Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks
Abstract We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearized models required by four‐dimensional variational (4D‐Var) data assimilation. Neural networks can be differentiated trivially,...
Main Authors: | Sam Hatfield, Matthew Chantry, Peter Dueben, Philippe Lopez, Alan Geer, Tim Palmer |
---|---|
Format: | Article |
Language: | English |
Published: |
American Geophysical Union (AGU)
2021-09-01
|
Series: | Journal of Advances in Modeling Earth Systems |
Subjects: | |
Online Access: | https://doi.org/10.1029/2021MS002521 |
Similar Items
-
Development of the tangent linear and adjoint models of the MPAS-Atmosphere dynamic core and applications in adjoint relative sensitivity studies
by: Xiaoxu Tian, et al.
Published: (2020-01-01) -
Assessing the tangent linear behaviour of common tracer transport schemes and their use in a linearised atmospheric general circulation model
by: Daniel Holdaway, et al.
Published: (2015-09-01) -
Large-Scale Simulations Using First and Second Order Adjoints with Applications in Data Assimilation
by: Zhang, Lin
Published: (2014) -
Data Assimilation in Fluid Dynamics using Adjoint Optimization
by: Lundvall, Johan
Published: (2007) -
Adjoint and self-adjoint differential operators on graphs
by: Robert Carlson
Published: (1998-02-01)