RTTOVpy v1.0: A Python framework for automated radiative-transfer simulations for satellite remote sensing using ERA5 and WRF atmospheric data

Abstract. Satellite observations provide an essential reference for evaluating atmospheric models, but meaningful comparisons require model variables to be transformed into the radiometric quantities measured by satellite instruments. This transformation commonly involves substantial preprocessing, configuration, radiative-transfer execution, and post-processing. We present RTTOVpy, an open-source Python application that provides an automated and reproducible interface between atmospheric datasets and the Radiative Transfer for TOVS (RTTOV) model. RTTOVpy supports atmospheric input from ERA5 reanalysis and Weather Research and Forecasting (WRF) model output. It extracts atmospheric and surface variables, constructs vertically consistent RTTOV profiles, defines surface and satellite-viewing geometries, executes simulations for configurable sensors and channels, and reorganizes the results into georeferenced NetCDF files and satellite-like imagery. Solar-reflectance calculations and aerosol simulations using WRF-Chem dust profiles can also be enabled when supported by the selected RTTOV coefficient files. An integrated verification workflow remaps satellite observations to the model grid and provides statistical comparisons and Taylor diagrams.

The application is demonstrated through an ERA5-based simulation for the MODIS instrument and WRF-based simulations for the SEVIRI instrument, including a dust event simulated with WRF-Chem. The latter experiment evaluates the sensitivity of simulated infrared brightness temperatures to atmospheric dust by comparing dust-free and dust-aware simulations against satellite observations. For three SEVIRI infrared channels (8.7, 10.8, and 12.0 μm), spatial correlations range from 0.72 to 0.76. Including dust reduces the normalized centered root-mean-square difference at 8.7 μm from 0.727 to 0.691, while the other channels exhibit negligible or slightly unfavorable changes, indicating a channel-dependent response rather than a uniform improvement. These experiments demonstrate RTTOVpy's capability to generate satellite-equivalent model fields and quantify the radiometric effects of atmospheric constituents within a reproducible simulation and verification workflow. The current implementation primarily targets clear-sky conditions, with cloudy-sky support and more efficient multi-profile RTTOV execution identified for future development. RTTOVpy provides a practical observation-operator framework for satellite-based model evaluation, radiative-transfer experimentation, and the generation of synthetic satellite products from atmospheric model and reanalysis datasets.