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A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing

Earth Sciences

A machine learning paradigm for necessary observations to reduce uncertainties in aerosol climate forcing

J. Redemann and L. Gao

Uncertainties in quantifying climate cooling due to anthropogenic aerosols can be addressed using an innovative machine learning approach by Jens Redemann and Lan Gao. This study demonstrates the potential of advanced neural networks to enhance aerosol property estimations, fundamentally improving conventional methods and promising greater accuracy in climate modeling.

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