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.

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 Geostrophic currents and kinetic energies in the Black Sea estimated from merged drifter and satellite altimetry data

Ocean Science ◽  
2014 ◽  
Vol 10 (2) ◽  
pp. 155-165 ◽  
Author(s):  
M. Menna ◽  
P.-M. Poulain
Keyword(s):  
Kinetic Energy ◽  
Black Sea ◽  
Satellite Altimetry ◽  
The Black Sea ◽  
Altimetry Data ◽  
Data Set ◽  
Velocity Statistics ◽  
Intense Activity ◽  
Surface Geostrophic Circulation ◽  
Satellite Altimetry Data

Abstract. Drifter measurements and satellite altimetry data are merged to reconstruct the surface geostrophic circulation of the Black Sea in the period 1999–2009. This combined data set is used to estimate pseudo-Eulerian velocity statistics for different time periods. Seasonal and interannual variability of currents and kinetic energy fields are described with particular attention to the mesoscale and sub-basin coastal eddies. The mean currents are generally stronger in winter and enhanced speeds are observed in the period 2002–2006. The most intense activity of sub-basin Batumi Eddy occurs in summer with greater speeds and dimensions in 2006 and 2008. The sub-basin Sevastopol Eddy is generated in spring from a meander of the Rim Current. Mesoscale eddies located along the Anatolia, Caucasus and Crimea coasts are permanent, quasi-permanent or intermittent features and can interact and merge with each other, showing high values of kinetic energy.

scholarly journals Geostrophic currents and kinetic energies in the Black Sea estimated from merged drifter and satellite altimetry data

2013 ◽  
Vol 10 (5) ◽  
pp. 1505-1524
Author(s):  
M. Menna ◽  
P. -M. Poulain
Keyword(s):  
Kinetic Energy ◽  
Black Sea ◽  
Satellite Altimetry ◽  
The Black Sea ◽  
Altimetry Data ◽  
Velocity Statistics ◽  
The Mean ◽  
Intense Activity ◽  
Surface Geostrophic Circulation ◽  
Satellite Altimetry Data

Abstract. Drifter measurements and satellite altimetry data are merged to reconstruct the surface geostrophic circulation of the Black Sea in the period 1999–2009. This combined dataset is used to estimate pseudo-Eulerian velocity statistics for different time periods. Seasonal and interannual variability of currents and kinetic energy fields are described with particular attention to the mesoscale and sub-basin coastal eddies. The mean currents are generally stronger in winter and enhanced speeds are observed in the period 2002–2006. The most intense activity of sub-basin Batumi Eddy occurs in summer with greater speeds and dimensions in 2006 and 2008. The sub-basin Sevastopol Eddy is generated in spring from a meander of the Rim Current. Mesoscale eddies located along the Anatolia, Caucasus and Crimea coasts are permanent, quasi-permanent or intermittent features and can interact and merge with each other, showing higher values of kinetic energy.

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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