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.

scholarly journals Adjusting altimetric sea surface height observations in coastal regions. Case study in the Greek Seas

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
Ioannis Mintourakis
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface Height ◽  
Sea Surface ◽  
Altimetry Data ◽  
Geoid Model ◽  
Sea Surface Topography ◽  
Marine Gravity ◽  
Mean Sea Surface ◽  
Process Strategy ◽  
Satellite Altimetry Data

AbstractWhen processing satellite altimetry data for Mean Sea Surface (MSS) modelling in coastal environments many problems arise. The degradation of the accuracy of the Sea Surface Height (SSH) observations close to the coastline and the usually irregular pattern and variability of the sea surface topography are the two dominant factors which have to be addressed. In the present paper, we study the statistical behavior of the SSH observations in relation to the range from the coastline for many satellite altimetry missions and we make an effort to minimize the effects of the ocean variability. Based on the above concepts we present a process strategy for the homogenization of multi satellite altimetry data that takes advantage ofweighted SSH observations and applies high degree polynomials for the adjustment and their uniffcation at a common epoch. At each step we present the contribution of each concept to MSS modelling and then we develop a MSS, a marine geoid model and a grid of gravity Free Air Anomalies (FAA) for the area under study. Finally, we evaluate the accuracy of the resulting models by comparisons to state of the art global models and other available data such as GPS/leveling points, marine GPS SSH’s and marine gravity FAA’s, in order to investigate any progress achieved by the presented strategy

scholarly journals GEODYNAMICS

GEODYNAMICS ◽  
2011 ◽  
Vol 1(10)2011 (1(10)) ◽  
pp. 27-30
Author(s):  
N. Marchenko ◽  
◽  
N.P. Yarema ◽  
T.R. Pavliv ◽  
◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Surface Topography ◽  
Black Sea ◽  
Satellite Altimetry ◽  
Sea Surface ◽  
The Black Sea ◽  
Altimetry Data ◽  
Sea Surface Topography ◽  
Satellite Altimetry Data

The study of Black Sea and Mediterranean Sea surface altitudes was carried out based on satellite altimetry data. The model of the Black Sea and Mediterranean Sea surface topography (SST) was build. The comparison of received results with the European quasigeoid was done.

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 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 ANALYSIS OF SEA SURFACE HEIGHT ANOMALY CHARACTERISTICS BASED ON SATELLITE ALTIMETRY DATA (CASE STUDY: SEAS SURROUNDING JAVA ISLAND)

2017 ◽  
Vol 11 (2) ◽  
pp. 137
Author(s):  
Sartono Marpaung ◽  
Wawan K. Harsanugraha
Keyword(s):  
Satellite Altimetry ◽  
Sea Surface Height ◽  
Coastal Areas ◽  
Sea Surface ◽  
Height Anomaly ◽  
Spatial And Temporal Variability ◽  
Altimetry Data ◽  
The South ◽  
Sea Surface Height Anomaly ◽  
Satellite Altimetry Data

Sea surface height anomaly is a oceanographic parameter that has spatial and temporal variability. This paper aims to determine the characters of sea surface height anomaly in the south and north seas of Java Island. To find these characters, a descriptive analysis of monthly anomaly data is performed spatially, zonally and temporally. Based on satellite altimetry data from 1993 to 2010, the analysis shows that the average of sea surface height anomaly varies, ranging from -15 cm to 15 cm. Spatially and zonally, there are three patterns that can be concidered as sea surface height anomaly characteristics: anomaly is higher in coastal areas than in open seas, anomaly is lower in coastal areas than in open seas and anomaly in coastal area is almost the same as in open seas. The first and second patterns occur in the south and north seas of Java Island. The third pattern occurs simultaneously in south and north seas of Java Island. Characteristics of temporal anomaly have a sinusoidal pattern in south and north seas of Java Island.

scholarly journals ESTIMATING AND FUSING OPTICAL FLOW, GEOSTROPHIC CURRENTS AND SEA SURFACE WIND IN THE WATERS AROUND KISH ISLAND

2015 ◽  
Vol XL-1-W5 ◽  
pp. 221-226 ◽  
Author(s):  
E. Ghalenoei ◽  
M. A. Sharifi ◽  
M. Hasanlou
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Altimetry ◽  
Surface Wind ◽  
Sea Surface ◽  
Data Sets ◽  
Surface Currents ◽  
Altimetry Data ◽  
Sea Surface Wind ◽  
Satellite Altimetry Data

The aim of this study is calculation of sea surface currents (SSCs) which are estimated from satellite data sets and processed with the variance component estimation (VCE) algorithm to check role of each data set, in fused surface currents (FSCs). The satellite data used in this study are sea surface temperature (SST), satellite altimetry data and sea surface wind (SSW) that plays the important role to make the SSCs and is measured by Ascat satellite. We use optical flow (OF) method (Horn-Schunck algorithm) to extract sea surface movements from sequential SST imageries; in addition, geostrophic currents (GCs) are estimated by satellite altimetry data like sea surface height (SSH). Combining these data sets, has its pros and cons, the OF results are so dense and precise due to high spatial resolution of MODIS data (SST), but sometimes cloud covering over the sea, does not allow the MODIS sensor to measure the SST. In contrast the SST data, the altimetry data have poor spatial resolution and the GCs are not able to determine small scale SSCs. The VCE algorithm shows variances of our data sets and it can be shown their correlations with themselves and with the FSCs. We also calculate angular differences between FSCs and OF, GCs and SSW, and plot distributions of these angular differences. We discover that, the OF and SSW are homolographic, but OF and GCs are accordant to each other.

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