Optical property dimensionality reduction techniques for accelerated radiative transfer performance: Application to remote sensing total ozone retrievals

Efremenko, Dmitry; Doicu, Adrian; Loyola, Diego; Trautmann, Thomas

doi:10.1016/j.jqsrt.2013.07.023

Cited by 31 publications

(24 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since a PCA-based RTM can be implemented in several ways (e.g., [12,13,[15][16][17]44,45]), we describe below the main features of the PCA-based RTM used in this paper. The idea of the method is to refine the two-stream solution by a correction function estimated in the reduced space of the optical properties.…”

Section: Pca-based Rtmmentioning

confidence: 99%

“…Setting ∆x w = ∑ M k=1 y wk q k , we consider a second-order Taylor expansion of f (x w ) aroundx in the direction ∆x w . Approximating the directional derivatives with central differences (see [13,17] for mathematical details) we obtain…”

Section: Pca-based Rtmmentioning

confidence: 99%

“…A two-stream radiative transfer model was used as an approximate model, and the dependency of the corresponding correction factor on the optical parameters was modeled by a second-order Taylor expansion about the mean value of the optical parameters in the reduced optical data space. This approach was extended to other dimensionality reduction techniques [13] and spectral ranges [14-16]; moreover, it was implemented in conjunction with PCA for spectral radiances [17] and with the k-distribution method [18]. The errors of these approaches are usually below 0.1% for the spectral radiances, while the performance enhancement may reach several orders of magnitude depending on the spectral region and the required level of accuracy.In Efremenko et al [19] it was shown that, after parallelizing the PCA-based RTM computations, as much as half of the computational time is due to the PCA itself; consequently, according to Amdahl's Law [20], no further acceleration of a PCA-based RTM is possible.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

et al. 2020

View full text Add to dashboard Cite

Current atmospheric composition sensors provide a large amount of high spectral resolution data. The accurate processing of this data employs time-consuming line-by-line (LBL) radiative transfer models (RTMs). In this paper, we describe a method to accelerate hyperspectral radiative transfer models based on the clustering of the spectral radiances computed with a low-stream RTM and the regression analysis performed for the low-stream and multi-stream RTMs within each cluster. This approach, which we refer to as the Cluster Low-Streams Regression (CLSR) method, is applied for computing the radiance spectra in the O 2 A-band at 760 nm and the CO 2 band at 1610 nm for five atmospheric scenarios. The CLSR method is also compared with the principal component analysis (PCA)-based RTM, showing an improvement in terms of accuracy and computational performance over PCA-based RTMs. As low-stream models, the two-stream and the single-scattering RTMs are considered. We show that the error of this approach is modulated by the optical thickness of the atmosphere. Nevertheless, the CLSR method provides a performance enhancement of almost two orders of magnitude compared to the LBL model, while the error of the technique is below 0.1% for both bands. simulations. In [7,8], the transmission function for a given spectral interval is fitted by a sum of exponentials, while the corresponding fitting coefficients are computed from a reduced number of monochromatic computations. A similar approach is described in Moncet et al. [9], where the fitting weights and the most representative wavelengths are chosen appropriately.The state-of-the-art hyperspectral RTMs employ dimensionality reduction techniques such as principal component analysis (PCA). In [10,11], PCA is applied to the spectral radiance data to establish a set of empirical orthogonal functions (EOFs), so that an arbitrary spectrum at full spectral resolution can be reconstructed as a weighted sum of EOFs. The weights are found by performing monochromatic simulations at a reduced number of wavelengths. To accelerate the computations in the O 2 A-band, Natraj et al. [12] proposed a fundamentally different PCA-based radiative transfer model, in which the dimensionality of the optical properties data is reduced. A two-stream radiative transfer model was used as an approximate model, and the dependency of the corresponding correction factor on the optical parameters was modeled by a second-order Taylor expansion about the mean value of the optical parameters in the reduced optical data space. This approach was extended to other dimensionality reduction techniques [13] and spectral ranges [14-16]; moreover, it was implemented in conjunction with PCA for spectral radiances [17] and with the k-distribution method [18]. The errors of these approaches are usually below 0.1% for the spectral radiances, while the performance enhancement may reach several orders of magnitude depending on the spectral region and the required level of accuracy.In Efremenko et al. [19] it was shown that, aft...

