Fast radiative transfer using monochromatic look-up tables

- Method to create, compress, and validate absorption coefficient LUTs is proposed.
- Allows for simulation of thermal IR radiances 20-100 times faster than line-by-line.
- LUTs were created for top 11 atmospheric trace gases for nadir-viewing scenarios.
- LUT interpolation errors for IASI simulations found to be factors below IASI noise.
- Validation of LUTs was performed over an atmospheric ensemble indicative of Earth.

Line-by-line (LBL) methods of numerically solving the equations of radiative transfer can be inhibitingly slow. Operational trace gas retrieval schemes generally require much faster output than current LBL radiative transfer models can achieve. One option to speed up computation is to precalculate absorption cross sections for each absorbing gas on a fixed grid and interpolate. This work presents a general method for creating, compressing, and validating a set of individual look-up tables (LUTs) for the 11 most abundant trace gases to use the Reference Forward Model (RFM) to simulate radiances observed by the Infrared Atmospheric Sounding Interferometer (IASI) at a more operational pace. These LUTs allow the RFM to generate radiances more than 20 times faster than LBL mode and were rigorously validated for 80 different atmospheric scenarios chosen to represent variability indicative of Earth׳s atmosphere. More than 99% of all IASI simulated spectral channels had LUT interpolation errors of brightness temperature less than 0.02 K, several factors below the IASI noise level. Including a reduced spectral grid for radiative transfer speed up the computation by another factor of six at the expense of approximately doubling interpolation errors, still factors below IASI noise. Furthermore, a simple spectral compression scheme based upon linear interpolation is presented, which reduced the total LUT file size from 120 Gbytes to 5.6 Gbytes; a compression to just 4.4% of the original. These LUTs are openly available for use by the scientific community, whether using the RFM or to be incorporated into any forward model.