Pilot Deployment of TERRAHydro: A framework, demonstration, and vision for Earth System Digital Twins

Our project has two intertwined goals in its pursuit of developing the next generation of Earth System Digital Twins (ESDT): 1. Development of the Coupled Reusable Earth System Tensor (CREST) Framework: This AI-first framework provides the infrastructure for constructing, operating, and deploying large community-developed federated ESDTs. It takes a hierarchical graph-based approach to building Earth System Models (ESMs) that enables integration of traditional and AI-based models within the ESDT. Utilizing a tensor-based software (TBS) backend, CREST enables high-performance computing and seamless AI integration, serving as a comprehensive middleware for building and deploying ESDTs. 2. Implementation of the Terrestrial Environmental Rapid-Replicating and Assimilation Hydrometeorological (TERRAHydro) AI-based Land Surface Digital Twin: Leveraging CREST, TERRAHydro combines the latest in data-driven hydrology to create an advanced land, vegetation, and water digital twin. Its capabilities include performing rapid recalibration, counterfactuals, and extensive scenario analysis, thereby addressing critical What-Now, What-Next, and What-If questions, and showcasing the value of the proposed technology. The currently funded grant (21-AIST21-0003) will produce infrastructure for building, training, and testing the TERRAHydro Earth Systems Model or digital replica, as well as forecasting and data assimilation capabilities. In addition, a final third-year full-scale demonstration will be given. To achieve this, a variety of CREST infrastructure is being produced under the current grant such as data management, model specification and building, inference, and archiving. However, a significant amount of deployment and interoperability capabilities for targeting operational settings still needs to be developed. The thrust of this proposal and award is to develop deployment and interoperability capabilities, culminating in the deployment of the TERRAHydro pilot system on the NASA Science Managed Cloud Environment (SMCE). This includes a control and monitoring system, impact and assessment capabilities, and a web-based front-end accessible through a user portal. Interoperability -- or the ability to integrate a larger class of models -- is critical for federation and wider adoption of an ESDT framework. To address this, current interoperability in CREST will be extended to include AI models written in different tensor-based languages, as well as traditional models (e.g., Fortran-based). For the former, we will expand the current infrastructure to allow seamless integration of models written in TensorFlow, PyTorch, and JAX by leveraging existing technology (e.g., Open Neural Network Exchange) to automatically detect and translate models appropriately. For the latter, we will explore two options, with the success and viability being demonstrated using TERRAHydro: 1. Re-writing the model in JAX: This comes at the cost of rewriting models but provides auto-differentiation and seamless integration within the current framework. As a low-risk approach to interoperability, it offers a demonstration of the benefits that come with tightly coupling process-based and data-driven models -- but requires higher effort on the part of end users. 2. Black-box gradient estimates: This will investigate approaches to gradient estimates of black boxes, and test their overall effectiveness. While higher risk, if successful this approach will require virtually no integration efforts from end users, but will come at the cost of an approximate gradient and differing performance profiles than what can be achieved from a full model rewrite. Our vision for CREST is to allow the creation of always-online systems continuously ingesting data and updating its state, and accessible through a web-browser and interactive clickable front-end.

Expand current definitions of modeling and leverage state-of-the-art computer and information science for innovating advanced modeling techniques as well as new technologies and frameworks that will be essential in the development of Earth System Digital Twins

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