Multiscale Temporal and Spatial Estimation of the b-Value

2021 ◽  
Author(s):  
Rubén García-Hernández ◽  
Luca D’Auria ◽  
José Barrancos ◽  
Germán D. Padilla ◽  
Nemesio M. Pérez
Keyword(s):  
Spatial Scales ◽  
A Priori ◽  
Central Italy ◽  
B Value ◽  
Novel Approach ◽  
Temporal Estimation ◽  
Temporal And Spatial ◽  
Traditional Approaches ◽  
Tridimensional Mapping ◽  
Marked Drop

Abstract The estimation of the b-value of the Gutenberg–Richter law is of great importance in different seismological applications. However, its estimate is strongly dependent upon selecting a proper temporal and spatial scale, due to the multiscale nature of the seismicity. For this reason, we propose a novel approach (MUltiscale Spatial and Temporal estimation of the B-value [MUST-B]), which allows consistent estimation of the b-value, avoiding subjective “a priori” choices, by considering simultaneously different temporal or spatial scales. A reliable appraisal of the b-value is obtained by applying a robust median over the estimates computed over all the considered scales. We validate the method using a synthetic dataset, showing its superior performances, compared to traditional approaches, in detecting sharp changes in the b-value as well as inconsistently mapping it for highly heterogeneous catalogs. We apply MUST-B to study the temporal and spatial variations of the b-value during the complex 2016–2017 seismic sequence in central Italy, revealing various interesting patterns. In particular, we observe a marked drop of the b-value after the Accumoli (24 August 2016 M 6.0) mainshock. The drop is also observed when realizing a tridimensional mapping of the b-values, showing the drop occurs mainly in the proximity of major earthquake hypocenters. In accordance with previous studies, we interpret these variations as the effect of the release of crustal fluids following the major earthquakes. We maintain that MUST-B can also be applied to other contexts, such as volcanic and induced seismicity, because of its capacity of dealing consistently with highly heterogeneous seismicity patterns.

A Novel Approach for Cognitive Clustering of Parkinsonisms through Affinity Propagation

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 49
Author(s):  
Alessia Sarica ◽  
Maria Grazia Vaccaro ◽  
Andrea Quattrone ◽  
Aldo Quattrone
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Performance ◽  
A Priori ◽  
Affinity Propagation ◽  
Cognitive Profiles ◽  
Regression Residuals ◽  
Novel Approach ◽  
Healthy Control ◽  
Novel Method ◽  
Traditional Approaches

Cluster analysis is widely applied in the neuropsychological field for exploring patterns in cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these traditional approaches need the initial specification of the number of clusters, based on a priori knowledge not always owned. For this reason, we proposed a novel method for cognitive clustering through the affinity propagation (AP) algorithm. In particular, we applied the AP clustering on the regression residuals of the Mini Mental State Examination scores—a commonly used screening tool for cognitive impairment—of a cohort of 49 Parkinson’s disease, 48 Progressive Supranuclear Palsy and 44 healthy control participants. We found four clusters, where two clusters (68 and 30 participants) showed almost intact cognitive performance, one cluster had a moderate cognitive impairment (34 participants), and the last cluster had a more extensive cognitive deficit (8 participants). The findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the cognitive profile of Parkinsonisms patients. Our novel method of unsupervised learning could represent a reliable tool for supporting the neuropsychologists in understanding the natural structure of the cognitive performance in the neurodegenerative diseases.

scholarly journals The Psychrometric Constant Is Not Constant: A Novel Approach to Enhance the Accuracy and Precision of Latent Energy Fluxes through Automated Water Vapor Calibrations

2009 ◽  
Vol 10 (5) ◽  
pp. 1271-1284 ◽  
Author(s):  
H. W. Loescher ◽  
C. V. Hanson ◽  
T. W. Ocheltree
Keyword(s):  
Water Vapor ◽  
Spatial Scales ◽  
Energy Fluxes ◽  
Latent Energy ◽  
Empirical Estimates ◽  
Novel Approach ◽  
Accuracy And Precision ◽  
Temporal And Spatial ◽  
National Networks ◽  
Temperature Depression

Abstract Numerous agencies, programs, and national networks are focused on improving understanding of water and energy fluxes across temporal and spatial scales and on enhancing confidence to synthesize data across multiple sites. Enhancing the accuracy and precision in the surface energy balance and the latent energy (λE) flux lies, in part, with being able to uniformly calibrate water vapor measurements at and among sites to traceable standards. This paper examines (i) the traceable physical controls on field applications of chilled-mirror hygrometers and (ii) an automated means to accurately and precisely calibrate infrared gas analyzers for water vapor concentrations and eddy covariance (λE) data. The environmental physics and gas handling were examined in a theoretical and applied manner that found that chilled-mirror technologies can be a robust measure of dewpoint temperatures and ambient water vapor only if the unit conversions are accounted for between inlet and body temperatures. Psychrometers were also examined and a functional relationship (exponential) was developed for the psychrometric constant against the wet-bulb temperature depression (Tdb − Twb), across a wider range of temperature depressions than previously reported. These empirical estimates of the psychrometer constant for small temperature depressions are much lower than other reported values—that is, ∼0.000 52 K−1 for a wet-bulb temperature depression (Tdb − Twb) of 4.3 K.

