Current State of Artificial Intelligence Exploitation in the AMS Community

The environmental community has long produced a wealth of mission specific observations, estimations, and simulations. Fusion of these sources traditionally occurs within numerical weather prediction frameworks through data-assimilation cycles that provide initial conditions to forecast models. Myriad environmental forecasting applications exist over all scales, but the environmental community as whole has been slower to adopt the application of artificial intelligence (AI) to these problem spaces than other industries (e.g., financial services, retail, etc.). However, a marked increase in AI-based applications that leverage the wealth of data available in the environmental sciences has been occurring over the last two years. This rapid increase in exploitation has been manifesting itself as a jump in AI-related presentations and publications within the AMS community and increased utilization in the operational meteorological domain. This presentation characterizes the increase in AI-based activity in AMS publications and identifies broad research areas that are reaching maturity using AI-based approaches. Additionally, this presentation discusses the catalysts responsible for this increase in activity along with research vectors that can benefit from AI-based data exploitation.