Abstract
MELTED - MachinE Learning for arcTic ice prEDiction. The primary goal is to develop a hybrid physics–data driven models for sea ice prediction. In this approach, machine learning is used to learn model error parameterizations by leveraging data assimilation outputs (analyses), which serve as an online, ML-based, state-dependent bias correction to the physical model core. Risultati Attesi: "1. Develpment of an hybrid physics-ML model based on the 1D column-physics model Icepack, using observing system simulation experiments (OSSEs). 1.1 Detailed investigation of the effect of different parametric model errors on forecasts and correspondent ML bias corrections. 1.2 Evaluation of the hybrid models' performance on long lead-time forecasts and assessment of its robustness under perturbed forcing conditions. 1.4 Execution of novel transfer learning experiments, examining in detail the capabilities and conditions allowing for an ML model optimized to bias-correct a given physical model to be successfully applied to another physical model. 2. Training of a 2D convolutional neural network to predict sea ice concentration and thickness analysis increments from the CMCC Global Ocean Physical Reanalysis System, based on the NEMO-CICE coupled ocean-sea ice model. 2.1 Investigation of transfer learning strategies to effectively use a dataset that is not statistically homogeneous, due to changes in the assimilated sea ice observations. 2.2 Development of a NEMO-CICE-NN hybrid model."
Project details
Unibo Team Leader: Natale Alberto Carrassi
Unibo involved Department/s:
Dipartimento di Fisica e Astronomia "Augusto Righi"
Coordinator:
Politecnico di MILANO(Italy)
Total Unibo Contribution: Euro (EUR) 81.486,00
Project Duration in months: 24
Start Date:
28/09/2023
End Date:
28/02/2026
