Radiative transfer acceleration based on the principal component analysis and...

Bak, J., X. Liu, R. J. D. Spurr, K. Yang, C. Nowlan, Chan Miller, Gonzalez Abad, and K. Chance (2021), Radiative transfer acceleration based on the principal component analysis and lookup table of corrections: optimization and application to UV ozone profile retrievals, Atmos. Meas. Tech., 14, 2659-2672, doi:10.5194/amt-14-2659-2021.

In this work, we apply a principal component analysis (PCA)-based approach combined with look-up tables (LUTs) of corrections to accelerate the VLIDORT radiative transfer (RT) 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 2-stream multiple scattering 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 native measurements at 229 wavelengths (spectral resolution: 0.4-0.6 nm). We also developed a Look Up Table (LUT) to correct RT approximations performed using a scalar RT model with 4 streams (discrete ordinates) and 24 layers, thereby achieving the accuracy at the level attainable from simulations with a vector model with 12 streams and 72 layers; this speeds up the RT calculations by more than 2 orders of magnitude when ignoring other overhead. 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


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Atmospheric Composition
Aura- OMI