Unmasking Compensating Errors in Numerical Weather Models: A Process-Additive Neural Network Approach

This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PANN branch represents the tendency error