Snow and ice thicknesses derived from Fast Ice Prediction System Version 2.0 (FIPS V2.0) in Prydz Bay, East Antarctica: comparison with in-situ observations

Abstract A Fast Ice Prediction System (FIPS) was constructed and is the first regional land-fast sea-ice forecasting system for the Antarctic. FIPS had two components: (1) near-real-time information on the ice-covered area from MODIS and SAR imagery that revealed, tidal cracks, ridged and rafted ice regions; (2) a high-resolution 1-D thermodynamic snow and ice model (HIGHTSI) that was extended to perform a 2-D simulation on snow and ice evolution using atmospheric forcing from ECMWF: either using ERA-Interim reanalysis (in hindcast mode) or HERS operational 10-day predictions (in forecast mode). A hindcast experiment for the 2015 season was in good agreement with field observations, with a mean bias of 0.14 ± 0.07 m and a correlation coefficient of 0.98 for modeled ice thickness. The errors are largely caused by a cold bias in the atmospheric forcing. The thick snow cover during the 2015 season led to modeled formation of extensive snow ice and superimposed ice. The first FIPS operational service was performed during the 2017/18 season. The system predicted a realistic ice thickness and onset of snow surface melt as well as the area of internal ice melt. The model results on the snow and ice properties were considered by the captain of R/V Xuelong when optimizing a low-risk route for on-ice transportation through fast ice to the coastal Zhongshan Station.

Download Full-text

On the application of in situ monazite chemical geochronology to constraining P–T–t histories in high-temperature (>850 °C) polymetamorphic granulites from Prydz Bay, East Antarctica

Journal of the Geological Society ◽

10.1144/0016-76492006-013 ◽

2007 ◽

Vol 164 (3) ◽

pp. 667-683 ◽

Cited By ~ 72

Author(s):

D.E. Kelsey ◽

M. Hand ◽

C. Clark ◽

C.J.L. Wilson

Keyword(s):

High Temperature ◽

East Antarctica ◽

Prydz Bay

Download Full-text

Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements

10.5194/tc-2019-126 ◽

2019 ◽

Author(s):

Pia Nielsen-Englyst ◽

Jacob L. Høyer ◽

Kristine S. Madsen ◽

Rasmus T. Tonboe ◽

Gorm Dybkjær

Keyword(s):

Surface Temperature ◽

Arctic Region ◽

Satellite Observations ◽

The Arctic ◽

Ice Surface ◽

Air Temperatures ◽

In Situ Observations ◽

The Arctic Region ◽

Snow And Ice

Abstract. The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic, ice covered areas is heavily under-sampled when it comes to in situ measurements, and large uncertainties exist in weather- and reanalysis products. This paper presents a method for estimating daily mean 2 meter air temperatures (T2m) in the Arctic from satellite observations of skin temperature, using the Arctic and Antarctic ice Surface Temperatures from thermal Infrared (AASTI) satellite dataset, providing spatially detailed observations of the Arctic. The method is based on a linear regression model which has been developed using in situ observations and daily mean satellite ice surface skin temperatures combined with a seasonal variation to estimate daily T2m. The satellite derived T2m product including estimated uncertainties covers clear sky snow and ice surfaces in the Arctic region during the period 2000–2009. Comparison with independent in situ measured T2m gives average correlations of 95.5 % and 96.5 % and average root mean square errors of 3.47 °C and 3.19 °C for land ice and sea ice, respectively. The reconstruction provides a much better spatial coverage than the sparse in situ observations of T2m in the Arctic, is independent of numerical weather prediction model input and it therefore provides an important alternative to simulated air temperatures to be used for assimilation or global surface temperature reconstructions. A comparison between in situ T2m versus T2m from satellite and ERA-Interim shows that the T2m derived from satellite observations validate similar or better than ERA-Interim estimates in the Arctic.

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