scholarly journals Vertical profiles of pure dust and mixed smoke–dust plumes inferred from inversion of multiwavelength Raman/polarization lidar data and comparison to AERONET retrievals and in situ observations

2013 ◽  
Vol 52 (14) ◽  
pp. 3178 ◽  
Author(s):  
Detlef Müller ◽  
Igor Veselovskii ◽  
Alexei Kolgotin ◽  
Matthias Tesche ◽  
Albert Ansmann ◽  
...  
Keyword(s):  
Lidar Data ◽  
Vertical Profiles ◽  
In Situ Observations

Could an ocean climate atlas generated by a model compete with an observational one?

2020 ◽  
Author(s):  
Georgy I. Shapiro ◽  
Jose M. Gonzalez-Ondina ◽  
Xavier Francis ◽  
Hyee S. Lim ◽  
Ali Almehrezi
Keyword(s):  
Standard Error ◽  
Sea Surface ◽  
Vertical Profiles ◽  
Individual Data ◽  
Model Simulations ◽  
Ocean Climate ◽  
Data Points ◽  
In Situ Observations ◽  
The Mean

<p>Modern numerical ocean models have matured over the last decades and are able to provide accurate fore- and hind-cast of the ocean state. The most accurate data could be obtained from the reanalysis where the model run in a hindcast mode with assimilation of available observational data. An obvious benefit of model simulation is that it provides the spatial density and temporal resolution which cannot be achieved by in-situ observations or satellite derived measurements. It is not unusual that even a relatively small area of the ocean model can have in access of 100,000 nodes in the horizontal, each containing vertical profiles of temperature, salinity, velocity and other ocean parameters with a temporal resolution theoretically as high as a few minutes. Remotely sensed (satellite) observations of sea surface temperature can compete with the models in terms of spatial resolution, however they only produce data at the sea surface not the vertical profiles. On the other hand, in-situ observations have a benefit of being much more precise than model simulations. For instance a widely used CTD profiler SBE 911plus has accuracy of about 0.001 °C, which is not achievable by models.</p><p>In the creation of a climatic atlas the higher accuracy of individual profiles provided by in-situ measurements may become less beneficial. Assuming the normal distribution of data at each location, the standard error of the mean (SEM) is calculated as SE=S/SQRT(N), where S is the standard deviation of individual data points around the mean, and N is the number of data points. The climatic data are obtained by averaging a large number of individual data points, and here the benefit of having more data points may become a greater advantage than the accuracy of a single observation.  </p><p>In this study we have created an ocean climate atlas for the northern part of the Indian Ocean including the Red Sea and the Arabian Gulf using model generated data. The data were taken from Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis product GLOBAL_REANALYSIS_PHY_001_030 with 1/12° horizontal resolution and 50 vertical levels for the period 1998 to 2017. The model component is the NEMO platform driven at the surface by ECMWF ERA-Interim reanalysis. The model assimilates along track altimeter data, satellite Sea Surface Temperature, as well as in-situ temperature and salinity vertical profiles where available. The monthly data from CMEMS were then averaged over 20 years to produce an atlas at the surface, 10, 20, 30, 75, 100, 125, 150, 200, 250, 300, 400, and 500 m depths.  The standard error of the mean has been calculated for each point and each depth level on the native grid (1/12 degree).</p><p>The atlas based on model simulations was compared with the latest version of the World Ocean Atlas (WOA)  2018 published by the NCEI.  WOA has objectively analysed climatological mean fields on a ¼  degree grid. The differences between the mean values and SEMs from observational and simulated atlases are analysed, and the potential causes of mismatch are discussed.</p>

scholarly journals Characterizing mega-city pollution with TES O<sub>3</sub> and CO measurements

2007 ◽  
Vol 7 (5) ◽  
pp. 15189-15212 ◽  
Author(s):  
C. Shim ◽  
Q. Li ◽  
M. Luo ◽  
S. Kulawik ◽  
H. Worden ◽  
...  
Keyword(s):  
Mexico City ◽  
Metropolitan Area ◽  
Correlation Coefficients ◽  
In Situ Measurements ◽  
Vertical Profiles ◽  
Aircraft Measurements ◽  
Aircraft Observations ◽  
In Situ Observations ◽  
Pollution Events

Abstract. Concurrent tropospheric O3 and CO vertical profiles from the Tropospheric Emission Spectrometer (TES) during the MILAGRO/INTEX-B aircraft campaigns over the Mexico City Metropolitan Area (MCMA) allow us to characterize mega-city pollution. Outflow from the MCMA occurred predominantly at 600–800 hPa, evident in O3, CO, and NOx enhancements in the in situ observations. We examined O3, CO, and their correlation at 600–800 hPa from TES retrievals, aircraft measurements, and GEOS-Chem model results over the aircraft coverage (within a radius of ~700 km around MCMA). The enhancements in O3 and CO seen in the in situ measurements are not apparent in TES data, due to the lack of TES coverage during several strong pollution events. However, TES O3 and CO data are consistent with the aircraft observations on a daily mean basis (50–60 ppbv and 100–130 ppbv for O3 and CO respectively). The O3-CO correlation coefficients and enhancement ratios (ΔO3/ΔCO) derived from TES data are in good agreements with those derived from the aircraft observations and GEOS-Chem model results (r : 0.5–0.9; ΔO3/ΔCO: 0.3–0.4), reflecting significant springtime photochemical production over MCMA and the surrounding region.

scholarly journals Multi-model evaluation of short-lived pollutant distributions over East Asia during summer 2008

2015 ◽  
Vol 15 (7) ◽  
pp. 11049-11109 ◽  
Author(s):  
B. Quennehen ◽  
J.-C. Raut ◽  
K. S. Law ◽  
G. Ancellet ◽  
C. Clerbaux ◽  
...  
Keyword(s):  
Eastern China ◽  
Regional Model ◽  
Vertical Profiles ◽  
Model Resolution ◽  
Northern India ◽  
Rural Environments ◽  
In Situ Observations ◽  
Aerosol Backscatter ◽  
Pollution Episodes

Abstract. The ability of six global and one regional model to reproduce distributions of tropospheric ozone and its precursors, as well as aerosols over Asia in summer 2008 is evaluated using satellite and in-situ observations. Whilst ozone precursors (NO2 and CO) are generally underestimated by the models in the troposphere, surface NO2 concentrations are overestimated, suggesting that emissions of NOx are too high. Ozone integrated columns and vertical profiles are generally well modeled, but the global models face difficulties simulating the ozone gradient at the surface between urban and rural environments, pointing to the need to increase model resolution. The accuracy of simulated aerosol patterns over eastern China and northern India varies between the models, and although most of the models reproduce the observed pollution features over eastern China, significant biases are noted in the magnitude of optical properties (aerosol optical depth, aerosol backscatter). These results have important implications for accurate prediction of pollution episodes affecting air quality and the radiative effects of these short-lived climate pollutants over Asia.

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.

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

Polar Biology ◽  
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.

In situ observations of isothermal cuspidine crystallization in molten mould fluxes with varying basicity

2020 ◽  
pp. 1-6
Author(s):  
Jiangling Li ◽  
Feifei Lai ◽  
Mei Leng ◽  
Qingcai Liu ◽  
Jian Yang ◽  
...  
Keyword(s):  
In Situ Observations
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