In recent years, video prediction methods by deep learning have demonstrated success in computer vision applications, such as self-driving cars, human action prediction, and gesture recognition. The weather and climate communities are beginning to investigate the use of these advanced methods in the context of weather and climate forecasting (Reichstein, M., et al., 2019). Current weather forecasting systems rely on numerical weather prediction models which divide the earth system into grid boxes and numerically solve complex differential equations describing the temporal evolution of the system in terms of momentum, energy and mass. While these models have reached remarkable quality in many aspects, high resolution simulations in space and time require huge computational resources (Zängl, Günther, et al. 2015). In addition to errors inherent from observational data, which are used for the initialization, discretization errors and the need of parametrization schemes for a variety of physical processes diminish the accuracy of numerical weather predictions. Two specific examples in this context are clouds and precipitation. Deep learning models promise to discover non-linear spatio-temporal properties from heterogeneous weather observations in a data-driven way and are, once these models have been trained, computationally much cheaper.
There are certain similarities between the deep learning tasks of video prediction and weather forecasting. Among others, they both explore spatio-temporal patterns from previously observed data to generate (i.e. forecast) the future frames. This work, then, explores the generalization capability and adaptation of the video prediction method and uses a GAN-based architecture, such as the stochastic adversarial video prediction (SAVP) (Figure 7) when applied to a weather dataset containing temperature, pressure, and geopotential, including a comparison of transfer learning and end-to-end training.
Lee, A.X., Zhang, R., Ebert, F., Abbeel, P., Finn, C., Levine, S.: Stochastic adversarial video prediction. arXiv preprint arXiv:1804.01523 (2018)
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., et al.: Deep learning and process understanding for data-driven earth system science. Nature 566(7743) (2019) 195–204
Zängl, Günther, et al. “The ICON (ICOsahedral Non‐hydrostatic) modelling framework of DWD and MPI‐M: Description of the non‐hydrostatic dynamical core.” Quarterly Journal of the Royal Meteorological Society 141.687 (2015): 563-579.