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Chapter 12

Challenges and future perspectives in ocean prediction

CHAPTER
COORDINATORS
Fraser Davidson
CHAPTER
AUTHORS
Enrique Alvarez Fanjul, Alain Arnaud, Stefania Ciliberti, Marie Drevillon, Ronan Fablet, Yosuke Fujii, Isabel Garcia-Hermosa, Stéphanie Guinehut, Emma Heslop, Villy Kourafalou, Julien Le Sommer, Matt Martin, Andrew M. Moore, Nadia Pinardi, Elizabeth Remy, Paul Sandery, Jun She, Marcos G. Sotillo, and Joaquin Tintorè

12.6 Opportunities of artificial intelligence for ocean forecasting systems

Recent developments in AI open many interesting opportunities in the context of operational oceanography and ocean forecasting systems. Operational forecasting systems are indeed not only based on observational data but also on algorithms. These algorithms gather and encode our understanding of physical systems and their dynamics, as well as of observation networks and associated uncertainties. They also reflect our collective knowledge on the relevant criteria for evaluating ocean data products. As in many activities relying on algorithms, the emergence of artificial intelligence, and especially of deep learning, opens a number of new possibilities, and is therefore the subject of growing interest in our community.

The ML generally refers to all the methods used to build algorithms whose components and parameters are not defined a priori but are trained according to a given objective. This field encompasses a large number of different methods, algorithms, and training strategies. It is a wide and fast-moving research field that includes, but is not restricted to, deep learning. ML is also intimately linked to a technological landscape and a software ecosystem in constant evolution. These technologies allow researchers and engineers to assemble complex algorithms from elementary building blocks in a very versatile and modular way, with interesting performances compared to state-of-the-art methods in many disciplines.

Applications of artificial intelligence are currently in vogue but, beyond the hype, artificial intelligence and machine learning can help us to overcome some of the current limitations of ocean forecasting systems. Ocean models and data assimilation methods, which are the scientific underpinning of current ocean forecasting systems, are indeed facing important challenges. Performing large ensemble simulations with full ocean models at increasingly fine spatial resolution is becoming more and more difficult computationally. We still do not know how to fully exploit hybrid computing architectures in our systems. We do not have a robust and plug-and-play framework to adapt their complexity to new custom applications. Although they are constantly being improved, our systems are also becoming increasingly difficult to modify and maintain. As developed in the following subsections, AI and ML may well help us to overcome these limitations and may even deeply impact on the structure of our operational systems.

12.6.1 Expected contributions of machine learning to ocean forecasting pipelines

Machine learning has long been used in ocean sciences and operational oceanography. However, these applications have so far mostly been limited to data retrieval algorithms upstream of forecasting systems (remote sensing, quality control), or to data processing and analysis in downstream applications (data mining, data fusion). In this context, ML algorithms have been essentially seen as black boxes without much physical basis. This perception is fundamentally renewed with the emergence of physics based machine learning and differentiable programming, which now allow to bridge physical sciences, scientific computing, uncertainty quantification, and machine learning (Carleo et al., 2019).

If we adopt a data-centric viewpoint, ocean forecasting systems can indeed be described as a succession of independent data processing steps in sequential pipelines (see Figure 4.1). These pipelines include the collection of past observational data, data-assimilation to reconstruct the current state of the ocean, forecasting with a physics-based model, and eventually the post-processing and dissemination to users. Data is being processed with algorithms at each step of the pipelines. It is now obvious that modern machine learning has the potential to impact each step of the data-processing pipelines of operational oceanography and ocean forecasting systems.

As mentioned above, many applications can be identified upstream or downstream of the core engines of ocean forecasting systems. Typical applications of ML upstream of core engines include, for instance, algorithms for alleviating observational noise, for retrieving parameters (Malmgren-Hansen, 2021), or for data quality control (Castelão, 2021). ML can thus be used for detecting outliers in Argo profiles (Maze et al., 2017). The range of possible downstream uses of core forecasting engines is even wider. ML is here expected to help design tailored services addressing key challenges (Persello et al., 2022), such as improving the prediction of Lagrangian drift or detecting anomalous extreme events.

