MACRO-GOM: Long Term Multi-Resolution Ocean Current Reanalysis Dataset for the Gulf of Mexico

2021 ◽  
Author(s):  
Neha Groves ◽  
Ashwanth Srinivasan ◽  
Leonid Ivanov ◽  
Jill Storie ◽  
Drew Gustafson ◽  
...  
Keyword(s):  
High Resolution ◽  
Gulf Of Mexico ◽  
Observational Data ◽  
Hydrodynamic Model ◽  
Life Extension ◽  
Ocean Current ◽  
Reanalysis Dataset ◽  
Time Period ◽  
Offshore Industry

Abstract The Gulf of Mexico's unique circulation characteristics pose a particular threat to marine operations and play a significant role in driving the criteria used for design and life extension analyses of offshore infrastructure. Estimates from existing reanalysis datasets used by operators in GOM show less than ideal correlation with in situ measurements and have a limited resolution that disallows for the capture of ocean features of interest. In this paper, we introduce a new high-resolution long-term reanalysis dataset, Multi-resolution Advanced Current Reanalysis for the Ocean – Gulf of Mexico (MACRO-GOM), based on a state-of the-science hydrodynamic model configured specifically for ocean current forecasting and hindcasting services for the offshore industry that assimilates extensive non-conventional observational data. The underlying hydrodynamic model used is the Woods Hole Group – Tendral Ocean Prediction System (WHG-TOPS). MACRO-GOM is being developed at the native resolution of the TOPS-GOM domain, i.e. 1/32° (~3 km) hourly grid for the 1994-2019 time period (25 years). A 3-level downscaling methodology is used wherein observation based estimates are first dynamically interpolated using a 1/4° model before being downscaled to the 1/16° Inter-American Seas (IAS) domain, which in turn is used to generate time-consistent boundary conditions for the 1/32° reanalysis. A multiscale data assimilation technique is used to constrain the model at synoptic and longer time scales. For this paper, a shorter, 5-year reanalysis run was conducted for the 2015-2019 time period for verification against assimilated and unassimilated observations, WHG's proprietary frontal analyses, and other reanalyses. Both the frontal analyses and Notice to Lesses (NTL) rig mounted ADCP data was withheld from assimilation for comparison. Offshore operations in the GOM can benefit from an improved reanalysis dataset capable of assimilating existing non-conventional observational datasets. Existing hindcast and reanalysis model datasets are limited in their ability to comprehensively and reliably quantify the 3D circulation and kinematic properties of the main features partly because of limited assimilation of observational data. MACRO-GOM incorporates all the advantages of available HYCOM-based reanalyses and further enhances the resolution, accuracy, and reliability by the assimilation of over three decades of WHG's proprietary datasets and frontal analyses for continuous model correction and ground-truthing. The final 25-year high resolution dataset will provide highly reliable design and operational criteria for new and existing infrastructure in GOM.

scholarly journals A Deep Learning Model for Forecasting Velocity Structures of the Loop Current System in the Gulf of Mexico

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 934-953
Author(s):  
Ali Muhamed Ali ◽  
Hanqi Zhuang ◽  
James VanZwieten ◽  
Ali K. Ibrahim ◽  
Laurent Chérubin
Keyword(s):  
Deep Learning ◽  
Gulf Of Mexico ◽  
Short Term Memory ◽  
Numerical Models ◽  
Current System ◽  
Ocean Modeling ◽  
Global Ocean ◽  
Ocean Current ◽  
Loop Current

Despite the large efforts made by the ocean modeling community, such as the GODAE (Global Ocean Data Assimilation Experiment), which started in 1997 and was renamed as OceanPredict in 2019, the prediction of ocean currents has remained a challenge until the present day—particularly in ocean regions that are characterized by rapid changes in their circulation due to changes in atmospheric forcing or due to the release of available potential energy through the development of instabilities. Ocean numerical models’ useful forecast window is no longer than two days over a given area with the best initialization possible. Predictions quickly diverge from the observational field throughout the water and become unreliable, despite the fact that they can simulate the observed dynamics through other variables such as temperature, salinity and sea surface height. Numerical methods such as harmonic analysis are used to predict both short- and long-term tidal currents with significant accuracy. However, they are limited to the areas where the tide was measured. In this study, a new approach to ocean current prediction based on deep learning is proposed. This method is evaluated on the measured energetic currents of the Gulf of Mexico circulation dominated by the Loop Current (LC) at multiple spatial and temporal scales. The approach taken herein consists of dividing the velocity tensor into planes perpendicular to each of the three Cartesian coordinate system directions. A Long Short-Term Memory Recurrent Neural Network, which is best suited to handling long-term dependencies in the data, was thus used to predict the evolution of the velocity field in each plane, along each of the three directions. The predicted tensors, made of the planes perpendicular to each Cartesian direction, revealed that the model’s prediction skills were best for the flow field in the planes perpendicular to the direction of prediction. Furthermore, the fusion of all three predicted tensors significantly increased the overall skills of the flow prediction over the individual model’s predictions. The useful forecast period of this new model was greater than 4 days with a root mean square error less than 0.05 cm·s−1 and a correlation coefficient of 0.6.

