A new high resolution Mean Sea Surface (DTU21MSS) for improved sea level monitoring

2021 ◽  
Author(s):  
Ole Baltazar Andersen ◽  
Adil Abulaitijiang ◽  
Shengjun Zhang ◽  
Stine Kildegaard Rose
Keyword(s):  
Sea Level ◽  
Sea Surface ◽  
Ocean Tide ◽  
Simultaneous Estimation ◽  
Polar Regions ◽  
Annual Variations ◽  
Sea Level Anomalies ◽  
Ocean Tide Model ◽  
Mean Sea Surface ◽  
The Mean

<p>A new Mean Sea Surface (DTU21MSS) for referencing sea level anomalies from satellite altimetry is presented. The major new advance leading up to the release of this MSS the use of 5 years of Sentinel-3A and an improved 10 years Cryosat-2 LRM+SAR+SARin record including retracked altimetry in Polar regions using the SAMOSA+ physical retracker via the ESA GPOD facility.</p><p>A new processing chain with updated editing and data filtering has been implemented. The filtering implies, that the 20Hz sea surface height data are filtered using the Parks-McClellan filter to derive 1Hz. This has a clear advantage over the 1 Hz boxcar filter in not introducing sidelobes degrading the MSS in the 10-40 km wavelength band. Similarly, the use of consistent ocean tide model for the Mean sea surface improves the usage of sun-syncronous satellites in high latitudes.</p><p>The presentation will also focus on the difficult issues to consolidating Cryosat-2 and Sentinel-3 onto a past 20 year mean sea surface. This is implemented using simultaneous estimation of the mean, sea level trend and annual and semi-annual variations in sea level.  </p>

Regional Mean Sea Surface Model over the Eastern Mediterranean Sea

2020 ◽  
Author(s):  
Milaa Murshan ◽  
Balaji Devaraju ◽  
Nagarajan Balasubramanium ◽  
Onkar Dikshit
Keyword(s):  
Time Series ◽  
Mediterranean Sea ◽  
Sea Level ◽  
Tide Gauge ◽  
Eastern Mediterranean ◽  
Sea Surface ◽  
Surface Model ◽  
Eastern Mediterranean Sea ◽  
Mean Sea Level ◽  
Mean Sea Surface

<p>The Mean Sea Level is not an equipotential surface because it is subject to several variations, e.g., the tides, currents, winds, etc. Mean Sea Level can be measured either by tide gauges near to coastlines relative to local datum or by satellite altimeter above the reference ellipsoid. From this observable quantity, one can derive a non-observable quantity at which the potential is constant called geoid and differs from mean sea surface by amount of ±1 m. This separation is called Sea Surface Topography. In this research, the data of nine altimetric Exact Repeat Missions (Envisat, ERS_1 of 35 days (phase C and G), ERS_2, GFO, Jason_1, Jason_2, Jason_3, Topex/Poseidon and SARAL) were used for computing the regional mean sea surface model over the eastern Mediterranean Sea. The data of all missions together span approximately 25 years from September -1992 to January-2017 and referenced to Topex ellipsoid.  Which is later transformed to WGS84 ellipsoid, as it is chosen to be a unified datum in this study. Prior to computing the altimetric MSS,  altimetric sea surface height measurements were validated  by comparing  time series of altimetric-MSL with mean sea level time series calculated from three in-situ tide gauge measurements.  The sea surface heights values of the derived MSS model is between 15.6 and 26.7 m. And the linear trend slope is between -3.02 to 6.53 mm/year.</p><p>Keywords: Mean Sea Level, Satellite Altimetry, Tide Gauge, Exact Repeat Missions</p>

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.

Impact of sea level variations on hydrographic survey around Taiwan

2020 ◽  
Author(s):  
Wen-Hau Lan ◽  
Chung-Yen Kuo ◽  
Sheng-Fong Lin ◽  
Chien-Hsing Lu
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Temporal Changes ◽  
Altimeter Data ◽  
Ocean Tide ◽  
Ocean Tides ◽  
Sea Level Anomalies ◽  
Sea Level Variations ◽  
Ocean Tide Models ◽  
And Sea Level

<p>Taiwan is an island entirely surrounded by oceans, so living and economics are significantly influenced by the oceans. The electronic navigational chart system is extremely important for improving the safety of marine navigation and ocean depth is the essential data for electronic charts. Sea surface variations affected by ocean tide and sea level change are the main error sources in hydrographic surveys since the traditional tidal correction only using tide gauge stations, ignoring geographically non-uniform ocean tides and sea level anomalies around Taiwan. In this research, we evaluate two factors impacting the accuracy of hydrographic surveys, including ocean tides and seasonal sea level variations, using tide gauge records, satellite altimeter data and ocean tide models around Taiwan, and also analyze the accuracy of the ocean tide models around Taiwan. In addition, sea level anomalies are strongly influenced by climate changes in recent years. An understanding of seasonal sea level cycle and its spatial and temporal changes are importance because its temporal changes can result in the variation of the frequency and magnitude of coastal hazards. Therefore, we will apply the Ensemble Empirical Mode Decomposition to sea level data to assess the stability of the long-term seasonal sea level fluctuations with time.</p>

scholarly journals A study of the chilean vertical network through global geopotential models and the cnes cls 2011 global mean sea surface

2014 ◽  
Vol 20 (2) ◽  
pp. 300-316 ◽  
Author(s):  
Henry Montecino Castro ◽  
Aharon Cuevas Cordero ◽  
Sílvio Rogério Correia de Freitas
Keyword(s):  
Vertical Component ◽  
Sea Surface ◽  
Tide Gauges ◽  
Satellite Mission ◽  
Geopotential Models ◽  
Mean Sea Surface ◽  
The Mean ◽  
Combining Information ◽  
Global Mean ◽  
Goce Satellite

Most aspects related to the horizontal component of the Geocentric Reference System for the Americas (SIRGAS) have been solved. However, in the case of the vertical component there are still aspects of definition, national realizations and continental unification still not accomplished. Chile is no exception; due to its particular geographic characteristics, a number of tide gauges (TG) had to be installed in the coast from which the leveling lines that compose the Chilean Vertical Network (CHVN) were established. This study explored the offsets of the CHVN by two different approaches; one geodetic and one oceanographic. In the first approach, the offsets were obtained in relation to the following Global Geopotential Models (GGM): the satellite-only model (unbiased) GO_CONS_gcf_2_tim_r3 derived from GOCE satellite mission; EGM2008 (combined-biased); and GOEGM08, combining information from the GO_CONS_gcf_2_tim_r3 in long wavelengths (n max~200) with the mean/short wavelengths of EGM2008 (n>200). In the oceanographic method, we used the CNES CLS 2011 Global Mean Sea surface and EIGEN_GRACE_5C GGM to obtain the values of MDT at the different TG. We also evaluated the CHVN in relation to different GGMs. The results showed consistency between the values obtained by the two methods at the TG of Valparaíso and Puerto Chacabuco. In terms of the evaluation of the GGM, GOEGM08 produced the best results.

Impact of the CHAMP Mission on Estimating the Mean Sea Surface

2005 ◽  
pp. 205-210
Author(s):  
Verena Seufer ◽  
Jens Schröter ◽  
Manfred Wenzel ◽  
Wolfgang Keller
Keyword(s):  
Sea Surface ◽  
Mean Sea Surface ◽  
The Mean
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