Abstract Due to lack of a unified description of the Earth surface temperature, a generic dynamic equation is postulated as an inference from the special case of snow. Solar radiation is explicitly included in the formulation for transparent media such as snow, ice and water while implicitly through (conductive) surface heat flux for non‐transparent media such as soil. The physical parameters of the equation are medium thermal inertia, thermal and radiative diffusivity. The equation for transparent media reduces to the familiar force‐restore model of soil surface temperature when the penetration depth of solar radiation tends to zero. Proof‐of‐concept validation for snow surface temperature as a paradigm of transparent media at three sites in the Arctic and Antarctica confirms the postulated equation as a generic description of the dynamics of surface temperature.
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Accurate depth-resolved temperature profiling via thermal-radiation spectroscopy: numerical methods vs machine learning
We present and compare three approaches for accurately retrieving depth-resolved temperature distributions within materials from their thermal-radiation spectra, based on: (1) a nonlinear equation solver implemented in commercial software, (2) a custom-built nonlinear equation solver, and (3) a deep neural network (DNN) model. These methods are first validated using synthetic datasets comprising randomly generated temperature profiles and corresponding noisy thermal-radiation spectra for three different structures: a fused-silica substrate, an indium antimonide substrate, and a thin-film gallium nitride layer on a sapphire substrate. We then assess the performance of each approach using experimental spectra collected from a fused-silica window heated on a temperature-controlled stage. Our results demonstrate that the DNN-based method consistently outperforms conventional numerical techniques on both synthetic and experimental data, providing a robust solution for accurate depth-resolved temperature profiling.
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- Award ID(s):
- 2244259
- PAR ID:
- 10634421
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Express
- Volume:
- 33
- Issue:
- 18
- ISSN:
- 1094-4087; OPEXFF
- Format(s):
- Medium: X Size: Article No. 39117
- Size(s):
- Article No. 39117
- Sponsoring Org:
- National Science Foundation
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