Tidal Current and Energy Flux in the Bohai Strait (China) Based on Multi-month In-situ Observations

AbstractThe baroclinic tides are a crucial source of mixing energy into the deep ocean; however, the incoherent portion of the spectrum is not well examined because it is difficult to observe. This study estimates the coherent and incoherent M2 internal tide energy fluxes in the Philippine Sea using a primitive equation model that resolves the M2 barotropic and baroclinic tides and the time-evolving atmospherically forced eddying circulation. A time-mean, incoherent, internal tide energy flux of 25% of the coherent energy flux is found to emanate eastward into the Philippine Sea from the Luzon Strait and a time-mean incoherent portion of 30% of the coherent energy flux propagates westward into the South China Sea (SCS). The incoherent internal tide energy results from baroclinic tide generation and propagation variability. Quantifying the incoherent portion estimates the energy missing from altimeter-derived or line-integral acoustic measurements and places short-lived, in situ observations in the context of variability.

Download Full-text

Observations of the Tasman Sea Internal Tide Beam

Journal of Physical Oceanography ◽

10.1175/jpo-d-17-0116.1 ◽

2018 ◽

Vol 48 (6) ◽

pp. 1283-1297 ◽

Cited By ~ 8

Author(s):

Amy F. Waterhouse ◽

Samuel M. Kelly ◽

Zhongxiang Zhao ◽

Jennifer A. MacKinnon ◽

Jonathan D. Nash ◽

...

Keyword(s):

Plane Wave ◽

Energy Flux ◽

Internal Tide ◽

Surface Displacement ◽

Internal Tides ◽

Near Surface ◽

Tasman Sea ◽

Stationary Energy ◽

In Situ Observations

AbstractLow-mode internal tides, a dominant part of the internal wave spectrum, carry energy over large distances, yet the ultimate fate of this energy is unknown. Internal tides in the Tasman Sea are generated at Macquarie Ridge, south of New Zealand, and propagate northwest as a focused beam before impinging on the Tasmanian continental slope. In situ observations from the Tasman Sea capture synoptic measurements of the incident semidiurnal mode-1 internal-tide, which has an observed wavelength of 183 km and surface displacement of approximately 1 cm. Plane-wave fits to in situ and altimetric estimates of surface displacement agree to within a measurement uncertainty of 0.3 cm, which is the same order of magnitude as the nonstationary (not phase locked) mode-1 tide observed over a 40-day mooring deployment. Stationary energy flux, estimated from a plane-wave fit to the in situ observations, is directed toward Tasmania with a magnitude of 3.4 ± 1.4 kW m−1, consistent with a satellite estimate of 3.9 ± 2.2 kW m−1. Approximately 90% of the time-mean energy flux is due to the stationary tide. However, nonstationary velocity and pressure, which are typically 1/4 the amplitude of the stationary components, sometimes lead to instantaneous energy fluxes that are double or half of the stationary energy flux, overwhelming any spring–neap variability. Despite strong winds and intermittent near-inertial currents, the parameterized turbulent-kinetic-energy dissipation rate is small (i.e., 10−10 W kg−1) below the near surface and observations of mode-1 internal tide energy-flux convergence are indistinguishable from zero (i.e., the confidence intervals include zero), indicating little decay of the mode-1 internal tide within the Tasman Sea.

Download Full-text

Physical controls on frost events in the central Andes of Peru using in situ observations and energy flux models

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2017.02.019 ◽

2017 ◽

Vol 239 ◽

pp. 58-70 ◽

Cited By ~ 6

Author(s):

Miguel Saavedra ◽

Ken Takahashi

Keyword(s):

Energy Flux ◽

Central Andes ◽

Physical Controls ◽

In Situ Observations

Download Full-text

In situ observations of electromigration induced void behavior

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100137598 ◽

1995 ◽

Vol 53 ◽

pp. 244-245

Author(s):

T. Marieb ◽

J. C. Bravman ◽

P. Flinn ◽

D. Gardner ◽

M. Madden

Keyword(s):

