An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observations, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions.Since 2021, NASA’s Advanced Information Systems Technology (AIST) program has invested in two ESDT efforts to tackle the impacts of our changing climate. The establishment of ESDT for flood and air quality enabled our teams to formalize the software framework. The open-source framework is called the Integrated Digital Earth Analysis System (IDEAS). By working with the Apache Science Data Analytics Platform (SDAP) community, IDEAS is now a subproject of SDAP. The paper presents the ongoing development of IDEAS and its current applications.

As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Over the past 20 years, more than 2 billion people have been exposed to consequences of fluvial floods. Flood detection, rapid mapping and risk assessment products play an important role in flood emergency response and management. Within this context, FloodDAM-Digital Twin is a pre-operational prototype which provides an automated service to reliably detect, monitor, assess and predict floods at local and global scale within digital twin Franco-American collaboration. At the end of the project, a proof of concept demonstration will be realized over French and USA selected catchments. This prototype could be commercialized for both public and private entities in the field of water management and risk prevention. The work presented in this paper relies on scientific improvements for each product and services as well as on the digital Twin architecture that allows interoperability with other systems.

Floods are the most common natural disasters all over the world and they are increasing in frequency and intensity due to climate changes. The Space for Climate Observatory (SCO)-FloodDAM-DT project with a joint collaboration effort between CNES, NASA’s partners and JPL is devoted to developing a federated Earth System Digital Twin (ESDT) for water-cycle applications focused on flood events. In particular, SCO-FloodDAM-DT project aims to provide an automated pre-operational service to reliably detect, monitor and assess floods at global scale within digital twin collaboration with NASA/JPL. The main objective is to connect data and existing models from both agencies in order to combine multi-scale simulations taking into account multiple phases of an entire flood event, from early alerts to post-event impact assessments. At the end, a proof-of-concept demonstration, planned after 18 months, will be presented with its multi-scale aspect over French and USA selected catchments.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA's Advanced Information Systems Technology (AIST)'s Integrated Digital Earth Analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others; for driving models with observation data; and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)'s FloodDAM project and the Centre National d'Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA's Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Floods are the most common natural disasters all over the world. The space climate observatory (SCO)-FloodDAM project aims at utilizing the capabilities of new observing strategies in order to better alert, detect and map flood events globally. Leveraging from both aerial- and satellite-based platforms (Sentinel, TerraSar-X, SWOT) as well as in-situ based sensors, the main objective of the project is to develop an automatic system to better prevent flood events and assess their consequences. In this paper, we will demonstrate the strategy deployed for the selected test-sites in France.

As Alvin Toffler had eloquently put it "You've got to think about big things while you're doing small things, so that all the small things go in the right direction." [6] We have a long history of building many innovative solutions. With a quick search on the web, we can find various tools that offer similar capabilities such as search, visualization, subsetting, analysis, etc. The community is very good with building domain-specific solutions for specific applications. The lack of cohesiveness among these tools introduces technology gaps, which lead to even more stovepipe solutions. An Analytics Center Framework is an architectural concept to establish an extensible, reusable software framework for specific research communities. This paper discusses the application of an open source data analytics framework NASA is developing through its Advanced Information Systems Technology (AIST) program to improve estimating ocean circulation modeling product generation and analysis.

NEXUS is a software project developed by the NASA Jet Propulsion Laboratory which aims to enable scientific analysis of large datasets collected by various NASA missions. Historically, data analysis required an analyst to move the data to the system computing the analysis. As the volume of data available increases, the storage, CPU, and memory requirements for this type of traditional analysis increases to a point that makes it impractical. The NEXUS approach is to remove this data movement, transform the data into a format convenient for analysis, and provide analytic functions that can be massively parallelized.