[M-GI26] Data assimilation: A fundamental approach in geosciences

[MGI26-P02] Using Data Assimilation to Improve Data-Driven Models

*Michael Goodliff¹, Takemasa Miyoshi¹ (1.RIKEN Center for Computational Science)

Keywords:Data Assimilation, Machine Learning, Data-Driven Models

Data-driven models (DDMs) are mathematical, statistical, 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, trends, and other statistical relationships. In areas such as numerical weather predictions (NWP), these DDMs are becoming increasingly popular with an aim to replace numerical models based on reanalysis data. 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.