This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations a...This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location.Our tidal modeling methodology follows an iterative process:partitioning sea surface height(SSH)observations into analysis/training and prediction/validation parts and ultimately identi-fying the set of tidal constituents that provide the best predictions at each time series location.The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea,each with its own set of tidal constituents.Verification of the developed tidal models against the sSH observations within the prediction/validation part reveals mean absolute error(MAE)values ranging from 0.0334 m to 0.1349 m,with an average MAE of 0.089 m.The same validation process is conducted on the FES2014 and EOT20 global tidal models,demonstrating that our tidal model,referred to as BT23(short for Baltic Tide 2023),outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m,respectively.In addition to providing details on the development of the time series and the tidal modeling procedure,we offer the 1256 along-track time series and their associated tidal models as supplementary materials.We encourage the satellite altimetry community to utilize these resources for further research and applications.展开更多
Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the...Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.展开更多
冰盖的数字高程模型(digital elevation model,DEM)是对冰盖形状的刻画,是研究南极冰盖必不可少的基础数据[1]。然而,受气候变化影响,西南极冰盖发生了剧烈的消融,冰盖的DEM也随之不断地变化。Ice,Cloud and land Elevation Satellite-2...冰盖的数字高程模型(digital elevation model,DEM)是对冰盖形状的刻画,是研究南极冰盖必不可少的基础数据[1]。然而,受气候变化影响,西南极冰盖发生了剧烈的消融,冰盖的DEM也随之不断地变化。Ice,Cloud and land Elevation Satellite-2(ICESat-2)是美国国家宇航局(National Aeronautics and Space Administration,NASA)于2018年发射的最新一代激光测高卫星,星上搭载的先进地形激光测高系统(Advanced Topographic Laser Altimeter System,ATLAS)对南极冰盖表面进行了高密度的精确观测。本文利用这一观测数据,使用最小二乘拟合法制作了1 km×1 km网格的南极冰盖DEM。使用NASA冰桥(IceBridge)计划采集的机载激光雷达测高数据评估发现,随着冰盖表面坡度的降低,该DEM的精确度逐渐提高。整体来看,该ICESat-2 DEM与IceBridge高程数据的差值的中位数、均方根和十分位距分别为–0.45 m,17.51 m和17.93 m。在不同坡度范围内,该ICESat-2 DEM的精确度均优于前人基于ICESat-2数据所得到的南极冰盖的DEM。展开更多
The calibration of the sea surface height(SSH)measured by satellite altimeters is essential to understand altimeter biases.Many factors affects the construction and maintenance of a permanent calibration site.In order...The calibration of the sea surface height(SSH)measured by satellite altimeters is essential to understand altimeter biases.Many factors affects the construction and maintenance of a permanent calibration site.In order to calibrate Chinese satellite altimetry missions,the feasibility of maintaining a calibration site based on the Qianliyan islet in Yellow Sea of China is taken into account.The related calibration facilities,such as the permanent tide gauge,GNSS reference station and meteorological station,were already operated by the Ministry of Natural Resources of China.The data could be fully used for satellite altimeter calibration with small fiscal expenditure.In addition,the location and marine environments of Qianliyan were discussed.Finally,we used the Jason-3 mission to check the possibility of calibration works.The result indicates that the brightness temperatures of three channels measured by microwave radiometer(MWR)and the derived wet tropospheric correction varies smoothly,which means the land contamination to MWR could be ignored.The high frequency waveforms at the Qianliyan site present no obvious difference from the normal waveforms received by satellite radar altimeter over the open ocean.In conclusion,the Qianliyan islet will not influence satellite altimetry observation.Following these analyses,a possible layout and mechanism of the Qianliyan calibration site are proposed.展开更多
文摘This research aims to optimize the utilization of long-term sea level data from the TOPEX/Poseidon,Jason1,Jason2,and Jason3 altimetry missions for tidal modeling.We generate a time series of along-track observations and apply a developed method to produce tidal models with specific tidal constituents for each location.Our tidal modeling methodology follows an iterative process:partitioning sea surface height(SSH)observations into analysis/training and prediction/validation parts and ultimately identi-fying the set of tidal constituents that provide the best predictions at each time series location.The study focuses on developing 1256 time series along the altimetry tracks over the Baltic Sea,each with its own set of tidal constituents.Verification of the developed tidal models against the sSH observations within the prediction/validation part reveals mean absolute error(MAE)values ranging from 0.0334 m to 0.1349 m,with an average MAE of 0.089 m.The same validation process is conducted on the FES2014 and EOT20 global tidal models,demonstrating that our tidal model,referred to as BT23(short for Baltic Tide 2023),outperforms both models with an average MAE improvement of 0.0417 m and 0.0346 m,respectively.In addition to providing details on the development of the time series and the tidal modeling procedure,we offer the 1256 along-track time series and their associated tidal models as supplementary materials.We encourage the satellite altimetry community to utilize these resources for further research and applications.
