本研究采用多个再跟踪器无缝组合（SCMR）策略获取 2002 年 1 月至 2022 年 4 月的再加工 Jason 数据。评估/验证结果表明，IAS2024 20Hz沿轨沿海海平面数据集在全球沿海海洋上取得了良好的性能。观察到IAS2024和独立高度计数据集之间的良好一致性，包括欧洲航天局气候变化倡议2.4 版（ESA CCI v2.4）20Hz沿线沿海海平面数据集和哥白尼海洋环境监测服务3级（CMEMS L3）1Hz沿线海平面数据集。海平面趋势差（0.16 ± 3.97 mm yr 的闭合−1）在全球范围内实现了IAS2024平均海平面和永久服务（PSMSL）潮汐计数据。此外，使用 IAS2024 沿海海平面数据集构建了 1548 个虚拟站点，这将有助于海洋界的沿海海平面分析和政策制定者的风险管理。我们的研究还发现，在全球范围内，过去20公里沿海地带从近海到海岸的线性海平面趋势不存在明显变化。此外，IAS2024 数据集与 PSMSL 潮汐仪记录相结合的垂直陆地运动（VLM）估计值与拉罗谢尔大学 7a（ULR7a）全球导航卫星系统（GNSS）解决方案非常吻合，VLM 估计值的平均差异为 0.12 ± 2.27 毫米 yr−1，这表明高度计衍生的 VLM 估计值可以用作验证 GNSS 解决方案的独立数据源。", "ds_source": "
数据来源于：https://zenodo.org/records/13208305", "ds_process_way": "
IAS2024沿海海平面数据集是使用 SCMR （Peng et al.， 2024b） 策略生成的。波形前沿检测开始，到来自多个 retracker 的海面高度 （SSH） 估计的无缝组合结束。本研究使用了四种不同的波形再跟踪器，它们是官方传感器地球物理数据记录 （SGDR） 最大似然估计器四参数 （MLE4） 再跟踪器（Amarouche et al.， 2004）、自适应前沿子波形（ALES， Passaro et al.， 2014）、加权最小二乘三参数（WLS3， Peng and 邓， 2018）和改进的布朗模型四参数 （MB4） 再跟踪器（Poisson et al.， 2018）.\n
SCMR处理流程：(1) SGDR MLE4、ALES、WLS3 和 MB4 四个跟踪器；(2) 估计不同跟踪器的 sigma0、SWH 和范围估计值；(3) 计算不同跟踪器的 SSH 估计值；(4) 消除跟踪器之间的偏差并合并不同跟踪器的 SSH 估计值。", "ds_quality": "
与官方的 SGDR MLE4 回溯器相比，SCMR 策略大大提高了数据的可用性，根据沿岸带的不同，提高的百分比在 12.6%到 53.2%之间。此外，还观察到 SCMR 策略对数据精度的适度提高（4.8%-10.1%），这主要是因为 SCMR 可以减少 50 公里波长以下 SLA 频谱的变化。因此，经过 SCMR 重新处理的 Jason 数据的数据可用性可以保留离岸 5 公里以外 90% 以上的信息，离岸 5 公里以内 70%-80% 以上的信息。此外，在 5-20 千米距离带内，20 赫兹 SLA 估计值的精度可保持在厘米级（5-9 厘米），在海岸线附近，精度可保持在分米级（20-23 厘米）。这些结果表明，采用 SCMR 战略生成的 IAS2024 数据集有可能为监测沿岸海平面变化提供可靠的 SLA 估计值。\n
通过将每月 SLA 时间序列以及海平面趋势与 PSMSL 验潮仪记录和独立的高度计数据集（即 ESA CCI v2.4 20 Hz 和 CMEMS L3 1 Hz 沿轨海平面数据集）进行比较，评估和验证了 IAS2024 数据集的性能。在全球沿岸海域，不同高度计数据集之间具有很好的一致性，相关系数很高（大于 0.4），均方根值很低（40-60 毫米）。IAS2024 数据集的海平面趋势比欧空局 CCI v2.4 数据集的海平面趋势平均高 1.32±2.40 毫米/年，与 CMEMS L3 产品的海平面趋势相似（-0.18±2.17 毫米/年）。这可能是由于采用了不同的数据处理技术，特别是用于估算发射间偏差的方法。与验潮仪的验证结果表明，IAS2024 和 CMEMS 数据集在全球尺度上实现了趋势差的闭合（0.16 ± 3.97 和 0.36 ± 3.72 mm yr-1），非常接近理论值零，表明 IAS2024 数据集在监测沿岸海平面方面具有良好的性能。相比之下，欧空局 CCI v2.4 数据集出现了负偏差(-1.50 ± 3.31 mm yr-1)。", "ds_acq_start_time": "2024-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 17614059816, "ds_files_count": 1552, "ds_format": "nc", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "f4b1d8a4-3dc2-4880-8101-684f7553124d.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.60" ], "quality_level": 3, "publish_time": "2025-05-29 16:16:23", "last_updated": "2026-01-14 10:40:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6842.2025", "i18n": { "en": { "title": "IAS2024 coastal sea level dataset", "ds_format": "nc", "ds_source": "
Data source: https://zenodo.org/records/13208305", "ds_quality": "
Compared with the official SGDR MLE4 backtracking system, the SCMR strategy significantly improves data availability, with a percentage increase ranging from 12.6% to 53.2% depending on the coastal zone. In addition, a moderate improvement in data accuracy (4.8% -10.1%) was observed with the SCMR strategy, mainly because SCMR can reduce the variation of SLA spectrum below 50 kilometers wavelength. Therefore, the data availability of Jason data reprocessed by SCMR can retain over 90% of the information beyond 5 kilometers offshore and over 70% -80% of the information within 5 kilometers offshore. In addition, within a distance range of 5-20 kilometers, the accuracy of the 20 Hz SLA estimation can be maintained at the centimeter level (5-9 centimeters), and near the coastline, the accuracy can be maintained at the decimeter level (20-23 centimeters). These results indicate that the IAS2024 dataset generated using the SCMR strategy may provide reliable SLA estimates for monitoring coastal sea level changes.\n
