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 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 MEAN SEA SURFACE (MSS) MODEL DETERMINATION FOR MALAYSIAN SEAS USING MULTI-MISSION SATELLITE ALTIMETER

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 247-252
Author(s):  
N. A. Z. Yahaya ◽  
T. A. Musa ◽  
K. M. Omar ◽  
A. H. M. Din ◽  
A. H. Omar ◽  
...  
Keyword(s):  
Monsoon Season ◽  
Sea Surface ◽  
Dynamic Topography ◽  
Level Variation ◽  
Radar Altimeter ◽  
Satellite Altimeter ◽  
Sea Level Variation ◽  
Mission Satellite ◽  
Mean Sea Surface ◽  
Model Determination

The advancement of satellite altimeter technology has generated many evolutions to oceanographic and geophysical studies. A multi-mission satellite altimeter consists with TOPEX, Jason-1 and Jason-2, ERS-2, Envisat-1, CryoSat-2 and Saral are extracted in this study and has been processed using Radar Altimeter Database System (RADS) for the period of January 2005 to December 2015 to produce the sea surface height (hereinafter referred to SSH). The monthly climatology data from SSH is generated and averaged to understand the variation of SSH during monsoon season. Then, SSH data are required to determine the localised and new mean sea surface (MSS). The differences between Localised MSS and DTU13 MSS Global Model is plotted with root mean square error value is 2.217 metres. The localised MSS is important towards several applications for instance, as a reference for sea level variation, bathymetry prediction and derivation of mean dynamic topography.

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>

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.

Assimilating Along-Track Altimetric Observations through Local Hydrostatic Adjustment in a Global Ocean Variational Assimilation System

2011 ◽  
Vol 139 (3) ◽  
pp. 738-754 ◽  
Author(s):  
Andrea Storto ◽  
Srdjan Dobricic ◽  
Simona Masina ◽  
Pierluigi Di Pietro
Keyword(s):  
Sea Level ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Global Ocean ◽  
Dynamic Topography ◽  
Sea Level Anomaly ◽  
Mean Dynamic Topography ◽  
Skill Scores ◽  
The Mean ◽  
Along Track

Abstract A global ocean three-dimensional variational data assimilation system was developed with the aim of assimilating along-track sea level anomaly observations, along with in situ observations from bathythermographs and conventional sea stations. All the available altimetric data within the period October 1992–January 2006 were used in this study. The sea level corrections were covariated with vertical profiles of temperature and salinity according to the bivariate definition of the background-error vertical covariances. Sea level anomaly observational error variance was carefully defined as a sum of instrumental, representativeness, observation operator, and mean dynamic topography error variances. The mean dynamic topography was computed from the model long-term mean sea surface height and adjusted through an optimal interpolation scheme to account for observation minus first-guess biases. Results show that the assimilation of sea level anomaly observations improves the model sea surface height skill scores as well as the subsurface temperature and salinity fields. Furthermore, the estimate of the tropical and subtropical surface circulation is clearly improved after assimilating altimetric data. Nonnegligible impacts of the mean dynamic topography used have also been found: compared to a gravimeter-based mean dynamic topography the use of the mean dynamic topography discussed in this paper improves both the consistency with sea level anomaly observations and the verification skill scores of temperature and salinity in the tropical regions. Furthermore, the use of a mean dynamic topography computed from the model long-term sea surface height mean without observation adjustments results in worsened verification skill scores and highlights the benefits of the current approach for deriving the mean dynamic topography.

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