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Our results show that optimization convergence, scaling strategy, and network complexity strongly influence emulation accuracy. When effective scaling is applied and convergence is achieved, the relatively simple architecture, used together with a moderate network size, can reproduce key features of the microphysics-induced aerosol concentration changes with promising accuracy. These findings provide practical clues for the next stages of emulator development; they also provide general insights that are likely applicable to the emulation of other aerosol processes, as well as other atmospheric physics involving multi-scale variability.
| Comments: | 16 pages, 7 figures |
| Subjects: | Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Geophysics (physics.geo-ph) |
| Report number: | PNNL-SA-221395 |
| Cite as: | arXiv:2604.21233 [physics.ao-ph] |
| (or arXiv:2604.21233v1 [physics.ao-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21233 arXiv-issued DOI via DataCite (pending registration) |
From: Shady E. Ahmed [view email]
[v1]
Thu, 23 Apr 2026 02:58:50 UTC (4,129 KB)
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