Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Formaldehyde (HCHO) is measured from space using backscattered ultraviolet sun-light. Because of HCHO’s short lifetime, space-based observations of HCHO can serve as a proxy for volatile organic compounds, helping to characterize their global emissions and distributions. HCHO satellite observations rely on Air Mass Factor (AMF) calculations to transform fitted slant columns into vertical column densities. Most HCHO satellite products do not explicitly consider the presence of snow on the ground during the calculation of AMFs. In this study, we leverage information from the MODIS bidirectional reflectance distribution function (BRDF), MODIS snow cover information, and the Interactive Multisensor Snow and Ice Mapping System to evaluate the impact of ground snow on Ozone Monitoring Instrument (OMI) HCHO retrievals. We focus our analysis on the year 2005. We compare AMFs computed using daily MODIS BRDF to AMFs computed using OMI’s surface reflectance climatology, the baseline for NASA’s OMHCHO product. Over snow-covered regions, both sets of AMFs show significant differences. We observe two different behaviors. Regions with permanent snow cover (Greenland and Antarctica) show smaller AMFs calculated with MODIS BRDF than with the OMI climatology resulting in a 6% median annual increase of HCHO VCDs. Over regions with seasonal snow cover, the situation is more complex with more variability in the differences during the year. For example, a February 2005 case study over Europe shows that the NASA OMHCHO VCDs (calculated using the OMI Lambertian climatology) are on average 16% larger than HCHO columns retrieved using daily MODIS BRDF information.

Ultraviolet radiation (UVR) plays a key role in the photochemistry of the atmosphere, through absorption or dispersion processes by its constituents (ozone, cloudiness, aerosols, and pollutants in the troposphere). Quantifying UVR in a spatial-temporal way and knowing its relationships with modulating variables is important for Rio Grande do Sul State, a region with one of Brazil’s highest skin neoplasms rates. Ultraviolet radiation data for the region, acquired by the Ozone Monitoring Instrument (OMI) for the period 2006 to 2020, and expressed in terms of erythemal daily dose (EDD), was used in this study, with the objective of quantifying UVR incidence and its stability in time and spatial distribution. Our results show that for this study area the radiation varies from 3300 to 3700 J m–2, with a latitudinal gradient of 66.7 J m–2 per degree, with maxima recorded in December (6028 J m–2, summer) and minima in June (1123 J m–2, winter). A long-term decreasing trend of 29.76% (z value = –2) was observed in the area, while 6.19% of the area had an increasing trend (z value = 5). During the studied period of 15 years, occurrences of high values of EDD were negatively correlated with total O3 as the dominant relationship. Positive or negative correlations with total NO2 were also recorded, depending on the investigated season or region.

Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.

Solar radiation, which is used in hydrological and agricultural modeling, agricultural, solar energy systems, and climatological studies, is the most important element of the energy reaching the earth. The present study compared the performance of two empirical equations -Angstrom and Hargreaves-Samani equations- and three machine learning models -Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM)-. Various learning models were developed for the variables used in each empirical equation. In the present study, monthly data of six stations in Turkey, three stations receiving the most solar radiation and three stations receiving the lowest solar radiation, were used. In terms of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and determination coefficient (R2) values of each model, the LSTM was the most successful model, followed by ANN and SVM. The MAE value was 2.65 with the Hargreaves-Samani equation and decreased to 0.987 with the LSTM model, while MAE was 1.24 in the Angstrom equation and decreased to 0.747 with the LSTM model. The study revealed that the deep learning model is more appropriate to use than the empirical equations, even in cases with limited data.

Walnut trees, as well as their fruits, represent an important sector of the agricultural industry and their cultivation significantly contributes to the global economy. Irrigation is a key factor in walnut cultivation and its most important problem is related to accurately estimating the need for irrigation water. Walnut water use was estimated in this study through artificial intelligence methods, namely artificial neural networks (ANN) and the adaptive neuro-fuzzy inference system (ANFIS) using meteorological data in western Turkey, which has semi-arid climatic conditions. Probabilistic scenarios based on maximum, minimum and average temperature, wind speed and sunshine hours over the period 2016-2019 were developed and tested with ANN and ANFIS to estimate walnut evapotranspiration. Results indicate that the optimum performance in the training and testing for ANN and ANFIS was obtained from the fourth scenario with R = 0.95 and two climate parameters: sunshine duration and mean temperature. Both ANN and ANFIS were able to predict crop water use obtaining a high correlation and the minimum number of climatic parameters. Nevertheless, the ANFIS model had a higher predictive capacity, with smaller MSE (0.36 for training and 0.29 for testing) compared to the ANN model.

