Dissipation in the Bay of Bengal from a Seaglider

Author(s):  
Gillian Damerell ◽  
Peter Sheehan ◽  
Rob Hall ◽  
Adrian Matthews ◽  
Karen Heywood

<p>In July 2016, a Seaglider equipped with a microstructure sensor system was deployed in the southern Bay of Bengal at 7° 54.0′ N, 89° 4.5′ E.  162 profiles (of which 146 were to 1000 m) of microstructure shear and temperature were collected as a time series at the same location.  Dissipation is calculated independently from both shear and temperature.  The time-average profile shows high dissipation (nearly 1×10<sup>-5</sup> W kg<sup>-1</sup>) near the surface, dropping rapidly over the uppermost 50 m to ~1×10<sup>-7</sup> W kg<sup>-1</sup>, followed by a more gradual decrease to ~5×10<sup>-10</sup> W kg<sup>-1</sup> at 300m.  A band of slightly higher dissipation around 500 m (~8×10<sup>-10</sup> W kg<sup>-1</sup>) could facilitate an increased vertical flux of nutrients, heat, salinity, etc at these depths.  From 600 to 1000 m dissipation remains roughly constant at ~1×10<sup>-10</sup> W kg<sup>-1</sup>.  Variability of the near surface dissipation in response to atmospheric forcing is also discussed.</p>

Author(s):  
Alexander Soloviev ◽  
Roger Lukas ◽  
Sharon DeCarlo ◽  
Jefrey Snyder ◽  
Anatoli Arjannikov ◽  
...  

2005 ◽  
Vol 35 (3) ◽  
pp. 395-400 ◽  
Author(s):  
S S C. Shenoi ◽  
D. Shankar ◽  
S. R. Shetye

Abstract The accuracy of data from the Simple Ocean Data Assimilation (SODA) model for estimating the heat budget of the upper ocean is tested in the Arabian Sea and the Bay of Bengal. SODA is able to reproduce the changes in heat content when they are forced more by the winds, as in wind-forced mixing, upwelling, and advection, but not when they are forced exclusively by surface heat fluxes, as in the warming before the summer monsoon.


2020 ◽  
Author(s):  
Marcos Wander Rodrigues ◽  
Luis Enrique Zárate

The use of sensors in environments where they require constant monitoring has been increasing in recent years. The main goal is to guarantee the effectiveness, safety, and smooth functioning of the system. To identify the occurrence of abnormal events, we propose a methodology that aims to detect patterns that can lead to abrupt changes in the behavior of the sensor signals. To achieve this objective, we provide a strategy to characterize the time series, and we use a clustering technique to analyze the temporal evolution of the sensor system. To validate our methodology, we propose the clusters’ stability index by windowing. Also, we have developed a parameterizable time series generator, which allows us to represent different operational scenarios for a sensor system where extreme anomalies may arise.


Author(s):  
Sarbani Basu ◽  
William J. Chaplin

This chapter considers some of the fundamentals associated with the basic datasets from which the asteroseismic and other intrinsic stellar parameters are extracted (usually lightcurves of photometric observations or time series of Doppler velocity observations). In particular, the chapter looks at how the observational technique affects the amplitudes of the observed oscillations. It also introduces the other intrinsic stellar signals that manifest in the data, specifically those due to granulation (signatures of near-surface convection) and magnetic activity. The chapter's aim is to familiarize the reader with the basic content of the typical data and lay some important groundwork for the detailed presentations that follow in the next two chapters.


2019 ◽  
Vol 10 (1) ◽  
pp. 73 ◽  
Author(s):  
Einar Agletdinov ◽  
Dmitry Merson ◽  
Alexei Vinogradov

A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.


Sign in / Sign up

Export Citation Format

Share Document