Sistemas Frontais Sobre a América do Sul parte II: Monitoramento Mensal em Dados da Reanálise i do NCEP/NCAR

A tracking of the frontal systems over South America is performed by the Climate Studies Group at the University of São Paulo  (GrEC/USP) since January 2014. This analysis used an objective methodology for tracking the frontal systems that consists in analyze daily data from I NCEP / NCAR Reanalysis I and verify the occurrence of decrease in temperature at 925hPa, simultaneous to a shift in the meridional wind in 925hPa and sea level pressure increase one day to another. This monitoring is done through maps with the number and anomalies of frontal systems and also analyzing the occurrence of these systems in grid points on the coast, inland and center of the continent.Um monitoramento dos sistemas frontais para a América do Sul é realizada pelo Grupo de Estudos Climáticos da Universidade de São Paulo (GrEC/USP) desde janeiro de 2014. Para esta análise é utilizada uma metodologia objetiva de rastreamento dos sistemas frontais que consiste em analisar dados diários da Reanálise I do NCEP/NCAR e verificar a ocorrência de queda na temperatura em 925hPa, simultânea a um giro no vento meridional em 925hPa e aumento na pressão ao nível médio do mar de um dia para o outro. Este monitoramento é feito através de mapas com o número e a anomalias de sistemas frontais e também para a análise da ocorrência destes sistemas em pontos de grade sobre o litoral, interior e centro do continente.

Investigação do Modo Sul no Clima Presente

O objetivo deste trabalho é relacionar o modo sul de precipitaçãocom eventos extremos de chuva ocorridos no estado do Rio Grandedo Sul no período de 1975 á 2006. Um evento foi selecionado para umaanálise sinótica. Em resultados preliminares, o Modo Sul se mostroucompatível com a ocorrência de eventos extremos de chuva no Rio Grandedo Sul

A novel simulator for probing water infiltration in rain-triggered landslides

This study presents a specially designed dripping rainfall simulator, functional in both laboratory and field settings, developed to research water infiltration processes relevant to landslide studies. The simulator incorporates several advanced features, including adjustable rainfall parameters and precise monitoring and measurement capabilities for a range of experimental setups. The system’s calibration was achieved by measuring the volume of water over a set period, correlating it with the rainfall intensity. Experiments were conducted on a slope surface for up to five hours at a constant rainfall intensity. During this time, 3D electrical resistivity measurements were taken to assess the influence of rainfall on resistivity data, offering insights into the subsurface dynamics of water infiltration. The findings suggest that the combination of dripping rainfall simulation and 3D electrical resistivity analysis holds promise for advancing landslide risk reduction research. This paper provides an in-depth overview of the simulator’s design, functionality, and performance, emphasising its applicability for comprehensive landslide investigations.</p

A Multivariate Assessment of Climate Change Projections over South America Using the Fifth Phase of the Coupled Model Intercomparison Project

This study presents results from an assessment of climate change projections over South America using fifth phase of the Coupled Model Intercomparison Project models. Change in near‐surface temperature, precipitation, evapotranspiration, integrated water vapour transport (IVT), sea level pressure (SLP), and wind at three pressure levels is quantified across the multi‐model suite. Additionally, model agreement for the sign and significance of projected change is assessed within the ensemble. Models are in strong agreement that the highest magnitude of projected warming will be over tropical regions. The CMIP5 models project a decrease in precipitation for all seasons over southern South America, especially along the northern portions of the present‐day mid‐latitude storm track. This is consistent with a robustly projected poleward shift of the Pacific extratropical high‐pressure system and mid‐latitude storm track indicated by a systematic increase in SLP and decrease in westerly wind magnitude over the region. Decreased precipitation for the months of September, October, and November is also projected, with strong model agreement, over portions of northern and northeastern Brazil, coincident with decreases in SLP and increases in evapotranspiration. IVT is broadly projected to decrease over southern South America, coincident with the projected poleward shift of the mid‐latitude storm track, with increases projected in the vicinity of the South Atlantic Convergence Zone in spring and summer. Results provide a comprehensive picture of climate change across South America and highlight where model consensus on change is most robust

A Climatology Of Daily Synoptic Circulation Patterns And Associated Surface Meteorology Over Southern South America

Synoptic circulation patterns, defined as anomalies in sea level pressure (SLP), 500 hPa geopotential height (Z500), and 250 hPa wind speed (V250) and referred to as large-scale meteorological patterns (LSMPs), are characterized using the self-organizing maps approach over southern South America. Results show a wide range of possible LSMP types over a 37-year period of study. LSMP type variability can be summarized as a spectrum from patterns dominated by positive SLP and Z500 anomalies with a poleward displacement of the strongest 250 hPa winds, to patterns dominated by similar structures but with anomalies of opposite sign. The LSMPs found are connected with lower tropospheric temperature and wind, precipitation, and the frequency of atmospheric rivers (ARs). This highlights LSMPs more closely associated with anomalous and potentially impactful surface meteorology. Results show ARs as primary drivers of heavy precipitation over some of the region and connect their occurrence to driving synoptic dynamics. Two important low frequency modes of climate variability, the Southern Annular Mode (SAM) and the El Nino Southern Oscillation (ENSO), show some influence on the frequency of LSMP type, with the SAM more directly related to LSMP type modulation than ENSO. This comprehensive climatology of synoptic variability across southern South America has potential to aid in a mechanistic approach to studying climate change projections of temperature, precipitation, and AR frequency in climate models