show abstract

Section: Pca-based Rtmmentioning

confidence: 99%

Section: Pca-based Rtmmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The error with respect to more rigorous solutions of the RTE in general depends on the scene and spectral band and can be estimated by carrying out rigorous simulations. Secondly, by making use of the inherent redundancy of line-by-line calculations by using data reduction techniques such as principal component analysis (e.g., Efremenko et al, 2013;Jolliffe, 2002), the number of individual radiative transfer simulations is reduced. Approaches have been published for the IR spectral region by Liu et al (2006) and the VIS to SWIR region by Natraj et al (2010Natraj et al ( , 2005 and Lindstrot and Preusker (2012).…”

Section: Introductionmentioning

confidence: 99%

Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

Hollstein

Lindstrot

2014

Atmos. Meas. Tech.

View full text Add to dashboard Cite

Abstract. Hyperspectral radiative transfer simulations are a versatile tool in remote sensing but can pose a major computational burden. We describe a simple method to construct hyperspectral simulation results by using only a small spectral subsample of the simulated wavelength range, thus leading to major speedups in such simulations. This is achieved by computing principal components for a small number of representative hyperspectral spectra and then deriving a reconstruction matrix for a specific spectral subset of channels to compute the hyperspectral data. The method is applied and discussed in detail using the example of top-of-atmosphere radiances in the oxygen A band, leading to speedups in the range of one to two orders of magnitude when compared to radiative transfer simulations at full spectral resolution.

show abstract

“…The computational speed of radiative transfer for satellite and ground based remote sensing measurementsis always a big issue [1][2][3][4]. The most precise way to simulate the atmospheric radiative transfer process is the line-by-line (LBLRTM) radiative transfer method, but its time-consuming nature prevents it from being applied to practical problems.…”

Section: Introductionmentioning

confidence: 99%

Alternate Mapping Correlated k-Distribution Method for Infrared Radiative Transfer Forward Simulation

Zhang

Zhu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

The alternate mapping correlated k-distribution (AMCKD) method is studied and applied to satellite simulations. To evaluate the accuracy of AMCKD, the simulated brightness temperatures at the top of the atmosphere are compared with line-by-line radiative transfer model (LBLRTM) or the observed data which are from Advanced Himawari Imager (AHI) on board the Himawari-8, as well as Medium Resolution Spectral Imager (MERSI) on board the Fengyun-3D. The result of AMCKD is also compared with the algorithm of Radiative Transfer for the Television Observation Satellite Operational Vertical Sounder (RTTOV). Under the standard atmospheric profiles, the absolute errors of AMCKD in all longwave channels of AHI and MERSI are bounded by 0.44K compared to the benchmark results of LBLRTM, which are more accurate than those of RTTOV. In the most cases, the error of AMCKD is smaller than the NEDT at ST, while the error of RTTOV is larger than the instrument noise equivalent temperature (NEDT) at scene temperature (ST). Under real atmospheric profile conditions, the errors of AMCKD increase, because the input data from ERA-Interim reanalysis dataause bias in the satellite remote sensing results. In the most considered cases, the accuracy of AMCKD is higher than RTTOV, while the efficiency of AMCKD is slightly slower than RTTOV.

show abstract

Optical property dimensionality reduction techniques for accelerated radiative transfer performance: Application to remote sensing total ozone retrievals

Cited by 31 publications

References 22 publications

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

Cluster Low-Streams Regression Method for Hyperspectral Radiative Transfer Computations: Cases of O2 A- and CO2 Bands

Fast reconstruction of hyperspectral radiative transfer simulations by using small spectral subsets: application to the oxygen A band

Alternate Mapping Correlated k-Distribution Method for Infrared Radiative Transfer Forward Simulation

Contact Info

Product

Resources

About