Main Subcommittee Category
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IndustryAbstract
Weather simulation represents a critical factor affecting the course and outcome of battles in wargaming. Adverse weather conditions, such as precipitation or fog, can limit visibility or degrade weapon accuracy. On the other hand, favorable weather conditions can significantly impact military operations, offering several advantages that enhance overall effectiveness and success. In the context of wargaming, designers create more realistic training scenarios by incorporating weather simulation. The weather simulation model currently being implemented within the USMC Wargaming System consists of two main capabilities: dynamic weather simulation and the ability to apply historical weather data. While the purpose of dynamic weather simulation is to produce a synthetic weather environment, historical weather data can be used to reproduce weather conditions from past conflicts, allowing wargame designers to create more realistic scenarios for training and analysis. During the scenario design phase, designers review the available historical weather data to determine if it is appropriate for the anticipated wargame scenario. They may want to know, “What will be the temperatures for the next seven days?”, or “What is the expectation of precipitation for the timeframe of the wargame?”. To do so, the designers employ the tools at their disposal to display and examine the weather data. A generative AI system for weather simulation will significantly enhance this task.
This paper explores the key components and considerations in developing such a generative AI system. The foundation of the proposed generative AI system lies in the utilization of a comprehensive weather ontology that captures the interrelationships and dependencies among various weather-related variables. It encompasses meteorological parameters, geographic features, and atmospheric phenomena, providing a rich semantic understanding of the wargaming domain. Finally, the paper will explain how the newly developed weather ontology will be incorporated into the system architecture of a prompt-based generative AI for weather simulation.