scholarly journals Moored Turbulence Measurements using Pulse-Coherent Doppler Sonar

2021 ◽  
Author(s):  
Seth F. Zippel ◽  
J. Thomas Farrar ◽  
Christopher J. Zappa ◽  
Una Miller ◽  
Louis St. Laurent ◽  
...  
Keyword(s):  
Mechanical Design ◽  
Surface Fluxes ◽  
Upper Ocean ◽  
Data Sets ◽  
Regional Study ◽  
Ocean Turbulence ◽  
Dissipation Rates ◽  
Optimal Programming ◽  
Long Time ◽  
Surface Buoyancy

AbstractUpper-ocean turbulence is central to the exchanges of heat, momentum, and gasses across the air/sea interface, and therefore plays a large role in weather and climate. Current understanding of upper-ocean mixing is lacking, often leading models to misrepresent mixed-layer depths and sea surface temperature. In part, progress has been limited due to the difficulty of measuring turbulence from fixed moorings which can simultaneously measure surface fluxes and upper-ocean stratification over long time periods. Here we introduce a direct wavenumber method for measuring Turbulent Kinetic Energy (TKE) dissipation rates, ϵ, from long-enduring moorings using pulse-coherent ADCPs. We discuss optimal programming of the ADCPs, a robust mechanical design for use on a mooring to maximize data return, and data processing techniques including phase-ambiguity unwrapping, spectral analysis, and a correction for instrument response. The method was used in the Salinity Processes Upper-ocean Regional Study (SPURS) to collect two year-long data sets. We find the mooring-derived TKE dissipation rates compare favorably to estimates made nearby from a microstructure shear probe mounted to a glider during its two separate two-week missions for (10−8) ≤ ϵ ≤ (10−5) m2 s−3. Periods of disagreement between turbulence estimates from the two platforms coincide with differences in vertical temperature profiles, which may indicate that barrier layers can substantially modulate upper-ocean turbulence over horizontal scales of 1-10 km. We also find that dissipation estimates from two different moorings at 12.5 m, and at 7 m are in agreement with the surface buoyancy flux during periods of strong nighttime convection, consistent with classic boundary layer theory.

scholarly journals SEA-POL Goes to Sea

2019 ◽  
Vol 100 (11) ◽  
pp. 2285-2301 ◽  
Author(s):  
Steven A. Rutledge ◽  
V. Chandrasekar ◽  
Brody Fuchs ◽  
Jim George ◽  
Francesc Junyent ◽  
...  
Keyword(s):  
Doppler Radar ◽  
The United States ◽  
Upper Ocean ◽  
State University ◽  
Dual Polarization ◽  
Regional Study ◽  
Ocean Turbulence ◽  
Colorado State University ◽  
Weather Radars ◽  
Microphysical Processes

AbstractA new, advanced radar has been developed at Colorado State University (CSU). The Sea-Going Polarimetric (SEA-POL) radar is a C-band, polarimetric Doppler radar specifically designed to deploy on research ships. SEA-POL is the first such weather radar developed in the United States. Ship-based weather radars have a long history, dating back to GATE in 1974. The GATE radars measured only reflectivity. After GATE, ship radars also provided Doppler measurements. SEA-POL represents the next advancement by adding dual-polarization technology, the ability to transmit and receive both horizontal and vertical polarizations. This configuration provides information about hydrometeor size, shape, and phase. As a result, superior rain-rate estimates are afforded by the dual-polarization technology, along with hydrometeor identification and overall improved data quality. SEA-POL made its first deployment as part of the Salinity Processes in the Upper Ocean Regional Study, second field phase (SPURS-2) fall 2017 cruise to the eastern tropical Pacific, sailing on the R/V Roger Revelle. SPURS-2 was a field project to investigate the fate of freshwater deposited on the ocean’s surface. Oceanographers are keenly interested in how fast these freshwater patches mix out by wind and upper-ocean turbulence, as the less dense rainfall sitting atop the salty ocean inhibits mixing through increased stability. To this end, during SPURS-2, SEA-POL produced rain maps identifying the location of freshwater lenses on the ocean’s surface thereby providing context for measurements of SST and salinity. Examples of SEA-POL polarization measurements are also discussed to assess microphysical processes within oceanic convection. Future ocean-based field campaigns will now benefit from SEA-POL’s advanced dual-polarization technology.

