Continuous Assimilation Weather Forecasts (CAWF)

Traditional data assimilation (DA) systems, which are essential for initializing numerical weather prediction (NWP) models, discard nearly 90% of available observational data due to computational constraints and limitations in physical models such as radiative transfer models (RTMs) and background error covariance (BEC) formulations. This results in suboptimal initial conditions (ICs), particularly in sparse observational environments such as the open ocean, where Navy operations rely heavily on satellite-based measurements. While machine learning weather prediction (MLWP) models have demonstrated significant improvements in forecast efficiency, they currently lack compatible DA frameworks that can fully leverage diverse and irregularly distributed observations.ÝThis project proposes a modular, ML-driven approach to enhance DA systems by replacing key computational bottlenecks with ML-based components. Specifically, we will focus on ML-driven improvements to BEC estimation and satellite retrievals, two of the most computationally expensive and limiting factors in traditional DA. By accelerating these processes, we aim to increase the volume of assimilated data while improving the accuracy of ICs. Additionally, we will explore methods for integrating raw satellite radiance data directly into DA systems, bypassing simplified RTMs to enable all-sky assimilation, including cloud-affected observations.ÝPhase I will assess the feasibility of ML-based DA components through literature review, prototype development, and performance evaluation against existing DA benchmarks, such as ECMWF analyses. We will develop rapid prototype proof-of-concept models to test the computational efficiency and accuracy of ML-driven BEC and retrieval components, ensuring compatibility with both traditional NWP and emerging MLWP models. The long-term objective is to lay the foundation for the Continuous Assimilation Weather Forecast (CAWF) system, a next-generation DA framework capable of assimilating data as it becomes available rather than integrating over fixed time windows.ÝThis research directly supports the Office of Naval Research (ONR) by advancing DA capabilities for operational forecasting in data-sparse environments. The outcomes will enhance the Navyís ability to generate more accurate, timely, and high-resolution forecasts, improving decision-making for mission-critical operations in the maritime domain. The proposed ML-driven DA framework offers a scalable and computationally efficient solution that aligns with the future of AI-driven weather prediction.