Towards probabilistic end-to-end Deep Learning Weather Forecasting: Spatio-Temporal Temperature Forecasting using Normalizing Flows

Forecasting of climate variables has been a long-standing problem in the community. There exist many Numerical Weather Prediction (NWP) methods for computing weather forecasts. The main disadvantages of NWPs are their slow convergence time and high computational cost. Recently, a Deep Generative-based model (DGM) outperformed SOTA NWP predictions on precipitation nowcasting, not only in terms of quantifiable scores and convergence time, but also in a qualitative study conducted among meteorologists. In contrast to deterministic forecasting models, DGMs allow for uncertainty estimates from ensembles of future predictions. In this work, we use Conditional Autoregressive Normalizing Flows (CANFs) [1, 2] for forecasting temperature frames from the ERA5 dataset. We motivate the use of Normalizing Flows over GANs due to their advantages in training stability, invertibility and convergence time.