AI 中文总结
本研究提出融合多海洋与大气后报模型的线性回归方法，较基线提升2天漂流浮标轨迹预测的Liu-Weisberg技能评分约40%，为相关海洋应用提供了低成本稳健方案。
AI 中文摘要
模拟海洋表层漂流浮标的轨迹对搜救、污染追踪、石油及化学品泄漏应对以及海洋风险分析具有重要意义。文献中普遍指出，精准预测仍存在难度，美国国防高级研究计划局（DARPA）2021年发起的“湍流中浮标预测”挑战赛也印证了这一点，该挑战赛最终促成了本研究。误差的主要来源通常是不确定的洋流，而风强迫和物体漂移特性的误差往往更小[Dagestad与Röhrs，2019]。本研究使用公开的一年期Sofar Spotter轨迹数据集，结合多个海洋与大气后报产品，大规模测试数据驱动的漂移模型。我们对比了三种方法：i）基于一个海洋模型和一个大气模型的标准（基线）漂流浮标轨迹模拟；ii）融合所有可用预测变量的线性回归（LR）模型；iii）使用相似输入的神经网络（NN）模型。结合所有预测变量的简单LR模型表现与NN相当。由于LR更简单、成本更低且更稳健，我们将其作为优选方法。在2天轨迹预测中，该方法的Liu-Weisberg技能评分较基线提升约40%。这些发现适用于后报模式；将该方法应用于预报模式仍有待未来研究。
英文摘要
Simulating the trajectory of surface drifters in the ocean matters for search and rescue, pollution tracking, oil and chemical spill response, and marine risk analysis. Accurate prediction remains difficult, as widely acknowledged in the literature, and also illustrated by the ``Forecasting Floats in Turbulence'' challenge issued by the US Defense Advanced Research Projects Agency (DARPA) in 2021, and which ultimately led to this paper. The main source of error usually comes from uncertain ocean currents, while errors in wind forcing and object drift properties are often smaller [Dagestad and Röhrs, 2019]. Here, we use an open one-year dataset of Sofar Spotter trajectories together with several ocean and atmospheric hindcast products to test data-driven drift models at scale. We compare three approaches: i) a standard (baseline) drifter trajectory simulation based on one ocean model and one atmospheric model, ii) linear regression (LR) models that fuse all available predictors, and iii) neural networks (NN) using similar inputs. A simple LR model that combines all predictors performs equally well as the NN. Because LR is simpler, cheaper, and more robust, we retain it as the preferred approach. In 2-day trajectory prediction, this improves the Liu-Weisberg skill score by around 40\% relative to the baseline. These findings apply to hindcast mode; applying this methodology for forecast mode remains for future work.