Characterizing Monthly Temperature Variability States and Associated Meteorology Across Southern South America

Key spatiotemporal patterns of monthly scale temperature variability are characterized over southern South America using k‐means clustering. The resulting clusters reveal patterns of temperature variability, referred to as temperature variability states. Analysis is performed over summer and winter months separately using data covering the period 1980–2015. Results for both seasons show four primary temperature variability states. In both seasons, one state is primarily characterized by warm temperature anomalies across the domain while another is characterized by cold anomalies. The other two patterns tend to be characterized by a warm north–cold south and cold north–warm south feature. This suggests two primary modes of temperature variability over the region. Composites of synoptic‐scale meteorological patterns (wind, geopotential height, and moisture fields) are computed for months assigned to each cluster to diagnose the driving meteorology associated with these variability states. Results suggest that low‐level temperature advection promoted by anomalies in atmospheric circulation patterns is a key process for driving these variability states. Moisture‐related processes also are shown to play a role, especially in summer. The El Niño–Southern Oscillation and the Southern Annular Mode exhibit some relationship with temperature variability state frequency, with some states more common during amplified phases of these two modes than others. However, the climate modes are not a primary driver of the temperature variability states

Fuzzy Artificial Intelligence&mdash;Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Understanding the key factors that play an important role in students&rsquo; performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students&rsquo; grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the &lsquo;frequency of using learning strategies&rsquo; and &lsquo;occurrence of failing grades&rsquo; was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students&rsquo; performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students&rsquo; planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions

Fuzzy Artificial Intelligence—Based Model Proposal to Forecast Student Performance and Retention Risk in Engineering Education: An Alternative for Handling with Small Data

Understanding the key factors that play an important role in students’ performance can assist improvements in the teaching-learning process. As an alternative, artificial intelligence (AI) methods have enormous potential, facilitating a new trend in education. Despite the advances, there is an open debate on the most suitable model for machine learning applied to forecast student performance patterns. This paper addresses this gap, where a comparative analysis between AI methods was performed. As a research hypothesis, a fuzzy inference system (FIS) should provide the best accuracy in this forecast task, due to its ability to deal with uncertainties. To do so, this paper introduces a model proposal based on AI using a FIS. An online survey was carried to collect data. Filling out a self-report, respondents declare how often they use some learning strategies. In addition, we also used historical records of students’ grades and retention from the last 5 years before the COVID pandemic. Firstly, two experimental groups were composed of students with failing and passing grades, compared by the Mann-Whitney test. Secondly, an association between the ‘frequency of using learning strategies’ and ‘occurrence of failing grades’ was quantified using a logistic regression model. Then, a discriminant analysis was performed to build an Index of Student Performance Expectation (SPE). Considering the learning strategies with greater discriminating power, the fuzzy AI-based model was built using the database of historical records. The learning strategies with the most significant effect on students’ performance were lesson review (34.6%), bibliography reading (25.6%), class attendance (23.5%), and emotion control (16.3%). The fuzzy AI-based model proposal outperformed other AI methods, achieving 94.0% accuracy during training and a generalization capacity of 91.9% over the testing dataset. As a practical implication, the SPE index can be applied as a tool to support students’ planning in relation to the use of learning strategies. In turn, the AI model based on fuzzy can assist professors in identifying students at higher risk of retention, enabling preventive interventions

Enhancing landslide predictability:Validating geophysical surveys for soil moisture detection in 2D and 3D scenarios

Every year, Brazil grapples with the destructive impact of landslides, typically during the summer season. The National Centre for Monitoring and Alerts of Natural Disasters (Cemaden) places significant emphasis on studying these phenomena to understand their processes and causes more deeply. One key challenge faced in this endeavour is the procurement of geotechnical properties of the soil in high-risk areas, with soil moisture being a crucial factor.Collecting point samples for acquiring these geotechnical parameters is not only costly but also limited in providing a comprehensive two-dimensional or three-dimensional coverage. Therefore, the primary aim of the proposed project is to validate the method of acquiring soil moisture data through geophysical surveys in both 2D and 3D scenarios.Data was gathered from soil moisture stations within Cemaden's network and various collected samples to confirm the results. To generate more controlled yet realistic conditions, a sequence of field infiltration experiments was conducted. The findings, related to the ability of the geoelectric method to define soil moisture, derived from this project form an invaluable foundation for future investigations spearheaded by the Geodynamics Group and its collaborating institutions

Severe Weather Events over Southeastern Brazil during the 2016 Dry Season

Southeastern Brazil is the most populated and economically developed region of this country. Its climate consists of two distinct seasons: the dry season, extending from April to September, the precipitation is significantly reduced in comparison to that of the wet season, which extends from October to March. However, during nine days of the 2016 dry season, successive convective systems were associated with atypical precipitation events, tornadoes and at least one microburst over the southern part of this region. These events led to flooding, damages to buildings, shortages of electricity and water in several places, many injuries, and two documented deaths. The present study investigates the synoptic and dynamical features related to these anomalous events. The convective systems were embedded in an unstable environment with intense low-level jet flow and strong wind shear and were supported by a sequence of extratropical cyclones occurring over the Southwest Atlantic Ocean. These features were intensified by the Madden–Julian oscillation (MJO) in its phase 8 and by intense negative values of the Pacific South America (PSA) 2 mode