Validating GFS Forecast of Arctic Surface Fluxes against Saildrone Observations

Uncrewed surface vehicles called Saildrones have been utilized to collect in-situ observations in the Arctic, a rapidly changing region where obtaining ground truth data is challenging. This research compares saildrone data from Arctic deployments with forecasts generated by the Global Forecast System (GFS). Analysis of latent and sensible heat flux, along with their related state variables, reveals temporal changes and assesses the accuracy of the prediction by the GFS product. Statistical comparison between saildrone observations and GFS forecasts identifies patterns in the discrepancies between the two data sources. The study identifies biases in the GFS model and highlights potential areas for forecast improvement, informing better predictions of the Arctic environment.