Estimating ice albedo from fine debris cover quantified by a semi-automatic method: the case study of Forni Glacier, Italian Alps

Abstract. In spite of the quite abundant literature focusing on fine debris deposition over glacier accumulation areas, less attention has been paid to the glacier melting surface. Accordingly, we proposed a novel method based on semi-automatic image analysis to estimate ice albedo from fine debris coverage (d). Our procedure was tested on the surface of a wide Alpine valley glacier (the Forni Glacier, Italy), in summer 2011, 2012 and 2013, acquiring parallel data sets of in situ measurements of ice albedo and high-resolution surface images. Analysis of 51 images yielded d values ranging from 0.01 to 0.63 and albedo was found to vary from 0.06 to 0.32. The estimated d values are in a linear relation with the natural logarithm of measured ice albedo (R  =  −0.84). The robustness of our approach in evaluating d was analyzed through five sensitivity tests, and we found that it is largely replicable. On the Forni Glacier, we also quantified a mean debris coverage rate (Cr) equal to 6 g m−2 per day during the ablation season of 2013, thus supporting previous studies that describe ongoing darkening phenomena at Alpine debris-free glaciers surface. In addition to debris coverage, we also considered the impact of water (both from melt and rainfall) as a factor that tunes albedo: meltwater occurs during the central hours of the day, decreasing the albedo due to its lower reflectivity; instead, rainfall causes a subsequent mean daily albedo increase slightly higher than 20 %, although it is short-lasting (from 1 to 4 days).

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A novel integrated method to describe dust and fine supraglacial debris and their effects on ice albedo: the case study of Forni Glacier, Italian Alps

The Cryosphere Discussions ◽

10.5194/tcd-8-3171-2014 ◽

2014 ◽

Vol 8 (3) ◽

pp. 3171-3206 ◽

Cited By ~ 5

Author(s):

R. S. Azzoni ◽

A. Senese ◽

A. Zerboni ◽

M. Maugeri ◽

C. Smiraglia ◽

...

Keyword(s):

Black Carbon ◽

Organic Content ◽

Glacier Surface ◽

Integrated Method ◽

Ice Melt ◽

Natural Logarithm ◽

Marked Variability ◽

Glacier Melting ◽

Melting Surface ◽

Debris Coverage

Abstract. We investigated the characteristics of sparse and fine debris coverage at the glacier melting surface and its relation to ice albedo. In spite of the abundant literature dealing with dust and black carbon deposition on glacier accumulation areas (i.e.: on snow and firn), few studies that describe the distribution and properties of fine and discontinuous debris and black carbon at the melting surface of glaciers are available. Furthermore, guidelines are needed to standardize field samplings and lab analyses thus permitting comparisons among different glaciers. We developed a protocol to (i) sample fine and sparse supraglacial debris and dust, (ii) quantify their surface coverage and the covering rate, (iii) describe composition and sedimentological properties, (iv) measure ice albedo and (v) identify the relationship between ice albedo and fine debris coverage. The procedure was tested on the Forni Glacier surface (northern Italy), in summer 2011, 2012 and 2013, when the fine debris and dust presence had marked variability in space and time (along the glacier tongue and from the beginning to the end of summer) thus influencing ice albedo: in particular the natural logarithm of albedo was found to depend on the percentage of glacier surface covered by debris. Debris and dust analyses indicate generally a local origin (from nesting rockwalls) and the organic content was locally high. Nevertheless the finding of some cenospheres suggests an anthropic contribution to the superficial dust as well. Moreover, the effect of liquid precipitation on ice albedo was not negligible, but short lasting (from 1 to 4 day long), thus indicating that also other processes affect ice albedo and ice melt rates and then some attention has to be spent analysing frequency and duration of summer rainfalls for better describing albedo and melt variability.

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Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

American Behavioral Scientist ◽

10.1177/00027642211021623 ◽

2021 ◽

pp. 000276422110216

Author(s):

Kazimierz M. Slomczynski ◽

Irina Tomescu-Dubrow ◽

Ilona Wysmulek

Keyword(s):

Data Processing ◽

Data Quality ◽

Survey Data ◽

A Priori ◽

Data Sets ◽

New Approach ◽

Survey Quality ◽

Survey Error ◽

Ex Post ◽

The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

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A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

Journal of Probability and Statistics ◽

10.1155/2012/194194 ◽

2012 ◽

Vol 2012 ◽

pp. 1-18 ◽

Cited By ~ 6

Author(s):

