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 observational data, and connects 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 Advanced Information Systems Technology (AIST) program’s Integrated Digital Earth Analysis System (IDEAS) project partners with the Space for Climate Observatory (SCO) (https://www.spaceclimateobservatory.org/) FloodDAM Digital Twin effort led by CNES to establish an extensible open-source framework to develop digital twins of our physical environment for Earth Science with an initial focus on surface water hydrology in Earth’s rivers and lakes. The joint effort delivers an open-source 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. Water resource science is multidisciplinary in nature, and it not only assesses the impact from our changing climate using measurements and modeling, but it also offers opportunities for science-guided, data-driven decision support. The joint effort uses flood prediction and analysis as its primary use case. The work presents a multi-agency joint effort to define and develop a federated Earth System Digital Twin solution between NASA and CNES that powers advanced immersive science and custom user applications for scenario-based analysis.

How to cite: Huang, T. and the NASA AIST IDEAS and SCO FloodDAM Teams: Open-Source Framework For Earth System Digital Twins Applied to Surface Water Hydrology, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10138, https://doi.org/10.5194/egusphere-egu23-10138, 2023.

The notion of digital twin can be ambiguous because it can be defined in various ways. These last months have seen the emergence of many global digital twin initiatives. The challenge of these global digital twins is to create a qualified digital replica model of our planet, making it possible to monitor, simulate and anticipate natural phenomena and human activities. The target users are either scientists or decision makers. Through the digital twin, they have access to a digital representation of an environment using all available spatial and non-spatial data accompanied with a set of physical and statistical models to calculate projections, replay past events or simulate future ones.

Refining and evaluating the accuracy of these projections is a major challenge for digital twins. In addition to the knowledge of physical modeling, suitable data must also be available. Complementary to the global approach, the notion of local and dated digital twins appears then to be essential. Considering a digital representation of a restricted geographical area of interest (an urban area, watershed, coastline, etc.) allows to access to very high-resolution "fresh" data in 2D and 3D, in-situ data and small-mesh physical model. This user-centered and naturally thematic approach responds more finely and more pragmatically to the objectives presented. These local, dated and thematic digital twins are by essence ephemeral: a way to meet a specific need.

The challenge is therefore to setup a Digital Twin Factory (DTF). This DTF relies on a data lake, a high computing capacity via clouds and/or HPC and has thematic algorithms and methodologies able to generate registered and coherent layers of information in order to enrich a datacube from which physical indicators can be computed spatially. Thanks to its thematic, local and on-demand characteristics, the DTF can mitigate the need to have an universal model of metadata. This datacube allows to apply local physical and artificial intelligence models. The overarching architecture of the DTF will be presented. Specific examples on coastal, urban and risk topics will also be presented. These digital twins rely on a large number of expertises in both data and modeling involving various French (CNES, IGN, SHOM, IRD, CEA, INRAE, METEOFRANCE, CERFACS, BRGM, etc.) or international organizations (ESA, NASA, NOAA,…).

For coastal areas, the goal is to well describe the bathymetry topography continuum by taking into account the intertidal zones and the specialized dynamic models together with 3D coastal land cover characterisation. For urban areas, the ambition is first to automatically produce a qualified 3D map together with its additional layers of information: 3D objects and related semantics (land cover and land use) including temporal dynamic, thermal information. Then, for issues related to the management of natural risks (such as floods or fires) similar data layers can be used. Finally, new hypothesis can be injected in these digital replica and multiple scenarios can be applied to assess causal relationship between hypothesis and prediction. Very promising results will also be presented.

How to cite: Delvit, J.-M., Brunet, P.-M., Lassalle, P., Lallement, D., and Baillarin, S.: Towards a local, dated and thematic digital twins factory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13921, https://doi.org/10.5194/egusphere-egu23-13921, 2023.

Due to climate change, we expect an exacerbation of fire in Europe and around the world, with major wildfire events extending to northern latitudes and boreal regions [1]. In this context, it is important to improve our capabilities to anticipate fire danger and understand its driving mechanisms at a global scale. As the earth is an interconnected system, large-scale processes can have an effect on the global climate and fire seasons. For example, extreme fires in Siberia have been linked to previous-year surface moisture conditions and anomalies in the Arctic Oscillation [2]. As part of the ESA-funded project SeasFire (https://seasfire.hua.gr), we gather and harmonize data related to seasonal fire drivers and develop deep learning models that are able to capture spatiotemporal associations with the goal to forecast burned area sizes on a seasonal scale, globally. We publish a global analysis-ready datacube for seasonal fire forecasting for the years 2001-2021 at a spatiotemporal resolution of 0.25 deg x 0.25 deg x 8 days [3]. The datacube includes a combination of variables describing the seasonal fire drivers , namely climate, vegetation, oceanic indices, human factors, land cover and the burned areas. We leverage the availability of big EO data and the advances in Deep Learning modeling [4, 5] to forecast global burned areas, capture the spatio-temporal interactions of the Earth System variables and identify potential teleconnections that determine wildfire regimes under the light of climate change. We present deep learning models that handle the Earth as a system, such as graph neural networks and transformer-based architectures. Applied on the prediction of wildfires at different temporal horizons we reveal that our deep learning models  skillfully predict burned area patterns. Exploring the explanation of the models, we reveal important spatio-temporal links.

