Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks. (23rd September 2021)

• Record Type: Journal Article
• Authors: Hatfield, Sam; Chantry, Matthew; Dueben, Peter; Lopez, Philippe; Geer, Alan; Palmer, Tim
• Is Part Of: Journal of advances in modeling earth systems. Volume 13:Number 9(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-23
• Subjects: neural network -- data assimilation -- tangent‐linear -- adjoint; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002521
• Languages: English
• ISSNs: 1942-2466

Abstract

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, and so if a physical parametrization scheme can be accurately emulated by a neural network then its tangent‐linear and adjoint versions can be obtained with minimal effort, compared with the standard paradigms of manual or automatic differentiation of the model code. Here we apply this idea by emulating the non‐orographic gravity wave drag parametrization scheme in an atmospheric model with a neural network, and deriving its tangent‐linear and adjoint models. We demonstrate that these neural network‐derived tangent‐linear and adjoint models not only pass the standard consistency tests but also can be used successfully to do 4D‐Var data assimilation. This technique holds the promise of significantly easing maintenance of tangent‐linear and adjoint codes in weather forecasting centers, if accurate neural network emulators can be constructed. Plain Language Summary: The neural network is an algorithm developed in the field of artificial intelligence that can in principle learn the relationship between any two variables, provided you give it enough real‐world data. There are countless applications for such an algorithm in the field of weather and climate simulation. The application that we focus on here is to use the neural network as a replacement for one component of a weather simulation. Essentially, you train the neural network so that it can accurately emulate the component that it replaces. For expensive components, using the neural network emulator instead of the original can provide a significant computational saving. Other studies have already demonstrated that this technique can be applied in weather simulations. What we show here, however, is that neural networks can also be used to easily and automatically calculate the slope of the line relating the two variables in question, through a slight modification of the network. This is an essential procedure for constructing the initial conditions for weather forecasts through a process known as data assimilation. Key Points: Neural network emulators of physical parametrization schemes can be used to easily construct tangent‐linear and adjoint models These neural network‐based linear models are potentially much easier to maintain compared with the traditional approach We test these tangent‐linear and adjoint models in data assimilation experiments in a state‐of‐the‐art weather forecasting model