Observation-driven correction of numerical weather prediction for marine winds

2025-12-03

AI Summary

Marine wind forecasting suffers from sparse, heterogeneous, and dynamically evolving observations, hindering navigation safety and offshore energy operations. To address this, we propose a Transformer-based error correction framework that jointly fuses irregularly distributed multi-source oceanic observations with Global Forecast System (GFS) numerical weather prediction outputs. The method employs ensemble attention to model observational uncertainty, cross-attention for conditional alignment between forecasts and observations, and integrates masking, recurrent temporal embeddings, and coordinate-aware positional encoding to enable single-pass inference at arbitrary locations. Experiments over the Atlantic demonstrate substantial improvements: 1-hour wind speed RMSE decreases by 45%, and 48-hour RMSE remains reduced by 13%, with particularly pronounced gains along coastlines and major shipping routes. This work pioneers the application of spatiotemporally adaptive attention mechanisms to NWP post-processing, achieving high-accuracy, low-latency marine wind field correction under sparse observational constraints.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable. We reformulate wind forecasting as observation-informed correction of a global numerical weather prediction (NWP) model. Rather than forecasting winds directly, we learn local correction patterns by assimilating the latest in-situ observations to adjust the Global Forecast System (GFS) output. We propose a transformer-based deep learning architecture that (i) handles irregular and time-varying observation sets through masking and set-based attention mechanisms, (ii) conditions predictions on recent observation-forecast pairs via cross-attention, and (iii) employs cyclical time embeddings and coordinate-aware location representations to enable single-pass inference at arbitrary spatial coordinates. We evaluate our model over the Atlantic Ocean using observations from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) as reference. The model reduces GFS 10-meter wind RMSE at all lead times up to 48 hours, achieving 45% improvement at 1-hour lead time and 13% improvement at 48-hour lead time. Spatial analyses reveal the most persistent improvements along coastlines and shipping routes, where observations are most abundant. The tokenized architecture naturally accommodates heterogeneous observing platforms (ships, buoys, tide gauges, and coastal stations) and produces both site-specific predictions and basin-scale gridded products in a single forward pass. These results demonstrate a practical, low-latency post-processing approach that complements NWP by learning to correct systematic forecast errors.

Problem

• Corrects marine wind forecasts using real-time observations
• Handles sparse, irregular ocean data with transformer architecture
• Reduces forecast errors for navigation and energy operations

Innovation

• Transformer-based architecture corrects global weather model outputs
• Set-based attention handles irregular, time-varying ocean observations
• Single-pass inference produces site-specific and gridded wind predictions