Data for: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlights the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (..., Observation data is from in situ wave buoys (Sofar Spotter buoys) and satellite altimeters. Model data is from the WaveWatchIII (WW3) spectral wave model. Full methods of data acquisition and processing are described in Houghton et al. (2023). , CSV files are provided to be accessed by the user's preferred program (Python, Excel, Matlab).本研究依托全球分布式Sofar Spotter浮标与卫星高度计的有效波高（significant wave height）观测数据，实现了基于集合的波浪数据同化（wave data assimilation）方法。局部集合变换卡尔曼滤波（Local Ensemble Transform Kalman Filter，简称LETKF）可生成高精度分析场，将其作为循环波浪数据同化系统的初始条件时，可将预报误差降低至2.5天以内。相较于最优插值（optimal interpolation）等同化方法，LETKF方法可生成更贴合物理规律的模型状态更新结果，能够更精准地反映海洋状态的动力学特性与不确定性。针对无观测覆盖区域的技能评估与典型风暴事件的分析，均验证了LETKF方法在数据同化任务中相较于最优插值方法的性能优势。该技术进展对提升海洋状态预报精度具有直接应用价值，更可助力未来耦合数据同化（coupled data assimilation）研究，并实现跨领域全球地表观测数据的高效利用。观测数据取自原位波浪浮标（Sofar Spotter浮标）与卫星高度计，模型数据则取自WaveWatchIII（WW3）谱波浪模型。完整的数据采集与处理方法详见Houghton等人（2023）的研究成果。本数据集提供CSV格式文件，可通过用户偏好的编程工具（如Python、Excel、Matlab）读取使用。