Abstract
Abstract. In this work, we apply a principal component analysis (PCA)-based approach combined with lookup tables (LUTs) of corrections to accelerate the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model used in the retrieval of ozone profiles from backscattered ultraviolet (UV) measurements by the Ozone Monitoring Instrument (OMI). The spectral binning scheme, which determines the accuracy and efficiency of the PCA-RT performance, is thoroughly optimized over the spectral range 265 to 360 nm with the assumption of a Rayleigh-scattering atmosphere above a Lambertian surface. The high level of accuracy (∼ 0.03 %) is achieved from fast-PCA calculations of full radiances. In this approach, computationally expensive full multiple scattering (MS) calculations are limited to a small set of PCA-derived optical states, while fast single scattering and two-stream MS calculations are performed, for every spectral point. The number of calls to the full MS model is only 51 in the application to OMI ozone profile retrievals with the fitting window of 270–330 nm where the RT model should be called at fine intervals (∼ 0.03 nm with ∼ 2000 wavelengths) to simulate OMI measurements (spectral resolution: 0.4–0.6 nm). LUT corrections are implemented to accelerate the online RT model due to the reduction of the number of streams (discrete ordinates) from 8 to 4, while improving the accuracy at the level attainable from simulations using a vector model with 12 streams and 72 layers. Overall, we speed up our OMI retrieval by a factor of 3.3 over the previous version, which has already been significantly sped up over line-by-line calculations due to various RT approximations. Improved treatments for RT approximation errors using LUT corrections improve spectral fitting (2 %–5 %) and hence retrieval errors, especially for tropospheric ozone by up to ∼ 10 %; the remaining errors due to the forward model errors are within 5 % in the troposphere and 3 % in the stratosphere.
Highlights
Optimal-estimation-based inversions have become standard for the retrieval of atmospheric ozone profiles from atmospheric chemistry UV and visible (UV–Vis) backscatter instruments
Such exact radiative transfer (RT) calculations cannot be applied in the operational data processing system, especially when thousands of spectral points are involved; in other words, the operational capability of the principal component analysis (PCA)-RT approach has been overestimated in previous studies
We have extended the PCA-based fast-RT method to improve computational challenges for OE-based SAO Ozone Monitoring Instrument (OMI) ozone profile retrievals requiring iterative calculations of the radiance and its Jacobian derivatives
Summary
Optimal-estimation-based inversions have become standard for the retrieval of atmospheric ozone profiles from atmospheric chemistry UV and visible (UV–Vis) backscatter instruments This inversion model requires iterative simulations of radiances, and of Jacobians with respect to atmospheric and surface variables, until the simulated radiances are sufficiently matched with the measured radiances. Bak et al.: RT acceleration to UV ozone profile retrievals correction scheme based on a lookup table (LUT) (Kroon et al, 2011; Miles et al, 2015) Another approach is to carry out online vector calculations at a few wavelengths (Liu et al, 2010) together with other approximations (e.g., low-stream, coarse vertical layering, Lambertian reflectance for surface and cloud, no aerosol treatment). In recent years, applying neural network techniques and principal component analysis (PCA) to RT computational performance has received quite a lot of attention (e.g., Natraj et al, 2005; Spurr et al, 2013, 2016; Liu et al, 2016; Yang et al, 2016; Loyola et al, 2018; Nanda et al, 2019; Liu et al, 2020)
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