Climate regionalization is essential for characterizing spatial and temporal climatic variability, producing meteorological forecasts, analyzing trends at different scales, and determining the climatic impact on human activities. The aim was to propose a climatic regionalization for Santa Cruz province, based on gridded rainfall and temperature data (period 1995 to 2014), and subsequent characterization. We applied the non-hierarchical k-means clustering method to monthly accumulated rainfall and monthly average temperature databases to achieve this goal. The Thornthwaite classification modified by Feddema was used to classify each cluster. Results from this study showed that Santa Cruz province is divided into 11 climatic regions based on rainfall and temperature. The driest and warmest regions are located in the center and northeast of the province and the most humid and coldest ones in the south and southwest. Regionalization is an important component of many applied climate studies and it can be used in other studies related to agriculture, energy production, water resource management, extreme weather events, and climate change, among others. This regionalization in particular can be used to examine the impacts of climate change in regional studies of climatic scale reduction in Santa Cruz province. It can also be essential in the study of drought and its impacts, and contributes to a better understanding of the climatic phenomena that condition drought.

Deciduous fruit trees need to be exposed to low winter temperatures for a certain period of time to produce regular crops. In addition to the effects of global warming in many other areas, its effect on cold accumulation is also a reason of concern. As a result, many studies have been carried out in important horticultural areas around the world on the impact of climate change on cold accumulation. In this study, historical changes of cold accumulation calculated using five models were examined in 12 locations in Turkey for the first time. Results show that there was no significant trend in cold accumulation in the provinces of Ankara, Bingöl, Diyarbakır, Malatya, and Tunceli. In some locations, the significance, magnitude, and direction of the chilling trend differed according to the model used. All five models used in the study indicated significant decreases in winter chill accumulation in Şanlıurfa, a site with relatively mild winters. In Erzincan, which has relatively cold winters, increasing trends were detected in cold accumulation calculated according to Utah, Modified Utah, and Positive Utah models. Results show that serious consequences may arise related to the chilling requirement of deciduous fruit trees, especially in regions with mild winters.

Experimental lock-release gravity currents are investigated as they propagate downslope over varying synthetic topography. We emulate and investigate the dynamics of thermally driven winds that propagate downslope while interacting with the roughness of a complex topographic surface. The mixing processes between the gravity currents and their surroundings are studied with Particle Image Velocimetry (PIV), and entrainment is quantified. The magnitude of the entrainment coefficient is shown to increase as the roughness of the slope increases. Shadowgraph visualizations qualitatively reproduce this behavior. Finally, pressure fields are estimated from velocity fields, and pressure time series are obtained over synthetic stations along the topographic surface. The arrival of gravity currents is shown to be detected in the pressure time series. This last result may help detect atmospheric gravity currents using only surface pressure measurements.

Chronic work stress, is the physical or emotional exhaustion in workers that increases the risk of absenteeism or job abandonment, (Peña et al., 2018) it is typical of certain professions that maintain direct relationship with people, being the case of health professionals who are a group with high prevalence, due to the psychosocial risk factors to which they are exposed (García et al., 2018) so that, if this syndrome is not treated and remains long, it will have harmful consequences that will be reflected in the form of disease or psychological clinical manifestation (Ramirez et al., 2019) where satisfaction for life, could be a modulating variable before this syndrome, since the development of the same will allow mitigating these effects and improving the adaptability of the person in front of the challenges or threats that are presented. (Ardila, 2003). The main objective of this research was to determine the relationship between chronic work stress and life satisfaction as perceived by a sample of 359 health professionals. The instruments used were the Maslach Burnout Inventory (MBI-HSS) and the Life Satisfaction Scale (ESV). Among the most relevant results we have the following: regarding Pearson's correlation, no significant relationship was found between burnout and life satisfaction, but a significant negative relationship was evidenced between burnout and depersonalization in reference to satisfaction; finally, regarding personal fulfillment, a significant relationship was found with satisfaction.