The annual volcanic gas input into the atmosphere, in particular into the stratosphere: a global data set for the past 100 years

2002 ◽  
Vol 115 (3-4) ◽  
pp. 511-528 ◽  
Author(s):  
M.M. Halmer ◽  
H.-U. Schmincke ◽  
H.-F. Graf
Keyword(s):  
Volcanic Gas ◽  
Data Set ◽  
The Past ◽  
Global Data

The NOVAC database of volcanic SO2 emissions

2021 ◽  
Author(s):  
Bo Galle ◽  
Keyword(s):  
Sulphur Dioxide ◽  
Meteorological Conditions ◽  
So2 Emissions ◽  
Volcanic Gas ◽  
Data Set ◽  
Gas Emissions ◽  
First Time ◽  
Global Data ◽  
Web Repository ◽  
The Web

<p>We present a detailed global data-set of volcanic sulphur dioxide (SO2) emissions during the period 2005-2017. Measurements were obtained by scanning-DOAS instruments of the NOVAC network at 32 volcanoes, and processed using a standardized procedure. We reveal the daily statistics of volcanic gas emissions under a variety of volcanological and meteorological conditions. Data from several volcanoes are presented for the first time. Our results  are compared with yearly averages derived from measurements from space by the Aura/OMI instrument and with historical inventories of GEIA. This comparison shows some interesting differences which reasons are briefly discussed. Data is openly available through the web repository at https://novac.chalmers.se/.</p>

scholarly journals Reconstructions of biomass burning from sediment-charcoal records to improve data–model comparisons

2016 ◽  
Vol 13 (11) ◽  
pp. 3225-3244 ◽  
Author(s):  
Jennifer R. Marlon ◽  
Ryan Kelly ◽  
Anne-Laure Daniau ◽  
Boris Vannière ◽  
Mitchell J. Power ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Data Model ◽  
Temporal Trends ◽  
Future Research ◽  
Data Set ◽  
Model Comparisons ◽  
The Past ◽  
The North ◽  
Global Data

Abstract. The location, timing, spatial extent, and frequency of wildfires are changing rapidly in many parts of the world, producing substantial impacts on ecosystems, people, and potentially climate. Paleofire records based on charcoal accumulation in sediments enable modern changes in biomass burning to be considered in their long-term context. Paleofire records also provide insights into the causes and impacts of past wildfires and emissions when analyzed in conjunction with other paleoenvironmental data and with fire models. Here we present new 1000-year and 22 000-year trends and gridded biomass burning reconstructions based on the Global Charcoal Database version 3 (GCDv3), which includes 736 charcoal records (57 more than in version 2). The new gridded reconstructions reveal the spatial patterns underlying the temporal trends in the data, allowing insights into likely controls on biomass burning at regional to global scales. In the most recent few decades, biomass burning has sharply increased in both hemispheres but especially in the north, where charcoal fluxes are now higher than at any other time during the past 22 000 years. We also discuss methodological issues relevant to data–model comparisons and identify areas for future research. Spatially gridded versions of the global data set from GCDv3 are provided to facilitate comparison with and validation of global fire simulations.

scholarly journals Change in mean and extreme temperature at Yingkou station in Northeast China from 1904 to 2017

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Xiaoying Xue ◽  
Guoyu Ren ◽  
Xiubao Sun ◽  
Panfeng Zhang ◽  
Yuyu Ren ◽  
...  
Keyword(s):  
Northeast China ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Data Set ◽  
The Past ◽  
Significant Downward Trend ◽  
Cold Events ◽  
Temperature Indices ◽  
Increasing Trends ◽  
Change In Mean

AbstractThe understanding of centennial trends of extreme temperature has been impeded due to the lack of early-year observations. In this paper, we collect and digitize the daily temperature data set of Northeast China Yingkou meteorological station since 1904. After quality control and homogenization, we analyze the changes of mean and extreme temperature in the past 114 years. The results show that mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) all have increasing trends during 1904–2017. The increase of Tmin is the most obvious with the rate of 0.34 °C/decade. The most significant warming occurs in spring and winter with the rate of Tmean reaching 0.32 °C/decade and 0.31 °C/decade, respectively. Most of the extreme temperature indices as defined using absolute and relative thresholds of Tmax and Tmin also show significant changes, with cold events witnessing a more significant downward trend. The change is similar to that reported for global land and China for the past six decades. It is also found that the extreme highest temperature (1958) and lowest temperature (1920) records all occurred in the first half of the whole period, and the change of extreme temperature indices before 1950 is different from that of the recent decades, in particular for diurnal temperature range (DTR), which shows an opposite trend in the two time periods.

