scholarly journals Study of the snow melt–freeze cycle using multi-sensor data and snow modeling

2004 ◽  
Vol 50 (170) ◽  
pp. 419-426 ◽  
Author(s):  
Anselmo Cagnati ◽  
Andrea Crepaz ◽  
Giovanni Macelloni ◽  
Paolo Pampaloni ◽  
Roberto Ranzi ◽  
...  
Keyword(s):  
Meteorological Data ◽  
High Sensitivity ◽  
Microwave Emission ◽  
Sensor Data ◽  
Snow Melt ◽  
Snow Modeling ◽  
Snow Model ◽  
Electromagnetic Models ◽  
Underlying Soil ◽  
Insight Into

AbstractThe melt cycle of snow is investigated by combining ground-based microwave radiometric measurements with conventional and meteorological data and by using a hydrological snow model. Measurements at 2000 m a.s.l in the basin of the Cor-devole river in the eastern Italian Alps confirm the high sensitivity of microwave emission at 19 and 37 GHz to the snow melt−freeze cycle, while the brightness at 6.8 GHz is mostly related to underlying soil. Simulations of snowpack changes performed by means of hydrological and electromagnetic models, driven with meteorological and snow data, provide additional insight into these processes and contribute to the interpretation of the experimental data.

scholarly journals Simulation of microwave emission from physically modeled snowpacks

2000 ◽  
Vol 31 ◽  
pp. 397-405 ◽  
Author(s):  
Andreas Wiesmann ◽  
Charles Fierz ◽  
Christian Mätzler
Keyword(s):  
Microwave Radiation ◽  
Meteorological Data ◽  
High Sensitivity ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Detailed Knowledge ◽  
Emission Model ◽  
Snowpack Properties ◽  
Good Agreement

AbstractDetailed knowledge of snowpack properties is crucial for the interpretation and modeling of thermal microwave radiation. Here we use two well-known snow models, Crocus and SNTHERM, to obtain snow profiles from meteorological data. These profiles are compared with pit profiles and used as input to the Microwave Emission Model of Layered Snowpacks (MEMLS) for the simulation of microwave radiation. The snow-profile data can be applied almost directly. Adaptation is needed only in the conversion of the grain-size used in the snow models to the correlation length used in the emission model; it is based on empirical fits. The resulting emissivities are compared with in situ microwave measurements. The computed snow depths are in good agreement with observations. Comparison of selected profiles shows that Crocus is in good agreement with the pit profile, but the density of simulated melt-freeze crusts is underestimated. The SNTHERM profiles show no such crusts, and the density deviates from the pit profiles. The computed temporal behavior of the snowpack emissivity is reasonable. Comparison of selected situations with in situ measurements indicates good agreement. However, the polarization difference tends to be underestimated because of inaccuracies in the simulation of density profiles. The results show the potential of combined snow-physical and microwave-emission models for understanding snow signatures and for developing snow algorithms for microwave remote sensing. Based on the frequency-selective penetration and on the high sensitivity to snow texture, density and wetness, microwave radiometry can offer a new dimension to snow physics. Potential applications are described.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

scholarly journals Turbulent fluxes above the snow surface

1998 ◽  
Vol 26 ◽  
pp. 179-183 ◽  
Author(s):  
Eric Martin ◽  
Yves Lejeune
Keyword(s):  
Surface Energy Balance ◽  
Meteorological Data ◽  
Energy Balance Equation ◽  
Turbulent Fluxes ◽  
Heat Fluxes ◽  
Indirect Measurements ◽  
Radiative Fluxes ◽  
Stable Conditions ◽  
Sonic Anemometers ◽  
Snow Model

Measurements of sensible- and latent-heat fluxes under stable conditions are rare. In order to obtain indirect measurements of turbulent fluxes, meteorological data measured at the Col de Porte laboratory (1320 m a.s.l, France) under very stable conditions (cold, clear night with low wind) are used. The radiative fluxes are measured, the conduction within the snowpack is calculated using the snow model Crocus and the turbulent fluxes are determined as a residual term of the surface-energy balance equation. These data were used to fit a new parameterization of the turbulent fluxes for the snow model. The turbulent fluxes are increased as compared to the theory. Crocus was also applied to the data from the LEADEX92 experiment and the turbulent fluxes calculated by the model were compared to the fluxes measured using sonic anemometers/thermometers on the site.

