Data Assimilation Research Team Events / Media

University College London

Eruptions that occur at volcanoes after periods of quiescence are difficult to forecast. Pathways that connect the source to the surface may have become sealed. The pressurisation of the source leads to the deformation of the crust. Initially the crust deforms elastically, strain is accommodated via ground movement and elastic strain energy is stored to the crust. Then, the deformation transitions to inelastic where strain is accommodated via brittle failure (volcano-tectonic event), and elastic strain energy is transferred from the crust.

Finally, we demonstrate how our method, along with the understanding of eruption precursors gained from the results, can be used to constrain deformation regimes at reawakening volcanoes after extended repose and to evaluate the hazard posed during periods of unrest.

Estimating and forecasting precipitation is essential for a wide range of human activities as well as for disaster prevention. In this talk we will discuss the application of deep neural networks to the estimation of precipitation with high time and spatial resolution, combining remote sensors and numerical weather predictions. The proposed models show that these information sources can be effectively combined to improve the accuracy of real-time precipitation estimates. Additionally, we will present the application of deep neural networks as a postprocessing tool for short-range deterministic and ensemble-based numerical weather predictions and for the quantification of their uncertainty. The performance of the machine-learning models in the quantification of the uncertainty is close to that achieved by the dynamical ensembles and can be even better in the presence of a model.