You raise several points which I’ll try to address. We did develop a surrogate model for this problem, see the narrative and references below eq. 1 of [1]. The

You raise several points which I’ll try to address. We did develop a surrogate model for this problem, see the narrative and references below eq. 1 of [1]. The surrogate is perhaps 10x faster than the full physics model. It was developed by a postdoc working with the science team and took a few months. (We needed a lot of science team expertise because they advised on what computations could be simplified and what parameters could be lumped together.) We use the surrogate for various Monte Carlo analyses, parameter sweeps, etc. And for MCMC as described in [1]. All tasks that the full physics model is too slow for. But the emulator, developed more recently, is much, much faster than the surrogate. It did not require as much science team expertise to develop, it was more of a data science problem than an integrated physics/data science task. So it was faster to put together. The increased speed of the emulator means that all those parameter sweeps and MCMC inversions can be much broader in scope. Furthermore, the emulator is somewhat generic. We have used the same approach in other spectroscopic contexts. E.g., [2], for imaging spectroscopy, and there is a still-unpublished infrared water-vapor/cloud sounder example. It seems like generic emulation for a broad class of far IR to UV spectroscopy radiative transfer is possible, without needing problem-specific physical modeling expertise to find model simplifications. (We have direct access to this expertise, it’s just that those folks have their own research to do.) So: faster than a surrogate, broad in scope, faster turnaround. About error control. We’re using a Gaussian process with a learned kernel, so we do get error estimates out of the approach, if we want to use them. See under “kernel flows” at [3]. [1] https://www.mdpi.com/2072-4292/11/17/2061 https://www.mdpi.com/2072-4292/11/17/2061 [2] https://ui.adsabs.harvard.edu/abs/2021AGUFMNG25A0506S/abstract https://ui.adsabs.harvard.edu/abs/2021AGUFMNG25A0506S/abstra... [3] http://users.cms.caltech.edu/~owhadi/research.htm http://users.cms.caltech.edu/~owhadi/research.htm