The DTU13 MSS (Mean Sea Surface) and MDT (Mean Dynamic Topography) from 20 Years of Satellite Altimetry

2015 ◽  
pp. 111-121 ◽  
Author(s):  
Ole Andersen ◽  
Per Knudsen ◽  
Lars Stenseng
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface

scholarly journals Nghiên cứu thử nghiệm sử dụng mô hình mặt biển tự nhiên toàn cầu (Mean Dynamic Topography) phục vụ tính chuyển trị đo sâu về hệ độ cao quốc gia

2016 ◽  
pp. 14-24
Author(s):  
Chí Công Dương ◽  
Minh Hòa Hà ◽  
Tuấn Anh Nguyễn ◽  
Thanh An Lê ◽  
Trung Thành Hoàng
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface ◽  
Viet Nam ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface

Trên thế giới các kết quả đo cao từ vệ tinh (Đo cao vệ tinh: Satellite Altimetry) như bề mặt biển tự nhiên biển (MSS – Mean Sea Surface), mặt địa hình biển động lực trung bình so với Geoid (MDT – Mean Dynamic Topography), xác định trường trọng lực và Geoid trên biển v.v đã được ứng dụng nhiều trong nghiên cứu về Hải dương học, Khí tượng – Hải văn biển, Trắc địa và Địa vật lý. Ở Việt Nam việc nghiên cứu sử dụng mô hình MSS, MDT trong Trắc địa bản đồ cũng đã được một số tác giả khởi xướng từ vài năm trở lại đây. Trong bài báo này chúng tôi thử nghiệm sử dụng mô hình DNSC08MDT để tính trị đo sâu. Mô hình này được cung cấp chính thức tại thời điểm nghiên cứu, sau này đã có thêm các mô hình DTU10, DTU12 được cải tiến hơn (Dương Chí Công và nnk, 2015). Tại khu vực Bắc Trung Bộ (từ vĩ tuyến ~15° đến ~20°, từ bờ biển đến kinh độ 116°) với mô hình DNSC08MDT sau khi cải chính (về Geoid cục bộ Hòn Dấu trong hệ triều 0 và độ lệch hệ thống so với các trạm nghiệm triều) đã tính được trị đo sâu cho 5 mảnh bản đồ địa hình đáy biển tỷ lệ 1/50.000. Độ chính xác trung bình đạt ±0.6m là có thể chấp nhận được đối với bản đồ địa hình đáy biển tỷ lệ 1/50.000 khu vực xa bờ (từ khoảng 20 km trở ra).

scholarly journals Regional Mean Sea Surface and Mean Dynamic Topography Models Around Malaysian Seas Developed From 27 Years of Along-Track Multi-Mission Satellite Altimetry Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Mohammad Hanif Hamden ◽  
Ami Hassan Md Din ◽  
Dudy Darmawan Wijaya ◽  
Mohd Yunus Mohd Yusoff ◽  
Muhammad Faiz Pa’suya
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Tide Gauges ◽  
Regional Models ◽  
Mean Dynamic Topography ◽  
Mission Satellite ◽  
Mean Sea Surface ◽  
Along Track ◽  
Good Agreement

Contemporary Universiti Teknologi Malaysia 2020 Mean Sea Surface (UTM20 MSS) and Mean Dynamic Topography (UTM20 MDT) models around Malaysian seas are introduced in this study. These regional models are computed via scrutinizing along-track sea surface height (SSH) points and specific interpolation methods. A 1.5-min resolution of UTM20 MSS is established by integrating 27 years of along-track multi-mission satellite altimetry covering 1993–2019 and considering the 19-year moving average technique. The Exact Repeat Mission (ERM) collinear analysis, reduction of sea level variability of geodetic mission (GM) data, crossover adjustment, and data gridding are presented as part of the MSS computation. The UTM20 MDT is derived using a pointwise approach from the differences between UTM20 MSS and the local gravimetric geoid. UTM20 MSS and MDT reliability are validated with the latest Technical University of Denmark (DTU) and Collecte Localisation Services (CLS) models along with coastal tide gauges. The findings presented that the UTM20, CLS15, and DTU18 MSS models exhibit good agreement. Besides, UTM20 MDT is also in good agreement with CLS18 and DTU15 MDT models with an accuracy of 5.1 and 5.5 cm, respectively. The results also indicate that UTM20 MDT statistically achieves better accuracy than global models compared to tide gauges. Meanwhile, the UTM20 MSS accuracy is within 7.5 cm. These outcomes prove that UTM20 MSS and MDT models yield significant improvement compared to the previous regional models developed by UTM, denoted as MSS1 and MSS2 in this study.

