scholarly journals Improving Sea Level Anomaly Precision from Satellite Altimetry Using Parameter Correction in the Red Sea

2020 ◽  
Vol 12 (5) ◽  
pp. 764
Author(s):  
Ahmed M. Taqi ◽  
Abdullah M. Al-Subhi ◽  
Mohammed A. Alsaafani ◽  
Cheriyeri P. Abdulla
Keyword(s):  
Sea Level ◽  
Red Sea ◽  
Distribution Centers ◽  
Sea Level Anomaly ◽  
Times Series ◽  
Sea State Bias ◽  
Data Points ◽  
The Times ◽  
Environmental Corrections ◽  
Along Track

An improved Fourier series model (FSM01) method is used in geophysical and environmental corrections to enhance the final product of the along-track Jason-2 sea level anomaly (SLA) data and extend it near the Red Sea borders. In this study, the ionospheric correction range, wet tropospheric correction range, sea state bias correction range, and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170, and 234) earlier neglected by the distribution centers and extend the tracks toward the coast. The FSM01 SLA is compared with Jason-2 SLA and Archiving Validation and Interpretation of Satellite Oceanographic (AVISO) SLA for the available five tracks, in which the FSM01 SLA shows a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to filling the gaps in the times series of all tracks. The newly retrieved tracks are also compared with those retrieved by AVISO, and both data points show similar variability, with FSM01 SLA showing no gaps in the time series. The FSM01 SLA was also extended toward the coast and showed high correlation with the coastal tide measurements.

scholarly journals Improving Sea Level Anomaly Precision from Satellite Altimetry Using Parameters Correction in the Red Sea

2020 ◽  
Author(s):  
Ahmed Mohammed Taqi ◽  
Abdullah Mohammed Al-Subhi ◽  
Mohammed Ali AlSaafani
Keyword(s):  
Red Sea ◽  
Distribution Centers ◽  
Sea State ◽  
Ionospheric Correction ◽  
Times Series ◽  
Sea State Bias ◽  
The Times ◽  
Environmental Corrections ◽  
Along Track ◽  
Good Agreement

An improved FSM method is used in geophysical and environmental corrections to enhance the final product of the along track Jason-2 SLA data and extend it near the Red Sea borders. In this study the ionospheric correction range, wet tropospheric correction range, sea state bias correction range and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170 and 234), earlier neglected by the distribution centers, and extend the tracks towards the coast. The FSM01 SLA is compared with Jason-2 SLA and AVISO SLA for the available 5 tracks, in which the FSM01 SLA show a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to that it fills the gaps in the times series of all tracks. The new retrieved tracks also compared with those retrieved by AVISO, both data show similar variability, with FSM01 SLA show no gaps in the time series. The FSM01 SLA also extended towards the coast and show high correlation with the coastal tide measurements.

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.

scholarly journals Introducing Along-Track Error Correlations for Altimetry Data in a Regional Ocean Prediction System

2019 ◽  
Vol 36 (8) ◽  
pp. 1657-1674 ◽  
Author(s):  
Andrea Storto ◽  
Paolo Oddo ◽  
Elisa Cozzani ◽  
Emanuel Ferreira Coelho
Keyword(s):  
Sea Level ◽  
Correlation Radius ◽  
Prediction System ◽  
Altimetry Data ◽  
Spatial Correlations ◽  
Sea Level Anomaly ◽  
Ligurian Sea ◽  
Track Error ◽  
Ocean Prediction ◽  
Along Track

AbstractBecause of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency.

Correlation of sea level anomaly amplitude modulation with El Ni˜o events in equatorial Pacific

Eos ◽  
1994 ◽  
Vol 75 (26) ◽  
pp. 295 ◽  
Author(s):  
Quanan Zheng ◽  
Xiao-Hai Yan ◽  
Chung-Ru Ho ◽  
Vic Klemas ◽  
Robert E. Chene ◽  
...  
Keyword(s):  
Sea Level ◽  
Amplitude Modulation ◽  
Equatorial Pacific ◽  
Sea Level Anomaly

Characteristics and possible causes of the seasonal sea level anomaly along the South China Sea coast

2017 ◽  
Vol 36 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Hui Wang ◽  
Kexiu Liu ◽  
Zhigang Gao ◽  
Wenjing Fan ◽  
Shouhua Liu ◽  
...  
Keyword(s):  
South China Sea ◽  
Sea Level ◽  
South China ◽  
The South China Sea ◽  
The South ◽  
Sea Level Anomaly ◽  
China Sea ◽  
Sea Coast

WALIS - Towards a global database of Last Interglacial sea-level proxies.