Magdalena Murawska ◽

Dimitris Rizopoulos ◽

Emmanuel Lesaffre

Keyword(s):

Empirical Bayes ◽

Cox Model ◽

Data Sets ◽

Two Stage ◽

Nonlinear Mixed Effects Model ◽

Resistance Evolution ◽

Longitudinal Response ◽

Longitudinal Measurements ◽

Subject Specific ◽

The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

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Human Performance Evaluation of a Metaphor Graphic Display for Respiratory Data

Methods of Information in Medicine ◽

10.1055/s-0038-1635042 ◽

1994 ◽

Vol 33 (04) ◽

pp. 390-396 ◽

Cited By ~ 30

Author(s):

J. G. Stewart ◽

W. G. Cole

Keyword(s):

Performance Evaluation ◽

Human Performance ◽

Pattern Detection ◽

Data Sets ◽

Complex Task ◽

Graphic Display ◽

Literal Sense ◽

Single Number ◽

The Impact ◽

Human Problem

Abstract:Metaphor graphics are data displays designed to look like corresponding variables in the real world, but in a non-literal sense of “look like”. Evaluation of the impact of these graphics on human problem solving has twice been carried out, but with conflicting results. The present experiment attempted to clarify the discrepancies between these findings by using a complex task in which expert subjects interpreted respiratory data. The metaphor graphic display led to interpretations twice as fast as a tabular (flowsheet) format, suggesting that conflict between earlier studies is due either to differences in training or to differences in goodness of metaphor, Findings to date indicate that metaphor graphics work with complex as well as simple data sets, pattern detection as well as single number reporting tasks, and with expert as well as novice subjects.

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The Midlatitude Continental Convective Clouds Experiment (MC3E) sounding network: operations, processing and analysis

Atmospheric Measurement Techniques ◽

10.5194/amt-8-421-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 421-434 ◽

Cited By ~ 18

Author(s):

M. P. Jensen ◽

T. Toto ◽

D. Troyan ◽

P. E. Ciesielski ◽

D. Holdridge ◽

...

Keyword(s):

Large Scale ◽

Scale Model ◽

Data Sets ◽

Central Plains ◽

Data Set ◽

Convective Systems ◽

Convective Clouds ◽

Quality Checks ◽

Network Operations ◽

The Impact

Abstract. The Midlatitude Continental Convective Clouds Experiment (MC3E) took place during the spring of 2011 centered in north-central Oklahoma, USA. The main goal of this field campaign was to capture the dynamical and microphysical characteristics of precipitating convective systems in the US Central Plains. A major component of the campaign was a six-site radiosonde array designed to capture the large-scale variability of the atmospheric state with the intent of deriving model forcing data sets. Over the course of the 46-day MC3E campaign, a total of 1362 radiosondes were launched from the enhanced sonde network. This manuscript provides details on the instrumentation used as part of the sounding array, the data processing activities including quality checks and humidity bias corrections and an analysis of the impacts of bias correction and algorithm assumptions on the determination of convective levels and indices. It is found that corrections for known radiosonde humidity biases and assumptions regarding the characteristics of the surface convective parcel result in significant differences in the derived values of convective levels and indices in many soundings. In addition, the impact of including the humidity corrections and quality controls on the thermodynamic profiles that are used in the derivation of a large-scale model forcing data set are investigated. The results show a significant impact on the derived large-scale vertical velocity field illustrating the importance of addressing these humidity biases.

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Considerations of the Scale of Radiocarbon Offsets in the East Mediterranean, and Considering a Case for the Latest (Most Recent) Likely Date for the Santorini Eruption

Radiocarbon ◽

10.1017/s0033822200047202 ◽

2012 ◽

Vol 54 (3-4) ◽

pp. 449-474 ◽

Cited By ~ 13

Author(s):

Sturt W Manning ◽

Bernd Kromer

Keyword(s):