Für Wetter und Klima – Maritim-Meteorologische Observationen beim DWD

2021 ◽  
Author(s):  
Axel Andersson ◽  
Henry Kleta ◽  
Hildrun Otten-Balaccanu ◽  
Thomas Möller
Keyword(s):  
Wird Eine ◽  
Data Set ◽  
Ocean Atmosphere ◽  
Global Data

<p>Die Erfassung und Überwachung des Wetters und des Klimas auf den Weltmeeren hat eine lange Tradition beim Deutschen Wetterdienst (DWD) und seinen Vorgängerorganisationen in Hamburg. Seit dem 19. Jahrhundert werden auf Schiffen systematisch meteorologische und ozeanographische Informationen gesammelt, die ein detailliertes Verständnis des maritimen Wetters und des Klimas ermöglichen. Bis heute sind die meteorologischen Schiffsbeobachtungen eine wichtige Datenquelle für die Wettervorhersage und die Klimaüberwachung.</p> <p>Der Deutsche Wetterdienst betreibt ein großes meteorologisches maritimes Messnetz, welches mehr als 500 Schiffe umfasst, die regelmäßig Wetterbeobachtungen auf allen Weltmeeren durchführen. Diese Schiffe beteiligen sich am internationalen <em>Voluntary Observing Ship (VOS) Scheme</em> und ihre Beobachtungen werden in Echtzeit über das globale Telekommunikationssystem (GTS) der WMO verbreitet. Dabei wird eine zunehmende Anzahl von Beobachtungen von automatischen Wetterstationen an Bord von Schiffen geliefert.</p> <p>Neben der Nutzung für die operationelle Wettervorhersage sind die maritim-meteorologischen Observationen ein wichtiger Beitrag zu klimatologischen Archiven wie der In-situ Datenbank des maritimen Klimadatenzentrums des DWD. Diese Datenbank besteht aus qualitätskontrollierten Daten aus Echtzeit- und <em>delayed mode</em> Datenströmen, sowie aus einer großen Menge historischer Daten. Der Datenbestand wächst kontinuierlich durch aktuelle operationelle Dateneingänge, aber auch durch die Digitalisierung alter meteorologischer Schiffsjournale und reicht von heute bis weit zurück in das 19 Jahrhundert. Im Rahmen des internationalen Datenaustauschs über die WMO / IOC <em>VOS Global Data Assembly Centres</em> (GDACs) werden die maritimen Klimadaten regelmäßig in den <em>International Comprehensive Ocean-Atmosphere Data Set</em> (ICOADS) integriert. Des Weiteren werden die Daten für eine Vielzahl von Klimaanwendungen verwendet, z.B. als Input für Reanalysen, für die operationelle Klimaüberwachung, klimatologische Analysen und Datenprodukte, sowie für die Kalibrierung von Satellitenbeobachtungen.</p>

scholarly journals Self-reflectivity: a moment of professionalization in psychotherapy training

2018 ◽  
Vol 21 (3) ◽  
Author(s):  
Margarete Finger-Ossinger ◽  
Henriette Löffler-Stastka
Keyword(s):  
Thematic Analysis ◽  
European Association ◽  
Psychotherapy Training ◽  
Self Awareness ◽  
Clear Indication ◽  
Learning Program ◽  
Data Set ◽  
Self Reflection ◽  
The Past ◽  
Essential Prerequisite

