DTAS: A prototype Digital Twin of Air-Sea interactions (DTAS)

Boundary layer interactions between oceanic and atmospheric surfaces are essential for predicting long-term climatic changes and the increasing occurrence of extreme weather events. These exchanges are critical indicators of climatic changes, especially regarding floods, droughts, storms, and hurricanes, and are increasingly studied to better understand extreme weather events. However, they are also a significant source of uncertainty in climate models as they are notoriously hard to directly observe and often involve expensive instrumentation, which introduces scaling difficulties. Traditionally, modelers have used various kinds of numerical physics-based parameterizations to understand these linkages. While these are helpful, their computational and memory needs make it inefficient to incorporate advanced parameterizations into larger climate models or run uncertainty quantification analyses. Moreover, these models tend to be one-directional, as they are generally initialized with a boundary layer condition to assess the outcome. In this research project, we propose to develop a hybrid physics-informed artificial intelligence model that ingests several existing flux estimates and observational data products to train against flux estimates computed from measurements collected by Saildrones, state-of-the-art autonomous platforms for simultaneous ocean-atmosphere observation. Hybrid models have been increasingly used in several domains as they can remarkably decrease the computational effort and data requirements. While data-driven models can handle high-dimensional complex systems and provide rapid inference, they are often considered ''black box'' models with poor interpretability, and they typically do not extrapolate well beyond the training data. Our novel hybrid approach of combining physics-based models with neural networks will overcome these deficiencies. It will provide rapid scalability and fast inference of data-driven models and has the advantages of traditional numerical physics-based models regarding data efficiency, interpretability, and generalizability. We will use the hybrid model for two primary purposes: (1) to ascertain the spatiotemporal uncertainty of existing flux measurements compared to those computed from Saildrone observations; and (2) to find the possible combinations of near-real-time data of existing flux products (satellite-based and reanalysis) and observational data of oceanic and atmospheric variables (remotely-sensed and in situ)) to obtain the best estimates for a given spatiotemporal slice. The near-real-time aspect of the hybrid model enables the development of a ''Digital Twin" of the boundary layer air-sea interactions. We will complete the framework with a front-end visual analysis system, which lets the user perform several actions: 1) identify the possibility space of future predictions based on a set of parameter choices (''what-if" investigations); 2) identify the parameter sweep of initial conditions for a given future prediction; and 3) perform sensitivity analysis of parameters for different scenarios. The model developed in this research investigation will focus on the Gulf Stream region. However, this is the first step towards building a Digital Twin for the Planetary Boundary Layer, which would be game-changing for scientists and decision-makers looking to advance our understanding of weather and climatic changes, to better forecast extreme weather events such as floods, hurricanes, and marine heatwaves, and to manage better and mitigate changes to ocean ecosystems.

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