This contribution presents the first multivariate deep learning frameworks for jointly forecasting four tropospheric parameters using nine long-sequence architectures representing Transformer, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multi-Layer Perceptron (MLP) model families. Models were trained on seven years of hourly observations from 501 globally distributed stations with a 96-hour input an...