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

Abstract

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...

The Cluster Low-Streams Regression Method for Fast Computations of Top-of-the-Atmosphere Radiances in Absorption Bands

“…Therefore, the median values remain almost constant from 3 regression points. This trend is identical to the one found in [11] for different atmospheric scenarios. Note that the scale of residuals for the water vapour band is one order of magnitude higher than for the Hartley-Huggins, O 2 A-and CO 2 bands.…”

“…The Cluster Low-Streams Regression (CLSR) method is described in detail in [11] and can be summarized as follows:…”

“…In this section we apply the CLSR method to simulate absorption bands. In addition to O 2 A-and CO 2 bands analyzed in [11], we consider Hartley-Huggins and water vapour bands.…”

“…the continuum radiance, which is used to avoid radiance values close to zero in the denominator of Eq. 4, when strong gas absorption is present [11,7]). Figure 2 shows the residuals of the CLSR method for different number of regression points per cluster for the four bands considered.…”

“…In our recent work [11], the Cluster Low-Streams Regression (CLSR) method was developed to accelerate hyper-spectral computations. The idea of the CLSR method is to perform LBL computations by using a fast two-stream RTM and then to refine results by using the correlation model for the two-stream and reference multi-stream RTMs.…”