scholarly journals A framework for benchmarking land models

2012 ◽  
Vol 9 (10) ◽  
pp. 3857-3874 ◽  
Author(s):  
Y. Q. Luo ◽  
J. T. Randerson ◽  
G. Abramowitz ◽  
C. Bacour ◽  
E. Blyth ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Research Effort ◽  
Spatial Scales ◽  
A Priori ◽  
Model Performance ◽  
Future Research ◽  
Test Model ◽  
Performance Skills ◽  
Temporal And Spatial

Abstract. Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

Larval Feeding: Mechanisms, Rates, and Performance in Nature

2018 ◽  
Keyword(s):  
Marine Invertebrates ◽  
Spatial Scales ◽  
Size Spectrum ◽  
Larval Feeding ◽  
Natural Habitats ◽  
Feeding Mechanism ◽  
Temporal And Spatial ◽  
And Performance ◽  
Bioenergetics Models ◽  
Many Particles

Larvae of many marine invertebrates must capture and ingest particulate food in order to develop to metamorphosis. These larvae use only a few physical processes to capture particles, but implement these processes using diverse morphologies and behaviors. Detailed understanding of larval feeding mechanism permits investigators to make predictions about feeding performance, including the size spectrum of particles larvae can capture and the rates at which they can capture them. In nature, larvae are immersed in complex mixtures of edible particles of varying size, density, flavor, and nutritional quality, as well as many particles that are too large to ingest. Concentrations of all of these components vary on fine temporal and spatial scales. Mechanistic models linking larval feeding mechanism to performance can be combined with data on food availability in nature and integrated into broader bioenergetics models to yield increased understanding of the biology of larvae in complex natural habitats.

Humans and the Environment

2013 ◽  
Keyword(s):  
Spatial Scales ◽  
Empirical Studies ◽  
Human Action ◽  
Cultural Practice ◽  
Environmental Archaeology ◽  
Archaeological Data ◽  
Temporal And Spatial ◽  
Global And Local ◽  
The Relationship ◽  
The Way

The environment has always been a central concept for archaeologists and, although it has been conceived in many ways, its role in archaeological explanation has fluctuated from a mere backdrop to human action, to a primary factor in the understanding of society and social change. Archaeology also has a unique position as its base of interest places it temporally between geological and ethnographic timescales, spatially between global and local dimensions, and epistemologically between empirical studies of environmental change and more heuristic studies of cultural practice. Drawing on data from across the globe at a variety of temporal and spatial scales, this volume resituates the way in which archaeologists use and apply the concept of the environment. Each chapter critically explores the potential for archaeological data and practice to contribute to modern environmental issues, including problems of climate change and environmental degradation. Overall the volume covers four basic themes: archaeological approaches to the way in which both scientists and locals conceive of the relationship between humans and their environment, applied environmental archaeology, the archaeology of disaster, and new interdisciplinary directions.The volume will be of interest to students and established archaeologists, as well as practitioners from a range of applied disciplines.

scholarly journals A novel methodology for epidemic risk assessment of COVID-19 outbreak

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Pluchino ◽  
A. E. Biondo ◽  
N. Giuffrida ◽  
G. Inturri ◽  
V. Latora ◽  
...  
Keyword(s):  
Human Mobility ◽  
Risk Index ◽  
A Priori ◽  
Geographical Area ◽  
Central Italy ◽  
Policy Model ◽  
Italian Regions ◽  
Northern Regions ◽  
Seasonal Flu ◽  
Epidemic Risk

AbstractWe propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

scholarly journals 8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale

2021 ◽  
Vol 13 (12) ◽  
pp. 2355
Author(s):  
Linglin Zeng ◽  
Yuchao Hu ◽  
Rui Wang ◽  
Xiang Zhang ◽  
Guozhang Peng ◽  
...  
Keyword(s):  
Spatial Scale ◽  
Air Temperature ◽  
Spatial Scales ◽  
Model Performance ◽  
Global Scale ◽  
Scale Model ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Zone ◽  
Temporal And Spatial

