Principal component-based radiative transfer model for hyperspectral sensors: theoretical concept

Abstract

Modern infrared satellite sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-Track Infrared Sounder (CrIS), the Tropospheric Emission Spectrometer (TES), the Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS), and the Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, superfast radiative transfer models are needed. We present a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the principal component-based radiative transfer model (PCRTM) predicts the principal component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from the properties of PC scores and instrument line-shape functions. The PCRTM is accurate and flexible. Because of its high speed and compressed spectral information format, it has great potential for superfast one-dimensional physical retrieval and for numerical weather prediction large volume radiance data assimilation applications. The model has been successfully developed for the NAST-I and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

The Spectral Signature of Recent Climate Change

“…Such an effort would be particularly timely given current and planned future instrumentation (e.g. AIRS, IASI, CrIS, IASI-NG [78]) and could potentially incorporate developments in our ability to perform fast, accurate, high spectral resolution radiative transfer calculations [79] whilst retaining consistency with assumptions made internally within the relevant models [80].…”

Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation

“…The computational expense can be lowered with ultra-fast radiative transfer methods (e.g. Liu et al, 2006). Alternatively, regional calculations may be considered for addressing those regions that contribute most significantly to climate sensitivity divergence (Armour et al, 2013).…”

“…The computational expense scales with the number of levels and sub-column calls. More optimized radiative transfer codes such as Principal Component Radiative Transfer Model (PCRTM) (Liu et al, 2006) can achieve a speed-up of at least 1 order of magnitude in the shortwave and 2 orders of magnitude in the long wave.…”

On the efficient treatment of temperature profiles for the estimation of atmospheric transmittance under scattering conditions

“…the O 2 A band, by optimally grouping wavenumbers with similar optical properties. Liu et al (2006) and Matricardi (2010) followed a related approach to speed up radiative transfer calculations for hyperspectral measurements from infrared sounders, such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Atmospheric Infrared Sounder (AIRS). We propose using PCA to reduce the dimensionality of temperature profiles and reproduce the top-of-atmosphere (TOA) transmittance, exploiting the largely linear relationship between temperature profile eigenvectors and the transmittance.…”