The required basic skills of European psychotherapists were published by the European Association of Psychotherapy in 2013. One of these abilities is self-reflection. To mentalize oneself, to reflect on what circumstances and experiences in the past and present have led to the present desires, thoughts and convictions is an essential prerequisite for professional work in the psychosocial field. With the help of the thematic analysis a data set of 41 self-reflection reports of students is analysed at the end of the training. Since the training should be evaluated and if necessary optimized, it should be examined which elements of the online preparation course make the selfreflection ability visible. The analysis of the students’ texts gives a clear indication of existing self-reflection skills. It was surprising that for some students, besides the great importance of self-awareness lessons, affective integration into the blended learning program was an essential impulse for self-reflection.

scholarly journals Augmented Data Selector to Initiate Text-Based CAPTCHA Attack

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Aolin Che ◽  
Yalin Liu ◽  
Hong Xiao ◽  
Hao Wang ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Accuracy Rate ◽  
Data Set ◽  
Research Directions ◽  
Training Models ◽  
The Past ◽  
Future Work ◽  
Design Cost ◽  
Accuracy Rates

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

scholarly journals Conceptual Framework for Assessing Process Variables Salient for Service-Learning Experience

2020 ◽  
Author(s):  
Ka Hing Lau ◽  
Robin Snell
Keyword(s):  
Service Learning ◽  
Community Service ◽  
Course Design ◽  
Learning Experience ◽  
Community Partner ◽  
Positive Role ◽  
Data Set ◽  
The Past ◽  
Multi Stakeholder ◽  
Effective Integration

Service-learning is an established pedagogy which integrates experiential learning with community service. It has been widely adopted in higher education around the world including in Hong Kong, yet the key ingredients that determine its successful impacts for its stakeholders have not been fully assessed. This study reviewed the past literature, which indicates the key ingredients that may be found in successful service-learning programmes. We identify six key ingredients: students provide meaningful service; the community partner representative plays a positive role; effective preparation and support for students; effective reflection by students; effective integration of service-learning within the course design; and stakeholder synergy in terms of collaboration, communication and co-ownership. In order to obtain an inter-subjectively fair and trustworthy data set, reflecting the extent to which those key ingredients are perceived to have been achieved, we propose a multi-stakeholder approach for data collection, involving students, instructors and community partner representatives.

scholarly journals A rapid deployment instrument network for temporarily monitoring volcanic SO<sub>2</sub> emissions – a study case from Telica volcano

2014 ◽  
Vol 4 (1) ◽  
pp. 191-211
Author(s):  
V. Conde ◽  
D. Nilsson ◽  
B. Galle ◽  
R. Cartagena ◽  
A. Muñoz
Keyword(s):  
Measurement Techniques ◽  
Active Volcano ◽  
Optical Absorption Spectroscopy ◽  
So2 Emissions ◽  
Volcanic Gas ◽  
Data Set ◽  
So2 Flux ◽  
Geophysical Processes ◽  
Rapid Deployment ◽  
Short Time

Abstract. Volcanic gas emissions play a crucial role in describing geophysical processes; hence measurements of magmatic gases such as SO2 can be used as tracers prior and during volcanic crises. Different measurement techniques based on optical spectroscopy have provided valuable information when assessing volcanic crises. This paper describes the design and implementation of a network of spectroscopic instruments based on Differential Optical Absorption Spectroscopy (DOAS) for remote sensing of volcanic SO2 emissions, which is robust, portable and can be deployed in relative short time. The setup allows the processing of raw data in situ even in remote areas with limited accessibility, and delivers pre-processed data to end-users in near real time even during periods of volcanic crisis, via a satellite link. In addition, the hardware can be used to conduct short term studies of volcanic plumes in remotes areas. The network was tested at Telica, an active volcano located in western Nicaragua, producing what is so far the largest data set of continuous SO2 flux measurements at this volcano.

scholarly journals Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space

2021 ◽  
Author(s):  
Yunhong Gong ◽  
Yanan Sun ◽  
Dezhong Peng ◽  
Peng Chen ◽  
Zhongtai Yan ◽  
...  
Keyword(s):  
Common Sense ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Data Set ◽  
The Past ◽  
Computed Tomography Images ◽  
Intensive Use ◽  
Hospital Resources ◽  
The Common ◽  
Ct Data

AbstractThe COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

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