2021 ◽  
Author(s):  
Alessio Rovere ◽  
Deirdre Ryan ◽  
Matteo Vacchi ◽  
Alexander Simms ◽  
Andrea Dutton ◽  
...  
Keyword(s):  
Sea Level ◽  
Research Council ◽  
Geological Data ◽  
Sea Level Changes ◽  
Last Interglacial ◽  
Global Database ◽  
The World ◽  
First Results ◽  
Data Points ◽  
The Subject

<p>The standardization of geological data, and their compilation into geodatabases, is essential to allow more coherent regional and global analyses. In sea-level studies, the compilation of databases containing details on geological paleo sea-level proxies has been the subject of decades of work. This was largely spearheaded by the community working on Holocene timescales. While several attempts were also made to compile data from older interglacials, a truly comprehensive approach was missing. Here, we present the ongoing efforts directed to create the World Atlas of Last Interglacial Shorelines (WALIS), a project spearheaded by the PALSEA (PAGES/INQUA) community and funded by the European Research Council (ERC StG 802414). The project aims at building a sea-level database centered on the Last Interglacial (Marine Isotope Stage 5e, 125 ka), a period of time considered as an "imperfect analog" for a future warmer climate. The database is composed of 17 tables embedded into a mySQL framework with a total of more than 500 single fields to describe several properties related to paleo sea-level proxies, dated samples and metadata. In this presentation, we will show the first results of the global compilation, which includes nearly 2000 data points and will discuss its relevance in answering some of the most pressing questions related to sea-level changes in past warmer worlds. </p>

scholarly journals Impact of SLA assimilation in the Sicily Channel Regional Model: model skills and mesoscale features

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 485-496 ◽  
Author(s):  
A. Olita ◽  
S. Dobricic ◽  
A. Ribotti ◽  
L. Fazioli ◽  
A. Cucco ◽  
...  
Keyword(s):  
Sea Level ◽  
Qualitative Evaluation ◽  
Regional Model ◽  
Reynolds Stresses ◽  
Mean Flow ◽  
Qualitative Comparison ◽  
Mesoscale Structures ◽  
Sicily Channel ◽  
Along Track ◽  
The Impact

Abstract. The impact of the assimilation of MyOcean sea level anomalies along-track data on the analyses of the Sicily Channel Regional Model was studied. The numerical model has a resolution of 1/32° degrees and is capable to reproduce mesoscale and sub-mesoscale features. The impact of the SLA assimilation is studied by comparing a simulation (SIM, which does not assimilate data) with an analysis (AN) assimilating SLA along-track multi-mission data produced in the framework of MyOcean project. The quality of the analysis was evaluated by computing RMSE of the misfits between analysis background and observations (sea level) before assimilation. A qualitative evaluation of the ability of the analyses to reproduce mesoscale structures is accomplished by comparing model results with ocean colour and SST satellite data, able to detect such features on the ocean surface. CTD profiles allowed to evaluate the impact of the SLA assimilation along the water column. We found a significant improvement for AN solution in terms of SLA RMSE with respect to SIM (the averaged RMSE of AN SLA misfits over 2 years is about 0.5 cm smaller than SIM). Comparison with CTD data shows a questionable improvement produced by the assimilation process in terms of vertical features: AN is better in temperature while for salinity it gets worse than SIM at the surface. This suggests that a better a-priori description of the vertical error covariances would be desirable. The qualitative comparison of simulation and analyses with synoptic satellite independent data proves that SLA assimilation allows to correctly reproduce some dynamical features (above all the circulation in the Ionian portion of the domain) and mesoscale structures otherwise misplaced or neglected by SIM. Such mesoscale changes also infer that the eddy momentum fluxes (i.e. Reynolds stresses) show major changes in the Ionian area. Changes in Reynolds stresses reflect a different pumping of eastward momentum from the eddy to the mean flow, in turn influencing transports through the channel.

scholarly journals APMFT: Anamoly Prediction Model for Financial Transactions Using Learning Methods in Machine Learning and Deep Learning

2021 ◽  
Author(s):  
R. Priyadarshini ◽  
K. Anuratha ◽  
N. Rajendran ◽  
S. Sujeetha
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Prediction Models ◽  
General Pattern ◽  
High Dimensional ◽  
Learning Methods ◽  
Data Points ◽  
The Times ◽  
Financial Transactions ◽  
Journal Entries

Anamoly is an uncommon and it represents an outlier i.e, a nonconforming case. According to Oxford Dictionary of Mathematics anamoly is defined as an unusal and erroneous observation that usually doesn’t follow the general pattern of drawn population. The process of detecting the anmolies is a process of data mining and it aims at finding the data points or patterns that do not adapt with the actual complete pattern of the data.The study on anamoly behavior and its impact has been done on areas such as Network Security, Finance, Healthcare and Earth Sciences etc. The proper detection and prediction of anamolies are of great importance as these rare observations may carry siginificant information. In today’s finanicial world, the enterprise data is digitized and stored in the cloudand so there is a significant need to detect the anaomalies in financial data which will help the enterprises to deal with the huge amount of auditing The corporate and enterprise is conducting auidts on large number of ledgers and journal entries. The monitoring of those kinds of auidts is performed manually most of the times. There should be proper anamoly detection in the high dimensional data published in the ledger format for auditing purpose. This work aims at analyzing and predicting unusal fraudulent financial transations by emplyoing few Machine Learning and Deep Learning Methods. Even if any of the anamoly like manipulation or tampering of data detected, such anamolies and errors can be identified and marked with proper proof with the help of the machine learning based algorithms. The accuracy of the prediction is increased by 7% by implementing the proposed prediction models.

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