Traditional weather prediction models rely on solving complex physical equations, with long computation time. Machine learning models can process large amount of data more quickly. We propose to use neural processes (NPs) for short-term weather attributes forecasting. This is a novel avenue of research, as previous work has focused on NPs for long-term forecasting. We compare a multi-task neural process (MTNP) to an ensemble of independent single-task NPs (STNP) and to an ensemble of Gaussian processes (GPs). We use time series data for multiple weather attributes from Chichester Harbour over a one-week period. We evaluate performance in terms of NLL and MSE with 2-hours and 6-hours time horizons. When limited context information is provided, the MTNP leverages inter-task knowledge and outperforms the STNP. The STNP outperforms both the MTNP and the GPs ensemble when a sufficient, but not exceeding, amount of context information is provided.