Data assimilation

The research group ‘Hydrological Data Assimilation’ utilizes data assimilation (DA) techniques, whose main purpose is to directly integrate information from experimental observations into hydrological models, thus obtaining an optimal estimate of the system state and parameters. DA techniques enable updating model predictions with measured data as soon as they become available, also providing an estimation of errors and uncertainties in simulations.

Among the various DA techniques available, the Ensemble Kalman Filter (EnKF) is of particular interest, thanks to its ability to handle nonlinear problems through a Monte Carlo approach, which involves approximating the statistical distributions of input/output data using hundreds or thousands of parallel simulations. For this reason, EnKF typically requires significant computational resources, both in terms of data storage and the number of CPUs to be used.

The CloudVeneto infrastructure allows us to fully exploit DA techniques and develop cutting-edge scientific tools for solving complex hydrology problems.

Dr. Matteo Camporese