Radiative transfer models for retrieval of cloud parameters from EPIC/DSCOVR measurements

Abstract

In this paper we analyze the accuracy and efficiency of several radiative transfer models for inferring cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR). The radiative transfer models are the exact discrete ordinate and matrix operator methods with matrix exponential, and the approximate asymptotic and equivalent Lambertian cloud models. To deal with the computationally expensive radiative transfer calculations, several acceleration techniques such as, for example, the telescoping technique, the method of false discrete ordinate, the correlated k-distribution method and the principal component analysis (PCA) are used. We found that, for the EPIC oxygen A-band absorption channel at 764 nm, the exact models using the correlated k-distribution in conjunction with PCA yield an accuracy better than 1.5 % and a computation time of 18 s for radiance calculations at 5 viewing zenith angles.

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

“…As future goals, following [18,49] we plan to extend the CLSR method for computing the Stokes parameters and analyze the possibility of hybrid use of the CLSR method, the PCA-based method and the correlated k-distribution technique. It is also planned to analyze different atmospheric scenarios such as cirrus clouds [50] as well as the efficiency of the CLRS method for improving the accuracy of other types of approximate models.…”

“…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.…”

Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

“…In this study, the multi-stream model is based on the discrete ordinate method with matrix exponential (DOME) [19][20][21] with 16 streams per hemisphere. Regarding the two-stream model, two comments are in order:…”

Linearized radiative transfer models for retrieval of cloud parameters from EPIC/DSCOVR measurements

“…EPIC has two pairs of reference and absorption channels in the oxygen A-band (780 and 764 nm) and B-band (680 and 688 nm), which are used for monitoring the vegetation condition [1], the aerosol layer height and optical depth [2], as well as the cloud height and optical depth [3]. A further description of EPIC/DSCOVR geometry and their channels can be found in [4].…”

“…In [4], we analyzed exact and approximate radiative transfer models regarding their applicability to the retrieval of cloud parameters from EPIC measurements. It has been shown that the exact Discrete Ordinate method with Matrix Exponential (DOME) and the Matrix Operator method with Matrix Exponential (MOME) using the correlated k-distribution method [5] in conjunction with the Principal Component Analysis (PCA) technique [6][7][8][9] fulfill the accuracy and efficiency requirements for such kind of application.…”