Time Horizon ◽

Weighted Average ◽

17Th Century ◽

Data Sets ◽

East Mediterranean ◽

Average Value ◽

Accuracy And Precision ◽

Wide Range ◽

The Impact ◽

East Mediterranean Region

The debate over the dating of the Santorini (Thera) volcanic eruption has seen sustained efforts to criticize or challenge the radiocarbon dating of this time horizon. We consider some of the relevant areas of possible movement in the14C dating—and, in particular, any plausible mechanisms to support as late (most recent) a date as possible. First, we report and analyze data investigating the scale of apparent possible14C offsets (growing season related) in the Aegean-Anatolia-east Mediterranean region (excluding the southern Levant and especially pre-modern, pre-dam Egypt, which is a distinct case), and find no evidence for more than very small possible offsets from several cases. This topic is thus not an explanation for current differences in dating in the Aegean and at best provides only a few years of latitude. Second, we consider some aspects of the accuracy and precision of14C dating with respect to the Santorini case. While the existing data appear robust, we nonetheless speculate that examination of the frequency distribution of the14C data on short-lived samples from the volcanic destruction level at Akrotiri on Santorini (Thera) may indicate that the average value of the overall data sets is not necessarily the most appropriate14C age to use for dating this time horizon. We note the recent paper of Soter (2011), which suggests that in such a volcanic context some (small) age increment may be possible from diffuse CO2emissions (the effect is hypothetical at this stage and hasnotbeen observed in the field), and that "if short-lived samples from the same stratigraphic horizon yield a wide range of14C ages, the lower values may be the least altered by old CO2." In this context, it might be argued that a substantive “low” grouping of14C ages observable within the overall14C data sets on short-lived samples from the Thera volcanic destruction level centered about 3326–3328 BP is perhaps more representative of the contemporary atmospheric14C age (without any volcanic CO2contamination). This is a subjective argument (since, in statistical terms, the existing studies using the weighted average remain valid) that looks to support as late a date as reasonable from the14C data. The impact of employing this revised14C age is discussed. In general, a late 17th century BC date range is found (to remain) to be most likelyeven ifsuch a late-dating strategy is followed—a late 17th century BC date range is thus a robust finding from the14C evidence even allowing for various possible variation factors. However, the possibility of a mid-16th century BC date (within ∼1593–1530 cal BC) is increased when compared against previous analyses if the Santorini data are considered in isolation.

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Modeling Mine Workforce Fatigue: Finding Leading Indicators of Fatigue in Operational Data Sets

Minerals ◽

10.3390/min11060621 ◽

2021 ◽

Vol 11 (6) ◽

pp. 621

Author(s):

Elaheh Talebi ◽

W. Pratt Rogers ◽

Tyler Morgan ◽

Frank A. Drews

Keyword(s):

Health And Safety ◽

Time Of Day ◽

Weather Data ◽

Data Sets ◽

Leading Indicators ◽

Safety Control ◽

Model Complex ◽

Fatigue Monitoring ◽

The Impact ◽

Operational Data

Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

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The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Data Sets – Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0209.1 ◽

2021 ◽

Author(s):

Therese Rieckh ◽

Jeremiah P. Sjoberg ◽

Richard A. Anthes

Keyword(s):

Radio Occultation ◽

State Of The Art ◽

Specific Humidity ◽

Data Sets ◽

Error Growth ◽

Atmospheric Conditions ◽

Error Statistics ◽

Weather And Climate ◽

Atmospheric Data ◽

The Impact

AbstractWe apply the three-cornered hat (3CH) method to estimate refractivity, bending angle, and specific humidity error variances for a number of data sets widely used in research and/or operations: radiosondes, radio occultation (COSMIC, COSMIC-2), NCEP global forecasts, and nine reanalyses. We use a large number and combinations of data sets to obtain insights into the impact of the error correlations among different data sets that affect 3CH estimates. Error correlations may be caused by actual correlations of errors, representativeness differences, or imperfect co-location of the data sets. We show that the 3CH method discriminates among the data sets and how error statistics of observations compare to state-of-the-art reanalyses and forecasts, as well as reanalyses that do not assimilate satellite data. We explore results for October and November 2006 and 2019 over different latitudinal regions and show error growth of the NCEP forecasts with time. Because of the importance of tropospheric water vapor to weather and climate, we compare error estimates of refractivity for dry and moist atmospheric conditions.

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Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

10.5194/egusphere-egu21-9 ◽

2021 ◽

Author(s):

David Cotton ◽

Keyword(s):

Coastal Zone ◽

Test Data ◽

River Discharge ◽

Altimeter Data ◽

Inland Waters ◽

Data Sets ◽

Data Set ◽

Discharge Data ◽

Processing Algorithms ◽

The Impact

IntroductionHYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.A series of case studies will assess these products in terms of their scientific impacts.All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided&#160;ObjectivesThe scientific objectives of HYDROCOASTAL are to enhance our understanding&#160; of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changesThe technical objectives are to develop and evaluate&#160; new SAR&#160; and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.Project&#160; OutlineThere are four tasks to the project<ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul>&#160;PresentationThe presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.&#160;

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