scholarly journals Impacts of mean dynamic topography on a regional ocean assimilation system

Ocean Science ◽  
2015 ◽  
Vol 11 (5) ◽  
pp. 829-837 ◽  
Author(s):  
C. Yan ◽  
J. Zhu ◽  
C. A. S. Tanajura
Keyword(s):  
Data Assimilation ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface ◽  
The Mean

Abstract. An ocean data assimilation system was developed for the Pacific–Indian oceans with the aim of assimilating altimetry data, sea surface temperature, and in situ measurements from Argo (Array for Real-time Geostrophic Oceanography), XBT (expendable bathythermographs), CTD (conductivity temperature depth), and TAO (Tropical Atmosphere Ocean). The altimetry data assimilation requires the addition of the mean dynamic topography to the altimetric sea level anomaly to match the model sea surface height. The mean dynamic topography is usually computed from the model long-term mean sea surface height, and is also available from gravimetric satellite data. In this study, the impact of different mean dynamic topographies on the sea level anomaly assimilation is examined. Results show that impacts of the mean dynamic topography cannot be neglected. The mean dynamic topography from the model long-term mean sea surface height without assimilating in situ observations results in worsened subsurface temperature and salinity estimates. Even if all available observations including in situ measurements, sea surface temperature measurements, and altimetry data are assimilated, the estimates are still not improved. This proves the significant impact of the MDT (mean dynamic topography) on the analysis system, as the other types of observations do not compensate for the shortcoming due to the altimetry data assimilation. The gravimeter-based mean dynamic topography results in a good estimate compared with that of the experiment without assimilation. The mean dynamic topography computed from the model long-term mean sea surface height after assimilating in situ observations presents better results.

scholarly journals A global vertical datum defined by the conventional geoid potential and the Earth ellipsoid parameters

2020 ◽  
Author(s):  
Hadi Amin ◽  
Lars E. Sjöberg ◽  
Mohammad Bagherbandi
Keyword(s):  
Gravity Field ◽  
Satellite Altimetry ◽  
Input Data ◽  
Equipotential Surface ◽  
Sea Surface ◽  
Dynamic Topography ◽  
The Earth ◽  
Degree Harmonic ◽  
Mean Sea Surface ◽  
Zero Degree

<p>According to the classical Gauss–Listing definition, the geoid is the equipotential surface of the Earth’s gravity field that in a least-squares sense best fits the undisturbed mean sea level. This equipotential surface, except for its zero-degree harmonic, can be characterized using the Earth’s Global Gravity Models (GGM). Although nowadays, the satellite altimetry technique provides the absolute geoid height over oceans that can be used to calibrate the unknown zero-degree harmonic of the gravimetric geoid models, this technique cannot be utilized to estimate the geometric parameters of the Mean Earth Ellipsoid (MEE). In this study, we perform joint estimation of W<sub>0</sub>, which defines the zero datum of vertical coordinates, and the MEE parameters relying on a new approach and on the newest gravity field, mean sea surface, and mean dynamic topography models. As our approach utilizes both satellite altimetry observations and a GGM model, we consider different aspects of the input data to evaluate the sensitivity of our estimations to the input data. Unlike previous studies, our results show that it is not sufficient to use only the satellite-component of a quasi-stationary GGM to estimate W<sub>0</sub>. In addition, our results confirm a high sensitivity of the applied approach to the altimetry-based geoid heights, i.e. mean sea surface and mean dynamic topography models. Moreover, as W<sub>0</sub> should be considered a quasi-stationary parameter, we quantify the effect of time-dependent Earth’s gravity field changes as well as the time-dependent sea-level changes on the estimation of W<sub>0</sub>. Our computations resulted in the geoid potential W<sub>0 </sub>= 62636848.102 ± 0.004 m<sup>2</sup>s<sup>-2</sup> and the semi-major and –minor axes of the MEE, a = 6378137.678 ± 0.0003 m and b = 6356752.964 ± 0.0005 m, which are 0.678 and 0.650 m larger than those axes of the GRS80 reference ellipsoid, respectively. Moreover, a new estimation for the geocentric gravitational constant was obtained as GM = (398600460.55 ± 0.03) × 10<sup>6</sup> m<sup>3</sup>s<sup>-2</sup>.</p>