Žemės stebėjimas iš kosmoso

2021 ◽  
Author(s):  
Justinas Kilpys ◽  
Laurynas Jukna ◽  
Edvinas Stonevičius ◽  
Rasa Šimanauskienė ◽  
Linas Bevainis
Keyword(s):  
Remote Sensing ◽  
Risk Management ◽  
Environmental Monitoring ◽  
Satellite Remote Sensing ◽  
Disaster Risk ◽  
Satellite Observations ◽  
Sensor Data ◽  
Earth Observations ◽  
The Earth ◽  
Insight Into

Title in English: Earth Observations from Space. There are more than 150 environmental satellites orbiting the Earth, and they are constantly monitoring its surface and the processes happening on it. This textbook offers an introduction to the physical concepts of satellite observations, describes how sensor data is transformed into information about the Earth’s surface and how it can be applied. The scientific background of satellite remote sensing is illustrated using examples from applications in agriculture, forestry, environmental monitoring, disaster risk management, and many other areas. Book provides insight into how satellite remote sensing is used to explore and monitor natural and anthropocentric processes on the Earth and serves as introduction to the practical remote sensing.

scholarly journals Modeling Dynamic Behaviors within Population

2020 ◽  
Vol 34 (10) ◽  
pp. 13736-13737
Author(s):  
Nazgol Tavabi
Keyword(s):  
Social Media ◽  
Activity Patterns ◽  
Sensor Data ◽  
Physiological Data ◽  
Temporal Data ◽  
Group Level ◽  
Human Behaviors ◽  
Doctoral Research ◽  
Similarities And Differences ◽  
Insight Into

The abundance of temporal data generated by mankind in recent years gives us the opportunity to better understand human behaviors along with the similarities and differences in groups of people. Better understanding of human behaviors could be very beneficial in choosing strategies, from group-level to society-level depending on the domain. This type of data could range from physiological data collected from sensors to activity patterns in social media. Identifying frequent behavioral patterns in sensor data could give more insight into the health of a community and provoke strategies towards improving it; By analyzing patterns of behaviors in social media, platform's attributes could be adjusted to the user's needs.This type of modeling introduces numerous challenges that varies depending on the data. The goal of my doctoral research is to introduce ways to better understand and capture human behavior by modeling individual's behaviors as time series and extracting interesting patterns within them.

Does shrubs growth in the high-Arctic lead to permafrost warming?

2020 ◽  
Author(s):  
Florent Domine ◽  
Georg Lackner ◽  
Maria Belke-Brea ◽  
Denis Sarrrazin ◽  
Daniel Nadeau
Keyword(s):  
Thermal Conductivity ◽  
Energy Budget ◽  
Surface Albedo ◽  
High Arctic ◽  
Snow Melt ◽  
Soil Warming ◽  
Yearly Average ◽  
Soil Thermal Properties ◽  
Permafrost Warming ◽  
Snow Model

<p>With climate warming shrubs can grow on high-Arctic tundra. This impacts many terms of the energy budget, resulting in a modification of the permafrost thermal regime. The summer surface albedo is decreased. The winter surface albedo is decreased because shrubs protrude above the snow. Winter conductive fluxes through the snow are reduced because shrubs trap snow, increasing snow depth. Shrubs also favor both snow melt in fall and spring and depth hoar formation in fall and winter, and both these factors affect snow thermal conductivity. Soil thermal properties may also be affected because of increased moisture.  We have measured many terms of the energy budget at Bylot Island, 73°N, Canada, at a herb tundra site and in a nearby large willow shrub patch. Monitored variables include radiation, snow and soil thermal conductivity and standard atmospheric variables. We observe that soil temperature at 15 cm depth is 1.5°C warmer under shrubs on a yearly average. The energetics of both sites are simulated using SurfexV8 including the detailed snow model Crocus. Combining observations and simulations indicates that the increased soil moisture under shrubs, by delaying  freezing by one month in fall, is an important factor in winter soil warming. Summer temperature is also markedly warmer under shrubs because of lower albedo and because the shrub understory is less insulating than on herb, which facilitates warming. These results show that investigating shrub impact using manipulations such as shrub removal is questionable because it does not restore pre-shrub understory and moisture.</p>

scholarly journals Using RESOURCESAT-1 data for determination of snow cover and snowline altitude, Baspa Basin, India

2010 ◽  
Vol 51 (54) ◽  
pp. 9-13 ◽  
Author(s):  
Rakesh Kaur ◽  
Anil V. Kulkarni ◽  
B.S. Chaudhary
Keyword(s):  
Snow Cover ◽  
Meteorological Data ◽  
Sensor Data ◽  
Wide Field ◽  
The World ◽  
Field Sensor ◽  
Terrain Elevation ◽  
Very High ◽  
Wide Swath

