Prompt Realistic Inference of Nowcasted Cloud Environments (PRINCE)

To address the need to improve at-sea Naval operations that rely on accurate visibility information, Areté, in collaboration with the University of Alabama in Huntsville (UAH), proposes the development of Prompt Realistic Inference of Nowcasted Cloud Environments (PRINCE), a novel artificial intelligence (AI) framework that enables realistic simulation of cloud and visibility properties at nowcasting timescales (0-24 hours) via intelligent morphing from the numerical weather prediction (NWP) domain to the true environment. Phase I technical objectives include: (1) a regionalized proof-of-concept demonstration of deriving nowcasted cloud parameters, (2) interviewing a relevant stakeholder for feedback on preferred capabilities, (3) a physical validation and verification of model behavior and performance, and (4) presenting a preliminary prototype design for review and discussion. Phase I development of PRINCE will reach Technology Readiness Level (TRL) 3, with Phase I Option achieving TRL 4. A mature PRINCE algorithm would ingest very high-resolution NWP to deliver a robust 4D representation of future cloud environments on the 0-24 hr nowcasting timescale.