Interactive Assessment of Variances of High-Resolution Model Features in Digital Twin Simulations
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2024-11-22
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Citation of Original Publication
Kulkarni, Chhaya, Nikki Privé, and Vandana P. Janeja. “Interactive Assessment of Variances of High-Resolution Model Features in Digital Twin Simulations.” In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, 685–88. SIGSPATIAL ’24. New York, NY, USA: Association for Computing Machinery, 2024. https://doi.org/10.1145/3678717.3691302.
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Attribution 4.0 International
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Abstract
Prior to the deployment of expensive instruments into orbit, spatio-temporal digital twin systems modeling the whole earth are used to study the efficacy of these instruments. However, we need to make sure that the simulated instruments have realistic characteristics (to reflect the physics of the atmosphere and limits of the instrument itself) in order for the results of the digital twin to be robust and usable. If these simulations are done accurately, the instrument can be deployed, leading to more accurate weather forecasts and climate research. This demonstration system validates the simulations, specifically the realism of remotely sensed observations. The digital twin system is a low-cost way to improve instrument design used in meteorological and climatological research. The primary goal is to show how atmospheric data can improve the development and validation of new observational systems for meteorology and climate science. We have developed an interactive variability study system that uses a dynamic platform to visualize, assess, and grasp complex atmospheric dynamics. The dashboard is built using Python for backend operations and integrates tools such as the Streamlit framework for quick web application development and the Folium library for advanced geospatial visualizations. This dashboard acts as a bridge between advanced atmospheric modeling and spatio-temporal digital twin applications, showcasing the substantial benefits of integrating comprehensive model outputs into the simulation of observational systems.