DNSC08 mean sea surface and mean dynamic topography models

2009 ◽  
Vol 114 (C11) ◽  
Author(s):  
Ole B. Andersen ◽  
Per Knudsen
Keyword(s):  
Sea Surface ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface

scholarly journals Impacts of mean dynamic topography on a regional ocean assimilation system

2015 ◽  
Vol 12 (3) ◽  
pp. 1083-1105
Author(s):  
C. Yan ◽  
J. Zhu ◽  
C. A. S. Tanajura
Keyword(s):  
Sea Surface Height ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface ◽  
In Situ Observations ◽  
The Mean

Abstract. An ocean assimilation system was developed for the Pacific-Indian oceans with the aim of assimilating altimetry data, sea surface temperature, and in-situ measurements from ARGO, XBT, CTD, and TAO. The altimetry data assimilation requires the addition of the mean dynamic topography to the altimetric sea level anomaly to match the model sea surface height. The mean dynamic topography is usually computed from the model long-term mean sea surface height, and is also available from gravimeteric satellite data. In this study, different mean dynamic topographies are used to examine their impacts on the sea level anomaly assimilation. Results show that impacts of the mean dynamic topography cannot be neglected. The mean dynamic topography from the model long-term mean sea surface height without assimilating in-situ observations results in worsened subsurface temperature and salinity estimates. The gravimeter-based mean dynamic topography results in an even worse estimate. Even if all available observations including in-situ measurements, sea surface temperature measurements, and altimetry data are assimilated, the estimates are still not improved. This further indicates that the other types of observations do not compensate for the shortcoming due to the altimetry data assimilation. The mean dynamic topography computed from the model's long-term mean sea surface height after assimilating in-situ observations presents better results.

scholarly journals A global vertical datum defined by the conventional geoid potential and the Earth ellipsoid parameters

2019 ◽  
Vol 93 (10) ◽  
pp. 1943-1961
Author(s):  
Hadi Amin ◽  
Lars E. Sjöberg ◽  
Mohammad Bagherbandi
Keyword(s):  
Gravity Field ◽  
Satellite Altimetry ◽  
Input Data ◽  
Equipotential Surface ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Earth’S Gravity Field ◽  
Degree Harmonic ◽  
Mean Sea Surface ◽  
Zero Degree

Abstract The geoid, according to the classical Gauss–Listing definition, is, among infinite equipotential surfaces of the Earth’s gravity field, the equipotential surface that in a least squares sense best fits the undisturbed mean sea level. This equipotential surface, except for its zero-degree harmonic, can be characterized using the Earth’s global gravity models (GGM). Although, nowadays, satellite altimetry technique provides the absolute geoid height over oceans that can be used to calibrate the unknown zero-degree harmonic of the gravimetric geoid models, this technique cannot be utilized to estimate the geometric parameters of the mean Earth ellipsoid (MEE). The main objective of this study is to perform a joint estimation of W0, which defines the zero datum of vertical coordinates, and the MEE parameters relying on a new approach and on the newest gravity field, mean sea surface and mean dynamic topography models. As our approach utilizes both satellite altimetry observations and a GGM model, we consider different aspects of the input data to evaluate the sensitivity of our estimations to the input data. Unlike previous studies, our results show that it is not sufficient to use only the satellite-component of a quasi-stationary GGM to estimate W0. In addition, our results confirm a high sensitivity of the applied approach to the altimetry-based geoid heights, i.e., mean sea surface and mean dynamic topography models. Moreover, as W0 should be considered a quasi-stationary parameter, we quantify the effect of time-dependent Earth’s gravity field changes as well as the time-dependent sea level changes on the estimation of W0. Our computations resulted in the geoid potential W0 = 62636848.102 ± 0.004 m2 s−2 and the semi-major and minor axes of the MEE, a = 6378137.678 ± 0.0003 m and b = 6356752.964 ± 0.0005 m, which are 0.678 and 0.650 m larger than those axes of GRS80 reference ellipsoid, respectively. Moreover, a new estimation for the geocentric gravitational constant was obtained as GM = (398600460.55 ± 0.03) × 106 m3 s−2.

