A Diagnostic Framework for Spectral Biases in Fast Radiative Transfer Models: An ANOVA-based Uncertainty Decomposition of RTTOV

RTTOV is the operational fast radiative transfer model used as the forward operator in data assimilation systems at major NWP centres, including Météo-France and ECMWF. Its accuracy plays a crucial role in the evaluation and representation of observation errors. For instance, any limitations of its transmittance model can introduce systematic biases in the simulated brightness temperatures. These biases may propagate through the assimilation system, affecting both the retrieved atmospheric fields and the performance of the bias correction scheme.

Estimating and attributing biases in fast RT simulations remains challenging due to the complex and interacting error sources. In this study, we present a new ANOVA-style methodology to diagnose and separate these sources of biases using reference line-by-line models, satellite observations, and 1D-Var retrievals. We focus on three main contributors: spectroscopy, transmittance parametrisation, and uncertainties in atmospheric profiles. By analysing spectral biases across channels, gas absorption bands, and atmospheric regimes (e.g., dry, humid, tropical, polar), we identify dominant error sources and their impact on temperature and humidity retrievals.

Recent improvements in RTTOV coefficients and spectroscopy are also evaluated, demonstrating their impact on forward simulations for IASI (and prospectively FORUM) and on retrieved profiles. By isolating key error sources, this work strengthens the link between fast forward model development, bias correction schemes and retrieval accuracy.