Automatic optical depth parametrization in radiative transfer model RTTOV v13 via LASSO-induced sparsity

The assimilation of satellite spectral sounder data requires fast and accurate radiative transfer models. This study proposes a novel methodology to automatically parameterize atmospheric optical depths within the Radiative Transfer for TOVS (RTTOV) version 13 scheme using statistical thresholds across pressure levels and Least Absolute Shrinkage and Selection Operator (LASSO) regression to induce sparsity. Numerical experiments with Visible Infrared Imaging Radiometer Suite (VIIRS) infrared channels demonstrate that this approach significantly reduces computational costs while maintaining accuracy. The sparsity also facilitates the automatic selection of absorbing gases and predictors by channel and pressure level, making it particularly effective for multispectral instruments with numerous atmospheric variables. These findings highlight the potential of sparse regression methods to enhance the efficiency of radiative transfer models for satellite data assimilation.