Our approach, using AI to model the earth as a system and capture long spatio-temporal interactions, showcases the potential of an application-specific digital twin. The SeasFire datacube can be exploited as a baseline digital twin for modeling different natural hazards, including floods, heatwaves, and droughts. Thus, we will discuss insights and future directions for digital twins in anticipating climate extremes, inspired by our global wildfire prediction paradigm. 

[1] Wu, Chao, et al. "Historical and future global burned area with changing climate and human demography." One Earth 4.4 (2021): 517-530.

[2] Kim, Jin-Soo, et al. "Extensive fires in southeastern Siberian permafrost linked to preceding Arctic Oscillation." Science advances 6.2 (2020): eaax3308.

[3] Alonso, Lazaro, et al. Seasfire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System. Zenodo, 15 July 2022, p., doi:10.5281/zenodo.6834584.

[4] Kondylatos, Spyros et al. “Wildfire Danger Prediction and Understanding with Deep Learning.” Geophysical Research Letters, 2022.  doi: 10.1029/2022GL099368

[5] Prapas, Ioannis et al. “Deep Learning for Global Wildfire Forecasting.” NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning, doi:  10.48550/arXiv.2211.00534

How to cite: Prapas, I., Karasante, I., Ahuja, A., Kondylatos, S., Panagiotou, E., Davalas, C., Alonso, L., Son, R., Dimitrios, M., Carvalhais, N., and Papoutsis, I.: Earth System Deep Learning towards a Global Digital Twin of Wildfires, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5443, https://doi.org/10.5194/egusphere-egu23-5443, 2023.

Ever increasing computing capabilities and crave for high-resolution numerical weather prediction and climate information are specially interesting for the representation of Earth surfaces. Knowledge of accurate and up-to-date surface state for ecosystems such as forest, agriculture, lakes and cities strongly influence the skin temperatures, turbulent latent and sensible heat fluxes providing the lower boundary conditions for energy and moisture availability near the surface. A quick and automatic tool to assess the benefits of updating different surface fields, that makes use of a neural network regression model trained to simulate satellite observed surface skin temperatures, was developed. This tool was deployed to determine the accuracy of several global datasets for lakes, forest, and urban distributions. Comparison results will be shown. The neural network regression model has proven to be useful and easily adaptable to assess unforeseen impacts of ancillary datasets, also detecting erroneous regional areas over the globe, proving to be a valuable support to model development. 

How to cite: Choulga, M., Kimpson, T., Chantry, M., Balsamo, G., Boussetta, S., Dueben, P., and Palmer, T.: Deep Learning for Verification of Earth's surfaces, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8777, https://doi.org/10.5194/egusphere-egu23-8777, 2023.

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data.

FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.

How to cite: Maskey, M., Ramachandran, R., Lee, T., and Ganti, R.: Foundation AI Models for Science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2944, https://doi.org/10.5194/egusphere-egu23-2944, 2023.

Accurate weather predictions are essential for many aspects of social society. Providing a reliable high-resolution precipitation field is essential to capture the finer scale of heavy precipitation events,  which is normally poorly represented in the numerical models. Statistical downscaling is an appealing tool since it is computationally inexpensive. Thus, it has been widely used over the last three decades. In recent years, super-resolution with deep learning has been successfully applied to generate high-resolution from low-resolution images in the computer vision domain. This task is somewhat analogous to downscaling in the meteorological domain.

Inspired by this, we explore the use of deep neural networks with a super-resolution approach for statistical precipitation downscaling. We apply the Swin transformer architecture (SwinIR) as well as convolutional neural network (U-Net) with a Generative Adversarial Network (GAN) and a diffusion component for probabilistic downscaling. We use short-range forecasts from the Integrated Forecast System (IFS) on a regular spherical grid with Δx_IFS=0.1° and map to the high-resolution observation radar data RADKLIM (Δx_RK=0.01°). The neural networks are fed with nine static and dynamic predictors, similar to the study by Harris et al., 2022. All the models are comprehensively evaluated by grid point-level errors as well as error metrics for spatial variability and the generated probability distribution. Our results demonstrate that the Swin Transformer model can improve accuracy with lower computation cost compared to the U-Net architecture.  The GAN and diffusion models both further help the model to capture the strong spatial variability from the observed data.   Our results encourage further development of DNNs that can be potentially leveraged to downscale other challenging Earth system data, such as cloud cover or wind. 

How to cite: Gong, B., Ji, Y., Langguth, M., and Schultz, M.: Statistical downscaling of precipitation with deep neural networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10488, https://doi.org/10.5194/egusphere-egu23-10488, 2023.