Data for "Observation-driven correction of numerical weather prediction for marine winds" (Earth-Intelligence-Lab/marine-wind-forecasting)

This repository contains the data for the paper "Observation-driven correction of numerical weather prediction for marine winds" submitted to JGR: Machine Learning and Computation.

The paper presents a transformer-based approach that reformulates marine wind forecasting as observation-informed correction of numerical weather prediction. Rather than forecasting winds directly, the model learns local correction patterns by assimilating the latest in-situ observations to adjust Global Forecast System (GFS) outputs. The architecture handles irregular and time-varying observation sets through masking and set-based attention mechanisms, conditions predictions on recent observation–forecast pairs via cross-attention, and employs cyclical time embeddings and coordinate-aware location representations to enable single-pass inference at arbitrary spatial coordinates.

The model is evaluated over the Atlantic Ocean using collocated observations from the International Comprehensive Ocean-Atmosphere Data Set (ICOADS). It reduces GFS 10-meter wind root-mean-square error at all lead times up to 48 hours, achieving 45% improvement at 1-hour lead time and 13% improvement at 48-hour lead time. 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.

Use the following citation when the data or the model are used:

> Peduto, M.; Yang, Q.; Giezendanner, J.; Tuia, D.; Wang, S.; Observation-driven correction of numerical weather prediction for marine winds. Submitted to JGR: Machine Learning and Computation, 2025.

The following data is available:

The files are already processed and ready to be used in the model.

For ICOADS, ERA5 and GFS, the following variables are available:- u and v component of wind vector at 10 meters above ground- additional variables for ERA5 and GFS

├── lead_time_n.zip      ├── train.parquet      ├── test.parquet      ├── validation.parquet