HAL (France)
open access
A comparison of data-driven and physics-informed statistical learning models for atmospheric remote sensing applications
Accurate models of gas radiation are required for the analysis of atmospheric remote sensing data. These models need to be CPU efficient in order to comply with the exponential growth of data provided by the most recent generations of satellite instruments. In this paper, we describe two met
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