In Situ Observations of Interplanetary Dust Variability in the Inner Heliosphere

Context. Jovian electrons serve an important role in test-particle distribution in the inner heliosphere. They have been used extensively in the past to study the (diffusive) transport of cosmic rays in the inner heliosphere. With new limits on the Jovian source function, that is, the particle intensity just outside the Jovian magnetosphere, and a new set of in-situ observations at 1 AU for cases of both good and poor magnetic connection between the source and observer, we revisit some of these earlier simulations. Aims. We aim to find the optimal numerical set-up that can be used to simulate the propagation of 6 MeV Jovian electrons in the inner heliosphere. Using such a setup, we further aim to study the residence (propagation) times of these particles for different levels of magnetic connection between Jupiter and an observer at Earth (1 AU). Methods. Using an advanced Jovian electron propagation model based on the stochastic differential equation approach, we calculated the Jovian electron intensity for different model parameters. A comparison with observations leads to an optimal numerical setup, which was then used to calculate the so-called residence (propagation) times of these particles. Results. Through a comparison with in-situ observations, we were able to derive transport parameters that are appropriate for the study of the propagation of 6 MeV Jovian electrons in the inner heliosphere. Moreover, using these values, we show that the method of calculating the residence time applied in the existing literature is not suited to being interpreted as the propagation time of physical particles. This is due to an incorrect weighting of the probability distribution. We applied a new method, where the results from each pseudo-particle are weighted by its resulting phase-space density (i.e. the number of physical particles that it represents). We thereby obtained more reliable estimates for the propagation time.

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Using In-Situ Juno Observations to Understand the Evolution of Interplanetary Coronal Mass Ejections Within 1 AU and Beyond

10.5194/egusphere-egu2020-13767 ◽

2020 ◽

Author(s):

Emma Davies ◽

Robert Forsyth ◽

Simon Good

Keyword(s):

Coronal Mass Ejections ◽

Flux Rope ◽

General Shape ◽

Interplanetary Coronal Mass Ejections ◽

Sheath Region ◽

In Situ Observations ◽

Phase Data ◽

Radial Evolution ◽

Inner Heliosphere

<p>Understanding the evolution of interplanetary coronal mass ejections (ICMEs) as they propagate through the heliosphere is essential in forecasting space weather severity. Much of our knowledge of ICMEs has been gained using in-situ measurements from single spacecraft, although the increasing number of missions in the inner heliosphere has led to an increase in multi-spacecraft studies improving our understanding of the global structure of ICMEs. Whilst most such recent studies have focused on the inner heliosphere within 1 AU, Juno cruise phase data provides a new opportunity to study ICME evolution over greater distances. We present analysis of ICMEs observed in-situ both by Juno and at least one other spacecraft within 1 AU to investigate their evolution as they propagate through the heliosphere. Investigation of the sheath region and timing considerations between spacecraft allows for the general shape of the shock front to be reconstructed. Combining in-situ observations and results of flux rope fitting techniques determines the global picture of the ICME as it propagates. However, effects on in-situ observations due to radial evolution and due to the longitudinal separation between multi-spacecraft remain hard to separate. We note the importance of the interplanetary environment in which the ICME propagates and the need for caution in radial alignment studies. &#160;</p>

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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].

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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

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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.

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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

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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.

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