use of local rather than global training sets is the production of 'granule-based' principal components with AIRS data (Antonelli et al, 2004;Tobin et al, 2007). Here a granule is made up of six minutes of AIRS observations.…”

Section: Principal Component Analysismentioning

confidence: 99%

The use of principal component analysis for the assimilation of high‐resolution infrared sounder observations for numerical weather prediction

Collard

McNally

Hilton

et al. 2010

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

Methodologies are discussed for the efficient representation of observations from high-resolution infrared sounders for the purposes of assimilation into numerical weather prediction models. The use of principal component analysis is explored and it is noted that, while the available information in the observations is stored efficiently, the non-locality of the Jacobians that arise may cause practical problems in an operational assimilation system. Reconstructing radiance spectra from the principal components appears to be a more realistic approach in the near term.

show abstract

“…Moreover, our study found that the use of a smaller set of the PCs as the predicators can effectively reduce the bias at extreme OLR values (>310 Wm −2 ). The biases at high OLR values may be related to AIRS's scene‐dependent instrumental noise at shortwave spectral range [ Tobin et al , 2007].…”

Section: Technique For Estimation Of Airs Outgoing Longwave Radiationmentioning

confidence: 99%

Estimation of outgoing longwave radiation from Atmospheric Infrared Sounder radiance measurements

Sun¹,

Goldberg

Liu³

et al. 2010

J. Geophys. Res.

View full text Add to dashboard Cite

This study demonstrates the ability to use Atmospheric Infrared Sounder (AIRS) hyperspectral radiance measurements and collocated Clouds and the Earth's Radiant Energy System outgoing longwave fluxes to estimate top‐of‐atmosphere outgoing longwave radiation (OLR) from AIRS radiance measurements. The first 35 principal component scores of AIRS radiances from its 1707 pristine channels are used as predictors, and the regression coefficients are generated in eight regimes of AIRS view angle to account for angular dependence of the AIRS radiance observations. Tests on an independence test ensemble show that the accuracy of the AIRS OLR is near zero and the precision is less than 3 Wm−2 for all scenes and 2 Wm−2 for uniform scenes. The AIRS OLR precision for uniform scenes is much higher than the High‐Resolution Infrared Sounder OLR of 5 Wm−2 for similar comparisons with the Earth Radiation Budget Experiment OLR. The same technique of empirical regression OLR can be applied to other hyperspectral sounders such as the Cross‐track Infrared Sounder that will be on board the National Polar‐Orbiting Operational Environmental Satellite System and the Infrared Atmospheric Sounding Interferometer on the European Meteorological Polar‐orbiting satellites.

show abstract

Hyperspectral data noise characterization using principle component analysis: application to the atmospheric infrared sounder

Cited by 28 publications

References 7 publications

Fengyun-3B MicroWave Humidity Sounder (MWHS) Data Noise Characterization and Filtering Using Principle Component Analysis

Fengyun-3B MicroWave Humidity Sounder (MWHS) Data Noise Characterization and Filtering Using Principle Component Analysis

The use of principal component analysis for the assimilation of high‐resolution infrared sounder observations for numerical weather prediction

Estimation of outgoing longwave radiation from Atmospheric Infrared Sounder radiance measurements

Contact Info

Product

Resources

About