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

ABSTRACT: Uncertainties in estimates of climate cooling by anthropogenic aerosols have not decreased significantly in the last two decades, partly because observational constraints on crucial aerosol properties simulated in Earth System Models are insufficient. To help address this insufficiency in aerosol observations, we describe a paradigm for deriving higher-level aerosol properties with machine learning algorithms that use only lidar observations and reanalysis data as predictors. Our paradigm employs high-accuracy suborbital lidar and collocated in situ measurements to train and test two fully-connected neural network algorithms. We use two lidar data sets as input to our machine learning algorithms. The first data set consists of suborbital lidar observations not previously used in the traini

SUBMITTER: Redemann J 

PROVIDER: S-EPMC11437084 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature