From educational accessibility to high-fidelity forward modeling: Development and validation of a lightweight python radiative transfer model

We present Lightweight Multiple-scattering Radiative Transfer (liteMRT), a purely Python-based radiative transfer code that bridges the gap between educational accessibility and high-fidelity forward modeling. Built upon the Gauss–Seidel iterative method–based gsit model, liteMRT demonstrates that sophisticated radiative transfer modeling can be achieved through a concise, well-structured codebase, making it highly accessible for educational purposes and algorithm development. In addition to its pedagogical clarity, this research also provides three benchmark cases for scalar (non-polarized) radiative transfer, covering homogeneous and non-homogeneous atmospheric profiles over Lambertian gray and urfaces. Validation against the benchmark Linearized Discrete Ordinate Radiative Transfer (LIDORT) model shows excellent numerical agreement, with maximum relative errors below 0.04%. The code maintains computational efficiency through Numba JIT acceleration and requires no external dependencies beyond standard scientific Python libraries, ensuring reproducibility across different computing environments. As an additional practical application example, the Appendix demonstrates the use of liteMRT for mission-relevant forward simulations using Orbiting Carbon Observatory-2 (OCO-2) observational geometry and instrument characteristics. The combination of educational clarity, high-fidelity forward modeling capability, and practical flexibility makes liteMRT suitable for applications ranging from newcomers’ learning to research-grade radiative transfer benchmarking and satellite mission simulation.