Autumn 2019 | Ensembles are useful forecast tools because they account for uncertainties in initial conditions, lateral boundary conditions, and/or model physics, and they provide probabilistic information to users. However, many ensembles suffer from under-dispersion, sub-optimal reliability, and systematic biases. Machine learning (ML) can help remedy these shortcomings by post-processing raw ensemble output. Conceptually, ML identifies (nonlinear and linear) patterns in… Read More