[M-GI24] Data assimilation: A fundamental approach in geosciences

[MGI24-02] Advancing Forecast Precision: Data-Driven Model Generation via Data Assimilation

★Invited Papers

*Michael Goodliff^1,2, Takemasa Miyoshi^1,2,3 (1.RIKEN Center for Computational Science, 2.RIKEN Cluster for Pioneering Research, 3.RIKEN interdisciplinary Theoretical and Mathematical Sciences (iTHEMS))

Keywords:Data Assimilation, Data-driven modelling, Machine Learning

Data-driven models (DDMs) are mathematical or computational models built upon data, where patterns, relationships, or predictions are derived directly from the available information rather than through explicit instructions or rules defined by humans. These models are constructed by analysing large volumes of data to identify patterns, correlations, and trends. In areas such as numerical weather predictions (NWP), these DDMs are becoming increasingly popular with an aim to replace numerical models (or components of) based on real observations. Data assimilation (DA) is a process which combines observations from various sources with numerical models to improve the accuracy of predictions or simulations of a system's behaviour.

This presentation focuses on the application of DA methodologies in enhancing the precision and efficiency of DDM generation within computation models characterised by inherent observation error. The aim is to demonstrate the pivotal role that DA techniques can play in refining and optimising the process of DDM generation, thereby augmenting the accuracy and reliability of predictive models despite the presence of observational uncertainties.