However, what is probably more difficult to perceive is how machine learning may soon affect the core engine of ocean forecasting systems, and eventually all the services to users. Machine learning and differentiable programming are indeed opening many opportunities in computational fluid dynamics (Vinuesa and Brunton, 2021), while deeply renewing inverse methods in many areas (Cranmer et al., 2020). These recent advances could be leveraged for improving ocean models, e.g. for better accounting for unresolved processes (Brunton et al., 2020; Zanna and Bolton, 2021). They could also help improve data assimilation schemes (Bonavita and Laloyaux, 2020), or even possibly replace full inversion pipelines (Fablet et al., 2021). 

These recent advances open the possibility to design and train our core forecasting engines in such a way that their complexity and performance could be optimised for specific applications, ultimately improving our ability to meet the diversity of user needs.

12.6.2 Designing fully trainable ocean forecasting systems core engines

The core engines of current ocean forecasting systems are based on two types of objects that are still quite independent, namely ocean circulation models and data assimilation methods. Ocean models, data assimilation methods, and their implementation in forecasting systems are being continuously improved. But our core forecasting engines are still rather static in their design and structure, due to technological, organisational and historical reasons. For instance, ocean models are generally developed without taking into account how they will be implemented with data assimilation. As such, there is no guarantee of the optimality of the overall design of our systems and its fit for purpose in specific contexts.

Recent developments at the interface of machine learning and scientific computing could open the possibility of optimising the design of our core prediction engines according to predefined objectives. Indeed, beyond the improvements of specific components of ocean models or data assimilation schemes, the real benefit to be expected from machine learning in forecasting systems is the ability to optimise entire pipelines with end-to-end strategies. The term end-to-end here refers to the ability to optimise components of processing pipelines based on metrics measuring the performance of the entire pipeline. End-to-end strategies may eventually allow the design of fit for purpose and user-centric processing chains and products.

There are obviously technological conditions to realise this potential. Integrating trainable components in core forecasting engines is indeed greatly facilitated if these engines are already composed of independent modules with robust and stable interfaces. It is therefore necessary a gradual evolution to make the system more modular and composable. Moreover, if we want to take advantage of end-to-end strategies, the core engines should be fully differentiable. This would allow to back-propagate a misfit in the prediction into an increment in the parameters of the engine. This is only possible if the core engine is written in a high-level differentiable language or programming framework.

Such prerequisites may at first appear daunting, but a gradual evolution towards modular, composable, and differentiable core engines would also have important side benefits. First, this effort to redesign our core engines, may actually provide a viable strategy for exploiting upcoming computing architectures, starting from GPUs (Kochkov et al., 2021). It may also simplify the maintenance of our engines, as for instance the development of adjoint models (Hatfield et al., 2021), therefore speeding up the transfer from research to operation (R2O). Another benefit is also the built-in treatment of uncertainties, thanks to recent advances in probabilistic programming (van de Meent et al., 2021) and Bayesian Machine Learning 🔗6 .

12.6.3 Towards user-centric, ocean digital twins leveraging lightweight emulators

Looking further ahead, it can be guessed what future digital twins of the ocean will eventually look like. The integration of AI components may indeed gradually change the underlying paradigm of ocean forecasting systems. While current systems essentially implement “single-core engines” with a predefined level of complexity, future systems may be based on collections of core engines, tailored to the specific needs of particular users. These tailored core engines would instantiate core methods and building blocks in a versatile and user-centric way, providing fit for purpose tools and products to users.

Whatever form digital twins will eventually take, a key methodology will be the ability to train emulators of existing systems at reduced costs and with controlled complexity. As described above, a gradual evolution of our core forecasting engines will be needed for leveraging the full potential of AI and ML. This transition may in particular leverage DDEs. They provide approximations of pre-existing algorithms (Kasim et al., 2021) and can be integrated in data assimilation schemes (Nonnenmacher and Greenberg, 2021). As such, DDEs offer a good solution for building upon existing expertise and tools, while benefiting from the pace of scientific and technological advances in AI.

In conclusion, it appears that we are at the beginning of an exciting phase in the evolution of ocean forecasting systems, which could deeply transform the entire service offered to users. The integration of AI in ocean forecasting systems will require a gradual but profound change of the algorithms that constitute their underpinnings. This transition will take advantage of the wealth of expertise on ocean physics, observing networks, and user needs available in ocean forecasting centres. It will also require developing and nurturing new collaborations with the broader AI technological and scientific community, and benefit from the adoption of open science practices.

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Chapter 12

Challenges and future perspectives in ocean prediction

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