Oil plumes and dispersion in Langmuir, upper-ocean turbulence: Large-eddy simulations and K-profile parameterization

2015 ◽  
Vol 120 (7) ◽  
pp. 4729-4759 ◽  
Author(s):  
Di Yang ◽  
Bicheng Chen ◽  
Marcelo Chamecki ◽  
Charles Meneveau
Keyword(s):  
Large Eddy Simulations ◽  
Upper Ocean ◽  
Ocean Turbulence ◽  
Large Eddy

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

scholarly journals Construction of merged satellite total O<sub>3</sub> and NO<sub>2</sub> time series in the tropics for trend studies and evaluation by comparison to NDACC SAOZ measurements

2014 ◽  
Vol 7 (10) ◽  
pp. 3337-3354 ◽  
Author(s):  
M. Pastel ◽  
J.-P. Pommereau ◽  
F. Goutail ◽  
A. Richter ◽  
A. Pazmiño ◽  
...  
Keyword(s):  
Time Series ◽  
Lower Stratosphere ◽  
Atmospheric Composition ◽  
Data Sets ◽  
Composition Change ◽  
Long Time ◽  
Uv Visible ◽  
The Tropics ◽  
Lower Noise ◽  
Total Column

Abstract. Long time series of ozone and NO2 total column measurements in the southern tropics are available from two ground-based SAOZ (Système d'Analyse par Observation Zénithale) UV-visible spectrometers operated within the Network for the Detection of Atmospheric Composition Change (NDACC) in Bauru (22° S, 49° W) in S-E Brazil since 1995 and Reunion Island (21° S, 55° E) in the S-W Indian Ocean since 1993. Although the stations are located at the same latitude, significant differences are observed in the columns of both species, attributed to differences in tropospheric content and equivalent latitude in the lower stratosphere. These data are used to identify which satellites operating during the same period, are capturing the same features and are thus best suited for building reliable merged time series for trend studies. For ozone, the satellites series best matching SAOZ observations are EP-TOMS (1995–2004) and OMI-TOMS (2005–2011), whereas for NO2, best results are obtained by combining GOME version GDP5 (1996–2003) and SCIAMACHY – IUP (2003–2011), displaying lower noise and seasonality in reference to SAOZ. Both merged data sets are fully consistent with the larger columns of the two species above South America and the seasonality of the differences between the two stations, reported by SAOZ, providing reliable time series for further trend analyses and identification of sources of interannual variability in the future analysis.

scholarly journals Multi Objective Optimization Based Feature Selection Algorithms for Big Data Analytics: A Review

2021 ◽  
pp. 1-4
Author(s):  
Aakriti Shukla ◽  
◽  
Dr Damodar Prasad Tiwari ◽  
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Data Sets ◽  
Big Data Applications ◽  
High Workload ◽  
Long Time ◽  
Analytical Result ◽  
Result Analysis

Dimension reduction or feature selection is thought to be the backbone of big data applications in order to improve performance. Many scholars have shifted their attention in recent years to data science and analysis for real-time applications using big data integration. It takes a long time for humans to interact with big data. As a result, while handling high workload in a distributed system, it is necessary to make feature selection elastic and scalable. In this study, a survey of alternative optimizing techniques for feature selection are presented, as well as an analytical result analysis of their limits. This study contributes to the development of a method for improving the efficiency of feature selection in big complicated data sets.

scholarly journals A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

2014 ◽  
Vol 11 (6) ◽  
pp. 6139-6166 ◽  
Author(s):  
T. R. Marthews ◽  
S. J. Dadson ◽  
B. Lehner ◽  
S. Abele ◽  
N. Gedney
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Global Scale ◽  
Surface Fluxes ◽  
Data Sets ◽  
Topographic Index ◽  
Surface Water Flow ◽  
Land Surface Water ◽  
Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

Autonomous Ocean Turbulence Measurements From a Moored Upwardly Rising Profiler Based on a Buoyancy-Driven Mechanism

2017 ◽  
Vol 51 (4) ◽  
pp. 12-22 ◽  
Author(s):  
Xiuyan Liu ◽  
Xin Luan ◽  
Z. Daniel Deng ◽  
Dalei Song ◽  
Shengbo Zang ◽  
...  
Keyword(s):  
The Novel ◽  
Ocean Turbulence ◽  
Turbulence Measurements ◽  
Sea Trial ◽  
Dissipation Rates ◽  
Turbulence Mixing ◽  
Mooring Cable ◽  
High Vertical Resolution ◽  
New Perspective

AbstractAn autonomous Moored Reciprocating Vertical Profiler (MRVP) has been developed and tested for measuring ocean turbulence. The MRVP is designed to combine the advantages of long-term moored measurements at specified depths with those of short-term ship-supported continuous profiling performed at high vertical resolution. The profiler is programmed to repeat vertical motions autonomously along the mooring cable based on a buoyancy-driven mechanism. A sea trial has been conducted in the South China Sea to evaluate the performance of the profiler. The shear probe data are unreliable when the flow past sensors is not sufficiently greater than an estimate of turbulent velocity. For 65% of the dataset, turbulence measurements are of high quality and the magnitude of dissipation rates is up to O(10−10) W kg−1. To minimize the contamination induced by instrument vibration and improve the estimation of turbulent kinetic energy terms, an advanced cross-spectrum algorithm is implemented to the measured shear data. The corrected spectra agreed well with the empirical Nasmyth spectrum, and dissipation rates had averagely decreased a factor of 2 and 8 times lower than the raw spectra. The autonomous MRVP is proven to be a stable platform, and the novel upward measurement provides a new perspective for measuring long-term time series of turbulence mixing.