基金The National Key R&D Program of China under contract Nos 2022YFC3003800,2020YFC1521700 and 2020YFC1521705the National Natural Science Foundation of China under contract No.41830540+3 种基金the Open Fund of the East China Coastal Field Scientific Observation and Research Station of the Ministry of Natural Resources under contract No.OR-SECCZ2022104the Deep Blue Project of Shanghai Jiao Tong University under contract No.SL2020ZD204the Special Funding Project for the Basic Scientific Research Operation Expenses of the Central Government-Level Research Institutes of Public Interest of China under contract No.SZ2102the Zhejiang Provincial Project under contract No.330000210130313013006。
文摘Understanding the topographic patterns of the seafloor is a very important part of understanding our planet.Although the science involved in bathymetric surveying has advanced much over the decades,less than 20%of the seafloor has been precisely modeled to date,and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data.In this study,we introduce a pretrained visual geometry group network(VGGNet)method based on deep learning.To apply this method,we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter,which has a larger spatial coverage,based on the former,which is considered the true value and is more accurate.After obtaining the corrected high-precision gravity model,it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation.We choose four data pairs collected from different environments,i.e.,the Southern Ocean,Pacific Ocean,Atlantic Ocean and Caribbean Sea,to evaluate the topographic correction results of the model.The experiments show that the coefficient of determination(R~2)reaches 0.834 among the results of the four experimental groups,signifying a high correlation.The standard deviation and normalized root mean square error are also evaluated,and the accuracy of their performance improved by up to 24.2%compared with similar research done in recent years.The evaluation of the R^(2) values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research.Finally,the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21%within 1%of the total water depths,which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results.
基金supported by the National Natural Science Foundation of China under Grants No. 42174001
文摘The calibration of the sea surface height(SSH)measured by satellite altimeters is essential to understand altimeter biases.Many factors affects the construction and maintenance of a permanent calibration site.In order to calibrate Chinese satellite altimetry missions,the feasibility of maintaining a calibration site based on the Qianliyan islet in Yellow Sea of China is taken into account.The related calibration facilities,such as the permanent tide gauge,GNSS reference station and meteorological station,were already operated by the Ministry of Natural Resources of China.The data could be fully used for satellite altimeter calibration with small fiscal expenditure.In addition,the location and marine environments of Qianliyan were discussed.Finally,we used the Jason-3 mission to check the possibility of calibration works.The result indicates that the brightness temperatures of three channels measured by microwave radiometer(MWR)and the derived wet tropospheric correction varies smoothly,which means the land contamination to MWR could be ignored.The high frequency waveforms at the Qianliyan site present no obvious difference from the normal waveforms received by satellite radar altimeter over the open ocean.In conclusion,the Qianliyan islet will not influence satellite altimetry observation.Following these analyses,a possible layout and mechanism of the Qianliyan calibration site are proposed.
