Improving Numerical Model Predicted Float Trajectories by Deep Learning
Improving Numerical Model Predicted Float Trajectories by Deep Learning
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DOI：
10.1029/2022ea002362
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发表时间：
2022-08
影响因子：
3.1
通讯作者：
D. Shen;S. Bao;Len Pietrafesa;P. Gayes
中科院分区：
文献类型：
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作者：
D. Shen;S. Bao;Len Pietrafesa;P. Gayes
拉格朗日跟踪方法经常用于数值模拟（NM），以模拟和预测海洋颗粒的运动，如塑料，石油泄漏和漂浮残骸。NM中的不确定性降低了预测精度，这是由于时间和空间分辨率粗糙，以及波浪、风和海流沿着。2020年8月29日至12月22日，美国国防部高级研究计划局的“海洋物联网”项目在墨西哥湾部署了422个漂浮漂流物，提供了一个利用观测到的轨迹作为地面实况来训练人工智能（AI）模型以纠正NM漂浮物轨迹预测的机会。开发了一个区域海洋模型系统（ROMS）和AI混合模型，以实施AI来校正ROMS预测的1天漂浮轨迹。AI模型建立在卷积神经网络和门控递归单元上。ROMS-AI混合模型的结果表明，在24小时内，82.0%的轨迹预测得到了改善，相应的总体平均分离误差从20.56公里减少到8.74公里，减少了11.82公里，改善了57%，误差增长率从每6小时5.06公里下降到每6小时1.95公里。这一明显的改善表明，ROMS-AI Hybrid模型可以校正ROMS模拟，以改善漂浮物轨迹的1天预测，显示出巨大的潜力，预测集群的浮动。
The Lagrangian tracking approach is often used in numerical modeling (NM) to simulate and predict the movement of marine particles such as plastic, oil spills, and floating wreckage. The uncertainties in NM reduce prediction accuracy as a result of the coarse temporal and spatial resolution, along with waves, winds, and currents. From 29 August to 22 December 2020, the United States Defense Advanced Research Projects Agency's Ocean of Things program deployed 422 floating drifters in the Gulf of Mexico, providing an opportunity of using the observed trajectories as ground truth to train Artificial Intelligence (AI) models to correct NM float trajectory predictions. A Regional Ocean Model System (ROMS) and AI Hybrid model was developed to implement AI to correct the ROMS‐predicted 1‐day float trajectories. The AI model is built on a convolutional neural network and Gated Recurrent Unit. The results of the ROMS‐AI Hybrid model show that 82.0% of the trajectory predictions were improved at the 24 hr, with the corresponding overall mean separation error decreasing by 11.82 km, from 20.56 to 8.74 km, which is a 57% improvement, and the error growth rate decreasing from 5.06 km per 6 hr to 1.95 km per 6 hr. The evident improvement indicates that the ROMS‐AI Hybrid model can correct the ROMS simulation to improve the 1‐day prediction of the float trajectories and shows great potential to predict the cluster of the floats.