Electron Microscopy ◽

Void Nucleation ◽

Plan View ◽

Transmission Electron ◽

Nucleation Sites ◽

Microelectronics Industry ◽

In Situ Observations ◽

Backscatter Electron Detector ◽

Voltage Scanning

Electromigration and stress voiding have been active areas of research in the microelectronics industry for many years. While accelerated testing of these phenomena has been performed for the last 25 years[1-2], only recently has the introduction of high voltage scanning electron microscopy (HVSEM) made possible in situ testing of realistic, passivated, full thickness samples at high resolution.With a combination of in situ HVSEM and post-testing transmission electron microscopy (TEM) , electromigration void nucleation sites in both normal polycrystalline and near-bamboo pure Al were investigated. The effect of the microstructure of the lines on the void motion was also studied.The HVSEM used was a slightly modified JEOL 1200 EX II scanning TEM with a backscatter electron detector placed above the sample[3]. To observe electromigration in situ the sample was heated and the line had current supplied to it to accelerate the voiding process. After testing lines were prepared for TEM by employing the plan-view wedge technique [6].

Download Full-text

In situ observations of coral spawning and spawn slick at Lankayan Island, Sabah, Malaysia

Marine Biodiversity ◽

10.1007/s12526-020-01158-5 ◽

2021 ◽

Vol 51 (1) ◽

Author(s):

Sze Hoon Gan ◽

Zarinah Waheed ◽

Fung Chen Chung ◽

Davies Austin Spiji ◽

Leony Sikim ◽

...

Keyword(s):

Coral Spawning ◽

In Situ Observations

Download Full-text

Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

Remote Sensing ◽

10.3390/rs13071250 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1250

Author(s):

Yanxing Hu ◽

Tao Che ◽

Liyun Dai ◽

Lin Xiao

Keyword(s):

Machine Learning ◽

Northern Hemisphere ◽

Snow Depth ◽

Learning Algorithm ◽

Random Forest Regression ◽

Machine Learning Methods ◽

Long Time ◽

In Situ Observations ◽

Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

Download Full-text

In situ observations of spiral growth on ice crystal surfaces

Physical Review Materials ◽

10.1103/physrevmaterials.2.093402 ◽

2018 ◽

Vol 2 (9) ◽

Cited By ~ 1

Author(s):

Ken-ichiro Murata ◽

Ken Nagashima ◽

Gen Sazaki

Keyword(s):

Spiral Growth ◽

Ice Crystal ◽

Crystal Surfaces ◽

In Situ Observations

Download Full-text

Pelagic deep-sea fauna observed on video transects in the southern Norwegian Sea

Polar Biology ◽

10.1007/s00300-021-02840-5 ◽

2021 ◽

Author(s):

Philipp Neitzel ◽

Aino Hosia ◽

Uwe Piatkowski ◽

Henk-Jan Hoving

Keyword(s):

First Record ◽

Observation System ◽

In Situ Observation ◽

Norwegian Sea ◽

Video Recordings ◽

In Situ Observations ◽

Abundance And Distribution ◽

Oceanic Food Webs ◽

Deep Sea Fauna

AbstractObservations of the diversity, distribution and abundance of pelagic fauna are absent for many ocean regions in the Atlantic, but baseline data are required to detect changes in communities as a result of climate change. Gelatinous fauna are increasingly recognized as vital players in oceanic food webs, but sampling these delicate organisms in nets is challenging. Underwater (in situ) observations have provided unprecedented insights into mesopelagic communities in particular for abundance and distribution of gelatinous fauna. In September 2018, we performed horizontal video transects (50–1200 m) using the pelagic in situ observation system during a research cruise in the southern Norwegian Sea. Annotation of the video recordings resulted in 12 abundant and 7 rare taxa. Chaetognaths, the trachymedusaAglantha digitaleand appendicularians were the three most abundant taxa. The high numbers of fishes and crustaceans in the upper 100 m was likely the result of vertical migration. Gelatinous zooplankton included ctenophores (lobate ctenophores,Beroespp.,Euplokamissp., and an undescribed cydippid) as well as calycophoran and physonect siphonophores. We discuss the distributions of these fauna, some of which represent the first record for the Norwegian Sea.

Download Full-text