Work

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

Year: 2021

Type: article

Abstract: Modeling the effect of subgrid-scale processes is one of the main obstacles in the accurate prediction of multiscale systems. An investigation considers how machine learning methods can be applied to model subgrid-scale processes and integrated within sequential data assimilation methods. It is found that the use of machine-learning-based closure modeling in conjunction with data assimilation improves the prediction of multiscale systems and can be considered a promising approach to numerical weather prediction tasks in the age of data.

Source: Physical Review Fluids

DOI: 10.1103/physrevfluids.6.050501

Authors: Suraj Pawar, Omer San

Institution: Oklahoma State University

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research