The Argo project deploys thousands of floats throughout the world's oceans. Carried only by the current, these floats take measurements such as temperature and salinity at depths of up to two kilometers. These measurements are critical for scientific tasks such as modeling climate change, estimating temperature and salinity fields, and tracking the global hydrological cycle. In the Southern Ocean, Argo floats frequently drift under ice cover which prevents tracking via GPS. Managing this missing location data is an important scientific challenge for the Argo project. To predict the floats' trajectories under ice and quantify their uncertainty, we introduce a probabilistic state-space model (SSM) called ArgoSSM. ArgoSSM infers the posterior distribution of a float's position and velocity at each time based on all available data, which includes GPS measurements, ice cover, and potential vorticity. This inference is achieved via an efficient particle filtering scheme, which is effective despite the high signal-to0noise ratio in the GPS data. Compared to existing interpolation approaches in oceanography, ArgoSSM more accurately predicts held-out GPS measurements. Moreover, because uncertainty estimates are well-calibrated in the posterior distribution, ArgoSSM enables more robust and accurate temperature, salinity, and circulation estimates.
翻译:阿尔戈项目在全世界海洋中部署数千个浮体。 仅以洋流为依托, 这些浮体只能测量温度和盐度等温度和盐度等测量量, 这些测量量对于模拟气候变化、 估计温度和盐度田以及跟踪全球水文循环等科学任务至关重要 。 在南大洋, 阿尔戈 浮体经常漂浮在冰盖下, 无法通过 GPS 跟踪。 管理这个缺失的位置数据是Argo 项目的一个重要科学挑战 。 要预测冰下的浮体轨迹并量化其不确定性, 我们引入了一种概率性州空间模型( SSM), 称为 ArgoSSSM 。 阿戈SM 根据所有现有数据, 包括GPS 测量、 冰盖和潜在易变异性。 这种推论是通过高效的粒子过滤计划实现的, 尽管GPS 数据中的信号到0 率比率很高, 但是, 我们比较了海洋学中现有的内部分析方法, ArgoSMSM 更精确地预测了每个浮标的位置和速度。 此外,, 更准确地预测了, 更精确的ARBSMS 。