Air temperature (Ta) is a required input in a wide range of applications, e.g., agriculture. Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) are widely used to estimate Ta. Previous studies of these products in Ta estimation, however, were generally applied in small areas and with a small number of meteorological stations. This study designed both temporal and spatial experiments to estimate 8-day and daily maximum and minimum Ta (Tmax and Tmin) on three spatial scales: climate zone, continental and global scales from 2009 to 2018, using the Random Forest (RF) method based on MODIS LST products and other auxiliary data. Factors contributing to the relation between LST and Ta were determined based on physical models and equations. Temporal and spatial experiments were defined by the rules of dividing the training and validation datasets for the RF method, in which the stations selected in the training dataset were all included or not in the validation dataset. The RF model was first trained and validated on each spatial scale, respectively. On a global scale, model accuracy with a determination coefficient (R2) > 0.96 and root mean square error (RMSE) < 1.96 °C and R2 > 0.95 and RMSE < 2.55 °C was achieved for 8-day and daily Ta estimations, respectively, in both temporal and spatial experiments. Then the model was trained and cross-validated on each spatial scale. The results showed that the data size and station distribution of the study area were the main factors influencing the model performance at different spatial scales. Finally, the spatial patterns of the model performance and variable importance were analyzed. Both daytime and nighttime LST had a significant contribution in the 8-day Tmax estimation on all the three spatial scales; while their contribution in daily Tmax estimation varied over different continents or climate zones. This study was expected to improve our understanding of Ta estimation in terms of accuracy variations and influencing variables on different spatial and temporal scales. The future work mainly includes identifying underlying mechanisms of estimation errors and the uncertainty sources of Ta estimation from a local to a global scale.

scholarly journals Sequential modelling of the Earth’s core magnetic field

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Guillaume Ropp ◽  
Vincent Lesur ◽  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Magnetic Field ◽  
Prior Information ◽  
Spatial Scales ◽  
Model Parameters ◽  
International Geomagnetic Reference Field ◽  
The Earth ◽  
Temporal And Spatial ◽  
Different Sources ◽  
Original Approach ◽  
Modelling Approach

Abstract We describe a new, original approach to the modelling of the Earth’s magnetic field. The overall objective of this study is to reliably render fast variations of the core field and its secular variation. This method combines a sequential modelling approach, a Kalman filter, and a correlation-based modelling step. Sources that most significantly contribute to the field measured at the surface of the Earth are modelled. Their separation is based on strong prior information on their spatial and temporal behaviours. We obtain a time series of model distributions which display behaviours similar to those of recent models based on more classic approaches, particularly at large temporal and spatial scales. Interesting new features and periodicities are visible in our models at smaller time and spatial scales. An important aspect of our method is to yield reliable error bars for all model parameters. These errors, however, are only as reliable as the description of the different sources and the prior information used are realistic. Finally, we used a slightly different version of our method to produce candidate models for the thirteenth edition of the International Geomagnetic Reference Field.

scholarly journals Dissecting the turbulent weather driven by mechanical AGN feedback

2020 ◽  
Vol 498 (4) ◽  
pp. 4983-5002
Author(s):  
D Wittor ◽  
M Gaspari
Keyword(s):  
Feedback Loop ◽  
Spatial Scales ◽  
Self Regulation ◽  
Cosmic Time ◽  
Massive Galaxies ◽  
Temporal And Spatial ◽  
Lagrangian Tracers ◽  
Field Unit ◽  
Integral Field ◽  
Gaseous Halo

ABSTRACT Turbulence in the intracluster, intragroup, and circumgalactic medium plays a crucial role in the self-regulated feeding and feedback loop of central supermassive black holes. We dissect the 3D turbulent ‘weather’ in a high-resolution Eulerian simulation of active galactic nucleus (AGN) feedback, shown to be consistent with multiple multiwavelength observables of massive galaxies. We carry out post-processing simulations of Lagrangian tracers to track the evolution of enstrophy, a proxy of turbulence, and its related sinks and sources. This allows us to isolate in depth the physical processes that determine the evolution of turbulence during the recurring strong and weak AGN feedback events, which repeat self-similarly over the Gyr evolution. We find that the evolution of enstrophy/turbulence in the gaseous halo is highly dynamic and variable over small temporal and spatial scales, similar to the chaotic weather processes on Earth. We observe major correlations between the enstrophy amplification and recurrent AGN activity, especially via its kinetic power. While advective and baroclinc motions are always subdominant, stretching motions are the key sources of the amplification of enstrophy, in particular along the jet/cocoon, while rarefactions decrease it throughout the bulk of the volume. This natural self-regulation is able to preserve, as ensemble, the typically observed subsonic turbulence during cosmic time, superposed by recurrent spikes via impulsive anisotropic AGN features (wide outflows, bubbles, cocoon shocks). This study facilitates the preparation and interpretation of the thermo-kinematical observations enabled by new revolutionary X-ray integral field unit telescopes, such as XRISM and Athena.

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