The performance of the IAS2024 dataset was evaluated and validated by comparing the monthly SLA time series and sea level trends with PSMSL tide gauge records and independent altimeter datasets (i.e. ESA CCI v2.4 20 Hz and CMEMS L3 1 Hz orbital sea level datasets). There is good consistency between different altimeter datasets in coastal waters around the world, with high correlation coefficients (greater than 0.4) and low root mean square values (40-60 millimeters). The sea level trend of the IAS2024 dataset is on average 1.32 ± 2.40 millimeters/year higher than that of the European Space Agency CCI v2.4 dataset, and is similar to the sea level trend of the CMEMS L3 product (-0.18 ± 2.17 millimeters/year). This may be due to the use of different data processing techniques, especially methods used to estimate inter launch deviations. The verification results with the tide gauge indicate that the IAS2024 and CMEMS datasets have achieved a closed trend difference at the global scale (0.16 ± 3.97 and 0.36 ± 3.72 mm yr-1), which is very close to the theoretical value of zero, indicating that the IAS2024 dataset has good performance in monitoring coastal sea levels. In contrast, the European Space Agency's CCI v2.4 dataset showed a negative bias (-1.50 ± 3.31 mm yr-1).", "ds_ref_way": "", "ds_abstract": "
One of the reasons for generating this dataset is that the quality of coastal altimeter data has been greatly improved with advanced coastal reprocessing strategies. In this study, the Seamless Combination of Multiple Retrackers (SCMR) strategy is adopted to obtain the reprocessed Jason data from January 2002 to April 2022. The evaluation/validation results show that the IAS2024 20 Hz along-track coastal sea level dataset achieves good performance over global coastal oceans. The good consistency between IAS2024 and independent altimeter datasets, including the European Space Agency Climate Change Initiative version 2.4 (ESA CCI v2.4) 20 Hz along-track coastal sea level dataset and the Copernicus Marine Environment Monitoring Service Level-3 (CMEMS L3) 1 Hz along-track sea level dataset, is observed. The closure of sea level trend differences (0.16 ± 3.97 mm yr−1) between IAS2024 and Permanent Service for Mean Sea Level (PSMSL) tide gauge data at the global scale is also achieved. Moreover, 1548 virtual stations have been constructed using the IAS2024 coastal sea level dataset, which will contribute to the analysis of coastal sea levels for the ocean community and to risk management for the policymakers. Our study also finds that no obvious variations exist in the linear sea level trends from the offshore to the coast over the last 20 km coastal strip at the global scale. In addition, the vertical land motion (VLM) estimates from the combination of the IAS2024 dataset with the PSMSL tide gauge records agree well with the University of La Rochelle 7a (ULR7a) Global Navigation Satellite System (GNSS) solution, with the mean difference of VLM estimates being 0.12 ± 2.27 mm yr−1, suggesting that altimeter-derived VLM estimates can be used as an independent data source to validate the GNSS solutions.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The IAS2024 coastal sea level dataset was generated using the SCMR (Peng et al., 2024b) strategy. The waveform front detection begins and ends with seamless combination of sea surface height (SSH) estimates from multiple retractors. This study used four different waveform re trackers, namely the official sensor geophysical data recording (SGDR) maximum likelihood estimator four parameter (MLE4) re tracker (Amarouche et al., 2004), adaptive frontier sub waveform (ALES, Passaro et al., 2014), weighted least squares three parameter (WLS3, Peng and Deng, 2018), and improved Brownian model four parameter (MB4) re tracker (Poisson et al., 2018)\n
SCMR processing flow: (1) Four trackers: SGDR MLE4, ALES, WLS3, and MB4; (2) Estimate sigma0, SWH, and range estimates for different trackers; (3) Calculate SSH estimates for different trackers; (4) Eliminate bias between trackers and merge SSH estimates from different trackers.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "IAS2024", "海岸线", "高度" ], "ds_subject_tags": [ "海洋科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2024 ], "ds_contributors": [ { "true_name": "彭福凯", "email": "pengfk@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "彭福凯", "email": "pengfk@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "ds_managers": [ { "true_name": "彭福凯", "email": "pengfk@mail.sysu.edu.cn", "work_for": "中山大学", "country": "中国" } ], "category": "其他" }