In situ observations of topotactic phase transitions in a ferrite memristor

2020 ◽  
Vol 128 (7) ◽  
pp. 074501
Author(s):  
Hyoung Gyun Kim ◽  
Ventaka Raveendra Nallagatla ◽  
Deok-Hwang Kwon ◽  
Chang Uk Jung ◽  
Miyoung Kim
Keyword(s):  
Phase Transitions ◽  
In Situ Observations

scholarly journals In Situ Observations of Phase Transitions in Metastable Nickel (Carbide)/Carbon Nanocomposites

2016 ◽  
Vol 120 (39) ◽  
pp. 22571-22584 ◽  
Author(s):  
Bernhard C. Bayer ◽  
David A. Bosworth ◽  
F. Benjamin Michaelis ◽  
Raoul Blume ◽  
Gerlinde Habler ◽  
...  
Keyword(s):  
Phase Transitions ◽  
Carbon Nanocomposites ◽  
Nickel Carbide ◽  
In Situ Observations

scholarly journals Representation of the Tropical Stratospheric Zonal Wind in Global Atmospheric Reanalyses

2016 ◽  
Author(s):  
Y. Kawatani ◽  
K. Hamilton ◽  
K. Miyazaki ◽  
M. Fujiwara ◽  
J. Anstey
Keyword(s):  
Phase Transitions ◽  
Zonal Wind ◽  
Phase Changes ◽  
Stationary Waves ◽  
Strong Constraint ◽  
Quasi Biennial Oscillation ◽  
Systematic Improvement ◽  
Zonal Wavenumber ◽  
In Situ Observations

Abstract. This paper reports on a project to compare the representation of the monthly-mean zonal wind in the equatorial stratosphere among major global atmospheric reanalysis datasets. The degree of disagreement among the reanalyses is characterized by the standard deviation (SD) of the monthly-mean zonal wind and this depends on latitude, longitude, height and the phase of the quasi-biennial oscillation (QBO). At each height the SD displays a prominent equatorial maximum, indicating the particularly challenging nature of the reanalysis problem in the low-latitude stratosphere. At 50–70 hPa the geographical distributions of SD are closely related to the density of radiosonde observations. The largest SD values are over the eastern Pacific, where few in situ observations are available. At 10–20 hPa the spread among the reanalyses and differences with in situ observations both depend significantly on the QBO phase. Notably the easterly-to-westerly phase transitions in all the reanalyses except MERRA are delayed relative to those directly observed at Singapore. In addition, the timing of the easterly-to-westerly phase transitions displays considerable variability among the different reanalyses and this spread is much larger than for the timing of the westerly-to-easterly phase changes. The eddy component in the monthly mean zonal wind near the equator is dominated by zonal wavenumber 1 and 2 quasi-stationary planetary waves propagating from mid-latitudes in the westerly phase of the QBO. There generally is considerable disagreement among the reanalyses in the details of the quasi-stationary waves near the equator. At each level, there is a tendency for the agreement to be best near the longitude of Singapore, suggesting that the Singapore observations act as a strong constraint on all the reanalyses. Our measures of the quality of the reanalysis clearly show systematic improvement over the period considered (1979–2012). The SD among the reanalysis declines significantly over the record, although the geographical pattern of SD remains nearly constant.

scholarly journals Representation of the tropical stratospheric zonal wind in global atmospheric reanalyses

2016 ◽  
Vol 16 (11) ◽  
pp. 6681-6699 ◽  
Author(s):  
Yoshio Kawatani ◽  
Kevin Hamilton ◽  
Kazuyuki Miyazaki ◽  
Masatomo Fujiwara ◽  
James A. Anstey
Keyword(s):  
Phase Transitions ◽  
Zonal Wind ◽  
Phase Changes ◽  
Stationary Waves ◽  
Central Pacific ◽  
Strong Constraint ◽  
Quasi Biennial Oscillation ◽  
Zonal Wavenumber ◽  
In Situ Observations

Abstract. This paper reports on a project to compare the representation of the monthly-mean zonal wind in the equatorial stratosphere among major global atmospheric reanalysis data sets. The degree of disagreement among the reanalyses is characterized by the standard deviation (SD) of the monthly-mean zonal wind and this depends on latitude, longitude, height, and the phase of the quasi-biennial oscillation (QBO). At each height the SD displays a prominent equatorial maximum, indicating the particularly challenging nature of the reanalysis problem in the low-latitude stratosphere. At 50–70 hPa the geographical distributions of SD are closely related to the density of radiosonde observations. The largest SD values are over the central Pacific, where few in situ observations are available. At 10–20 hPa the spread among the reanalyses and differences with in situ observations both depend significantly on the QBO phase. Notably the easterly-to-westerly phase transitions in all the reanalyses except MERRA are delayed relative to those directly observed in Singapore. In addition, the timing of the easterly-to-westerly phase transitions displays considerable variability among the different reanalyses and this spread is much larger than for the timing of the westerly-to-easterly phase changes. The eddy component in the monthly-mean zonal wind near the Equator is dominated by zonal wavenumber 1 and 2 quasi-stationary planetary waves propagating from midlatitudes in the westerly phase of the QBO. There generally is considerable disagreement among the reanalyses in the details of the quasi-stationary waves near the Equator. At each level, there is a tendency for the agreement to be best near the longitude of Singapore, suggesting that the Singapore observations act as a strong constraint on all the reanalyses. Our measures of the quality of the reanalysis clearly show systematic improvement over the period considered (1979–2012). The SD among the reanalysis declines significantly over the record, although the geographical pattern of SD remains nearly constant.

In situ observations of temperature- and pressure-induced phase transitions in TiH2: Angle-dispersive and synchrotron energy-dispersive X-ray diffraction studies

2008 ◽  
Vol 69 (9) ◽  
pp. 2240-2244 ◽  
Author(s):  
Patricia E. Kalita ◽  
Andrew L. Cornelius ◽  
Kristina E. Lipinska-Kalita ◽  
Cédric L. Gobin ◽  
H. Peter Liermann
Keyword(s):  
Phase Transitions ◽  
Energy Dispersive ◽  
X Ray Diffraction ◽  
X Ray ◽  
In Situ Observations ◽  
Temperature And Pressure

In situ observations of electromigration induced void behavior

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].

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

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

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

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.

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