Spatiotemporal forecasting in earth system science: Methods, uncertainties, predictability and future directions

Spatiotemporal forecasting (STF) extends traditional time series forecasting or spatial interpolation problem to space and time dimensions. Here, we review the statistical, physical and artificial intelligence (AI) methods, data and model uncertainties, predictability and future directions for STF problems. Statistical STF methods have limitations in high-level feature extractions and long-term memory modeling. Physical models are computationally intensive and are imperfect in model structure and parameterization. AI models lack the interpretability and require elaborate training but can model complex nonlinear and non-Gaussian problems. Integrating data-driven and physical model-driven methods could facilitate the improvement of interpretability and forecasting accuracy. The predictive uncertainty comes from data and models, which could be measured by probability distribution and Bayesian inference, respectively. The predictive uncertainty is generally missing in AI models and could be resolved by incorporating Bayesian frameworks. The predictability of dynamic earth systems is spatiotemporally heterogeneous and is generally examined by diagnostic and prognostic approaches. Diagnostic methods analyze the predictability empirically from a theoretical perspective, while prognostic methods investigate the predictability through real experiments. Unraveling the predictability in space and time and the predictability sources will greatly improve earth system understanding and operational forecasting development. Current STF systems are largely not user-friendly to provide probabilistic and understandable forecasting services in near real-time. Intelligent STF systems should automatically prepare various data sources, train the models in a self-adaptative way and provide timely predictive information services for users to make decisions. This review provides state-of-the-art advances in forecasting sciences and highlights new directions for new-generation STF systems. •Review of methods, uncertainties and predictability of spatiotemporal forecasting.•Integration of artificial intelligence and physical model to improve forecasting.•Probabilistic modeling of forecasting uncertainty by Bayesian methods.•Development of intelligent spatiotemporal forecasting systems for decision-making.