Development of the mean sea dynamic topography on the Vietnam water area based on TOPEX/POSEIDON, ENVISAT and JASON-2 DATA

2017 ◽  
Vol 925 (7) ◽  
pp. 9-14
Author(s):  
Van Sang Nguyen ◽  
V.V. Popadyev
Keyword(s):  
Surface Topography ◽  
Satellite Altimetry ◽  
Sea Surface Height ◽  
Geoid Height ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Altimetry Data ◽  
Mean Dynamic Topography ◽  
Sea Surface Topography ◽  
Satellite Altimetry Data

Mean Dynamic Topography (MDT) is the difference between mean sea surface height and geoid. Satellite altimetry data are known as sea surface height (ellipsoidal height), including geoid height, Mean Dynamic Topography and dynamic sea surface topography ht. To determine Mean Dynamic Topography from satellite altimetry data, the geoid height and dynamic sea surface topography should be removed from sea surface height. In this study, geoid height was computed from spherical harmonic coefficients of global Earth Gravity Model (EGM-2008). ht was determined using technique of tracks crossover adjustment. Finally, gridded model of Mean Dynamic Topography was established by using mean-squares prediction technique. By experimental processing and analysis, the gridded model of Mean Dynamic Topography had successfully built 5′ × 5′, named HUMG16MDT, for East Sea, using data of three altimetric satellites, namely TOPEX/POSEIDON, ENVISAT and JASON-2. For control purposes, this model was compared with the measurements on nine tidal stations, the computed estimation of standard deviation 15,5 cm.

Mean sea surface and mean dynamic topography determination from Cryosat-2 data around Australia

2020 ◽  
Author(s):  
Armin Agha Karimi ◽  
Ole Baltazar Andersen ◽  
Xiaoli Deng
Keyword(s):  
Sea Surface ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Mean Sea Surface

scholarly journals Calculating the Ocean’s Mean Dynamic Topography from a Mean Sea Surface and a Geoid

2008 ◽  
Vol 25 (10) ◽  
pp. 1808-1822 ◽  
Author(s):  
Rory J. Bingham ◽  
Keith Haines ◽  
Chris W. Hughes
Keyword(s):  
Geoid Height ◽  
Sea Surface ◽  
Essential Difference ◽  
Spectral Approach ◽  
Height Field ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Surface Circulation ◽  
Mean Sea Surface ◽  
Global Mean

Abstract In principle the global mean geostrophic surface circulation of the ocean can be diagnosed by subtracting a geoid from a mean sea surface (MSS). However, because the resulting mean dynamic topography (MDT) is approximately two orders of magnitude smaller than either of the constituent surfaces, and because the geoid is most naturally expressed as a spectral model while the MSS is a gridded product, in practice complications arise. Two algorithms for combining MSS and satellite-derived geoid data to determine the ocean’s mean dynamic topography (MDT) are considered in this paper: a pointwise approach, whereby the gridded geoid height field is subtracted from the gridded MSS; and a spectral approach, whereby the spherical harmonic coefficients of the geoid are subtracted from an equivalent set of coefficients representing the MSS, from which the gridded MDT is then obtained. The essential difference is that with the latter approach the MSS is truncated, a form of filtering, just as with the geoid. This ensures that errors of omission resulting from the truncation of the geoid, which are small in comparison to the geoid but large in comparison to the MDT, are matched, and therefore negated, by similar errors of omission in the MSS. The MDTs produced by both methods require additional filtering. However, the spectral MDT requires less filtering to remove noise, and therefore it retains more oceanographic information than its pointwise equivalent. The spectral method also results in a more realistic MDT at coastlines.

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