Change in Mt. Elbrus nival-glacial system in the last century

2020 ◽  
Author(s):  
Sergey Sokratov ◽  
Yuri Seliverstov ◽  
Alla Turchaniniva ◽  
Evgenii Kharkovets ◽  
Heitor Evangelista da Silva
Keyword(s):  
High Resolution ◽  
Lake Sediments ◽  
Digital Camera ◽  
The Other ◽  
Southern Slope ◽  
Time Period ◽  
Lake Formation ◽  
Glacier Ice ◽  
Ice Retreat

<p>We investigated the long-term dynamics of four glaciers that are part of the nival-glacial system of Mount Elbrus and located on its southern slope: Terskol, Garabashi, Malyi Azau, Bol’shoi Azau. The time period of the study covers 1887–2017. Glaciological measurements were carried out using DEM, compiled from early-year maps and from the results of stereo surveys in 2017, made by UAVs and high-resolution digital camera. New results present the change in the area of these glaciers, the elevation of their lowest points and the height of the surface. All these characteristicsindicate decrease of glaciation at the southern slope of Elbrus and intensification of this process in the last decade. Some differences in dynamics of changes of different glaciers can be explained by differences in their morphological types, morphometric indicators, the state of the beds, which we do not have much information about. Additionally, cores of two near glaciers lakes sediments were extracted and analyzed, offering high resolution record of sedimentation. The age of the bottom lake sediments near Malyi Azau glacier corresponds to documented beginning of the lake formation due to glacier ice retreat in 1950<sup>th</sup>. The other lake to the side of the Garabashi glacier was formed much earlier and the upper 15 cm of the lake sediments core is formed between 1893 and 2016.</p><p>The obtained results are compared with the results of other investigations. We believe that the new data of glaciers dynamics is more accurate and more promising in understanding the specific of accumulation and melt in dependence on elevation, slopes aspect s and angle.</p>

Stability and Change in Emotional Intelligence: Exploring the Transition to Young Adulthood

2005 ◽  
Vol 26 (2) ◽  
pp. 100-106 ◽  
Author(s):  
James D.A. Parker ◽  
Donald H. Saklofske ◽  
Laura M. Wood ◽  
Jennifer M. Eastabrook ◽  
Robyn N. Taylor
Keyword(s):  
High School ◽  
Emotional Intelligence ◽  
Postsecondary Education ◽  
Life Transition ◽  
Full Time ◽  
Major Life ◽  
Long Term Stability ◽  
Time Period ◽  
Transition From High School

Abstract. The concept of emotional intelligence (EI) has attracted growing interest from researchers working in various fields. The present study examined the long-term stability (32 months) of EI-related abilities over the course of a major life transition (the transition from high school to university). During the first week of full-time study, a large group of undergraduates completed the EQ-i:Short; 32 months later a random subset of these students (N = 238), who had started their postsecondary education within 24 months of graduating from high school, completed the measures for a second time. The study found EI scores to be relatively stable over the 32-month time period. EI scores were also found to be significantly higher at Time 2; the overall pattern of change in EI-levels was more than can be attributed to the increased age of the participants.

scholarly journals Sociolinguistic Markers of Friendship Among Persons With Dementia in Long-Term Care

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 575-575
Author(s):  
Pamela Saunders
Keyword(s):  
Social Interaction ◽  
Long Term Care ◽  
Ethnographic Study ◽  
Linguistic Features ◽  
Term Care ◽  
Socially Constructed ◽  
Time Period ◽  
Pronominal Reference ◽  
Field Notes

Abstract Sociolinguistics and discourse analysis provide tools through which to examine how friendship is socially constructed through language and communication. Research on social isolation and loneliness reveals the importance of social interaction on the psychological and physical health of older adults. Given that linguistic, communicative, and functional abilities decline as dementia progresses, it is challenging to identify markers of friendship. The Friendship Project is an ethnographic study of social interaction among persons with dementia living in a long-term care setting. The data are from transcripts and field-notes of social interactions among residents with a range of cognitive impairments over a six-month time period. Results reveal that persons with dementia employ specific linguistic features such as narrative, evaluation, evidentials, and pronominal reference to make meaning and create relationships over time. Practical implications will be discussed.

Sign in / Sign up

Export Citation Format

Share Document