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Abstract:
The increasing resolution of numerical weather prediction models makes 3D radiative effects more and more important. However, 3D radiative transfer solvers are still computationally expensive, largely preventing their use in operational weather forecasting. To address this issue, we present a new, “dynamic” 3D radiative transfer model that is based on the TenStream solver (Jakub and Mayer, 2015) and delivers a significant speed-up utilizing two main concepts. First, radiation in this model is not calculated from scratch every time the scheme is called, but uses a time-stepping scheme to update the radiative field based on the result from the previous radiation time step. Secondly, the model is based on incomplete solves, performing just the first few steps towards convergence every time it is called. Applied, these two concepts alone allow to produce radiative flux and heating rate fields close to the original TenStream results at dramatically increased speed. In addition, we use an optimized wavelength sampling that allows to noticeably reduce the number of spectral intervals to calculate integrated shortwave and longwave heating rates without a significant loss in precision. Together, these approaches allow to accelerate 3D radiative transfer towards the speed of currently employed 1D solvers. To demonstrate this, we apply the new solver to a precomputed shallow cumulus cloud time series and compare it to both a traditional 1D delta-Eddington solver, the original TenStream solver, as well as to a benchmark solution provided by the 3D Monte Carlo solver MYSTIC (Mayer, 2009).
