AI/ML Integration for Enhancing Data Assimilation in Weather

Current weather prediction methods integrate observations from various sources with numerical models through data assimilation (DA), forming the basis for more accurate weather forecasts. However, this process is resource-intensive and dimensionally complex. Our study investigated the potential of using AI and machine learning (ML) to optimize data assimilation, aimed to reduce computational demands, address dimensionality challenges, and enhance forecast accuracy. Using ML-DA, we can demonstrate the ability to accelerate data processing by understanding errors and bias that are time- and/or geography-dependent and apply corrections to future forecasts.

As part of the study, we compared between model runs and created new possible data assimilation metrics based on time-difference and other ML algorithms. Such metrics may have broader model utility beyond standard Root Mean Square Error metrics. We developed an AI/ML-informed data assimilation prototype, evaluated the technical infrastructure, and established benchmarks to measure improvements. This research is aimed at harnessing AI in tandem with data assimilation to enhance weather forecasting, making it more efficient and precise.