Quantification of Shortwave Surface Albedo Feedback Using a Neural Network Approach
Radiative transfer is a nonlinear process. Despite this, most current methods to evaluate radiative feedback, such as the kernel method, rely on linear assumptions. Neural network (NN) models can emulate nonlinear radiative transfer due to their structure and activation functions. This study aims to...
Main Authors: | Diana Laura Diaz Garcia, Yi Huang |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-01-01
|
Series: | Atmosphere |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4433/15/2/150 |
Similar Items
-
Temporal Variation of the Shortwave Spherical Albedo of the Earth
by: A. Penttilä, et al.
Published: (2022-03-01) -
A new approach for simultaneously retrieving cloud albedo and cloud fraction from surface-based shortwave radiation measurements
by: Yu Xie, et al.
Published: (2013-01-01) -
Efficacy of black carbon aerosols: the role of shortwave cloud feedback
by: Angshuman Modak, et al.
Published: (2019-01-01) -
Snow Surface Albedo Sensitivity to Black Carbon: Radiative Transfer Modelling
by: Nicholas D. Beres, et al.
Published: (2020-10-01) -
Climate Sensitivity and Feedback of a New Coupled Model (K-ACE) to Idealized CO<sub>2</sub> Forcing
by: Min-Ah Sun, et al.
Published: (2020-11-01)