Community Replacement Pathways: What Do Fossil Sequences Reveal About Marine Ecosystem Transitions?

1990 ◽  
Vol 5 ◽  
pp. 262-272
Author(s):  
William Miller
Keyword(s):  
Fossil Record ◽  
Large Scale ◽  
Marine Ecosystem ◽  
Small Scale ◽  
Data Sets ◽  
Mass Extinctions ◽  
Ultimate Cause ◽  
Long Time ◽  
History Of ◽  
Time And Energy

Paleontologists have lavished much time and energy on description and explanation of large-scale patterns in the fossil record (e.g., mass extinctions, histories of monophyletic taxa, deployment of major biogeographic units), while paying comparatively little attention to biologic patterns preserved only in local stratigraphic sequences. Interpretation of the large-scale patterns will always be seen as the chief justification for the science of paleontology, but solving problems framed by long time spans and large areas is rife with tenuous inference and patterns are prone to varied interpretation by different investigators using virtually the same data sets (as in the controversy over ultimate cause of the terminal Cretaceous extinctions). In other words, the large-scale patterns in the history of life are the true philosophical property of paleontology, but there will always be serious problems in attempting to resolve processes that transpired over millions to hundreds-of-millions of years and encompassed vast areas of seafloor or landscape. By contrast, less spectacular and more commonplace changes in local habitats (often related to larger-scale events and cycles) and attendant biologic responses are closer to our direct experience of the living world and should be easier to interpret unequivocally. These small-scale responses are reflected in the fossil record at the scale of local outcrops.

scholarly journals Mosaic versus dual source approaches for modelling the surface energy balance of a semi-arid land

1999 ◽  
Vol 3 (2) ◽  
pp. 247-258 ◽  
Author(s):  
G. Boulet ◽  
A. Chehbouni ◽  
I. Braud ◽  
M. Vauclin
Keyword(s):  
Surface Energy ◽  
Bare Soil ◽  
Surface Fluxes ◽  
Surface Energy Budget ◽  
Arid Land ◽  
Data Sets ◽  
Sparse Vegetation ◽  
Land Surfaces ◽  
Using Data ◽  
Semi Arid

Abstract. Two-layer parameterisation of the surface energy budget proves to be realistic for sparse but homogeneously distributed vegetation. For semi-arid land surfaces however, sparse vegetation is usually interspersed by large patches of unshaded bare soil which may interact directly with the atmosphere with little interference with the vegetation. Therefore such surfaces might not be realistically represented by a two-layer parameterisation. The objective of this study is to investigate the issue of representing water and energy transfer processes in arid and semi-arid regions. Two different surface schemes, namely the classic two layer (one-compartment) approach and a two adjacent compartment ("mosaic") approach are used. The performance of both schemes is documented using data sets collected over two sparsely vegetated surfaces in the San Pedro river basin: homogeneously distributed grassland and heterogeneously distributed shrubs. In the latter case the mosaic scheme seems to be more realistic given the quality of the temperature estimates. But no clear statement can be made on the efficiency of both schemes for the total fluxes. Over each site, we investigate the possibility of artificially modifying some of the surface parameters in order to get the surface fluxes simulated by the one-compartment scheme to reproduce the two-compartment ones. The "cost" associated with this process in terms of surface temperature estimates is eventually discussed.

Data Driven Model Identification for a Chaotic Pendulum With Variable Interaction Potential

2020 ◽  
Author(s):  
Melih C. Yesilli ◽  
Firas A. Khasawneh
Keyword(s):  
Numerical Models ◽  
Model Identification ◽  
Potential Interaction ◽  
Data Driven ◽  
Estimation Methods ◽  
Total Variation Regularization ◽  
Data Sets ◽  
Derivative Estimation ◽  
Long Time ◽  
And Performance

Abstract Data driven model identification methods have grown increasingly popular due to enhancements in measuring devices and data mining. They provide a useful approach for comparing the performance of a device to the simplified model that was used in the design phase. One of the modern, popular methods for model identification is Sparse Identification of Nonlinear Dynamics (SINDy). Although this approach has been widely investigated in the literature using mostly numerical models, its applicability and performance with physical systems is still a topic of current research. In this paper we extend SINDy to identify the mathematical model of a complicated physical experiment of a chaotic pendulum with a varying potential interaction. We also test the approach using a simulated model of a nonlinear, simple pendulum. The input to the approach is a time series, and estimates of its derivatives. While the standard approach in SINDy is to use the Total Variation Regularization (TVR) for derivative estimates, we show some caveats for using this route, and we benchmark the performance of TVR against other methods for derivative estimation. Our results show that the estimated model coefficients and their resulting fit are sensitive to the selection of the TVR parameters, and that most of the available derivative estimation methods are easier to tune than TVR. We also highlight other guidelines for utilizing SINDy to avoid overfitting, and we point out that the fitted model may not yield accurate results over long time scales. We test the performance of each method for noisy data sets and provide both experimental and simulation results. We also post the files needed to build and reproduce our experiment in a public repository.

Sign in / Sign up

Export Citation Format

Share Document