AbstractThis paper presents a method for deriving the snowline altitude using a combined analysis of terrain elevation and multispectral Advanced Wide Field Sensor (AWiFS) data from the RESOURCESAT-1 satellite, launched by India on 17 October 2003. AWiFS is a unique instrument capable of acquiring imagery of the world repeatedly every 5 days with very high radiometric resolution. It provides a wide swath width, which is very useful for snow-cover mapping. Snowline altitude is a good indicator of snow coverage. Snowline altitude for the period from October 2004 to June 2005, and from October 2006 to June 2007, is determined for the Baspa Basin, which is located in the Kinnaur district of Himachal Pradesh, India. It is observed that snowline altitude is generally higher during the year 2006/07 than during 2004/05, except in December 2006 and March 2007. The results obtained with the analysis presented here are validated using available meteorological data and are found to be satisfactory. This thus establishes a method that can be applied to other terrains and over longer periods of time within the coverage of the AWiFS sensor data.

scholarly journals A critical insight into the development pipeline of microfluidic immunoassay devices for the sensitive quantitation of protein biomarkers at the point of care

The Analyst ◽  
2017 ◽  
Vol 142 (6) ◽  
pp. 858-882 ◽  
Author(s):  
Ana I. Barbosa ◽  
Nuno M. Reis
Keyword(s):  
Critical Review ◽  
Point Of Care ◽  
High Sensitivity ◽  
Protein Biomarkers ◽  
Sensitivity Measurement ◽  
Microfluidic Immunoassay ◽  
Development Pipeline ◽  
Critical Insight ◽  
Insight Into

A critical review on the development of microfluidic tests capable of the high-sensitivity measurement of protein biomarkers at the point-of-care.

Spectroscopic Determination of C2 Densities in AR/H 2/CH 4 and AR/H2/C60 Microwave Plasmas For Nanocrystalline Diamond Synthesis

1997 ◽  
Vol 502 ◽  
Author(s):  
A. N. Goyette ◽  
J. E. Lawler ◽  
L. W. Anderson ◽  
D. M. Gruen ◽  
T. G. Mccauley ◽  
...  
Keyword(s):  
Gas Phase ◽  
Diamond Films ◽  
High Sensitivity ◽  
Nanocrystalline Diamond ◽  
Chamber Pressure ◽  
Diamond Synthesis ◽  
Microwave Plasmas ◽  
State Concentration ◽  
Insight Into

ABSTRACTWe have measured the steady state concentration of gas phase C2 in Ar/H2/CH4 and Ar/H2/C60 microwave plasmas used for the deposition of nanocrystalline diamond films. High sensitivity white light absorption spectroscopy is used to monitor the C2 density using the d 3 Π ← a3Π (0,0) vibrational band of C2 as chamber pressure, microwave power, substrate temperature and feed gas mixtures are varied in both chemistries. Understanding how these parameters influence the C2 density in the plasma volume provides insight into discharge mechanisms relevant to the deposition of nanocrystalline diamond.

scholarly journals The Bacterial Pneumonia Characteristics based on Climate and Meteorological Parameters in Indonesia, the Tropical Country: A Preliminary Study

2021 ◽  
Vol 4 (1) ◽  
pp. 15
Author(s):  
Bramantono Bramantono ◽  
Brian Eka Rachman ◽  
Erika Marfiani ◽  
Neneng Dewi Kurniati ◽  
Muhammad Vitanata Arifijanto ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Antibiotic Sensitivity ◽  
High Sensitivity ◽  
Surveillance Period ◽  
Acinetobacter Baumanii ◽  
Tropical Country ◽  
Dry Seasons ◽  
Preliminary Study ◽  
Bacterial Patterns ◽  
Insight Into

Introduction: As a tropical country, Indonesia has the potential to influence the characteristics of infectious diseases. The purpose of this study was to see how the pattern of pneumonia on various weather components such as rainfall, humidity, and temperature.Methods: This study was carried by retrospective screening in the infectious ward in one of the tertiary referral hospitals.Results: A total of 876 cases of pneumonia from eight treatment rooms were included in the study. The highest pneumonia cases occurred in February with 239 (27.2%) cases followed in April and May with 169 (19.2%) and 159 (18.2%) cases respectively. After microbiological examination, bacterial growth was found in 191 cases. The highest number of bacteria was found in February with 62 (32%), followed by May with 33 (17.2%) and April with 31 (16.2%) samples. Most of the bacterial grown were gram-negative (94.3%), dominated by Klebsiella pneumoniae (35%), followed by Acinetobacter baumanii 17.2%  and Pseudomonas aeruginosa  12%. On the examination of antibiotic sensitivity, it was found that amikacin has a consistently high sensitivity (90%) for pneumonia caused by most bacterial causes (Acinetobacter baumanii, Klebsiella pneumoniae, and Escherichia coli).Conclusion: The study did not show any remarkable bacterial patterns based on weather components such as rainfall, humidity, and temperature during the rainy season. However, increasing the surveillance period will provide a better insight into the bacterial pattern and can compare it in the rainy and dry seasons.

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