Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as...Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.展开更多
In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology consi...In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.展开更多
The present study focuses on the impacts of extreme drought and flooding situations in Amazonia, using level/discharge data from some rivers in the Amazon region as indicators of impacts. The last 10 years have featur...The present study focuses on the impacts of extreme drought and flooding situations in Amazonia, using level/discharge data from some rivers in the Amazon region as indicators of impacts. The last 10 years have featured various “once in a century” droughts and floods in the Amazon basin, which have affected human and natural systems in the region. We assess a history of such hazards based on river data, and discuss some of the observed impacts in terms of vulnerability of human and natural systems, as well as some of adaptation strategies implemented by regional and local governments to cope with them. A critical perspective of mitigation of drought and flood policies in Amazonia suggests that they have been mostly ineffective in reducing vulnerability for the majority of the population, constituting, perhaps, examples of maladaptation via the undermining of resilience.展开更多
Sea ice is an important and complex component of the Earth’s system, acting as both an indicator and an amplifier of climate change. Here, we investigated the ability of the Brazilian Earth System Model (BESM-OA2.5) ...Sea ice is an important and complex component of the Earth’s system, acting as both an indicator and an amplifier of climate change. Here, we investigated the ability of the Brazilian Earth System Model (BESM-OA2.5) and four state-of-the-art climate models participating in the fifth phase of the Coupled Model Intercomparison Project, Version 5 (CMIP5) to represent the Antarctic Sea Ice Concentration (SIC) seasonal cycle. We validated the sea ice model’s performance using satellite data from 1980 to 2005 and calculated the skill and RMSE of each model. BESM-OA2.5 results for melt-freeze transitions in the Southern Ocean are consistent with CMIP5 models and satellite data. In February, when the sea ice reaches its annual minimum, the BESM-OA2.5 has the best fit among the models. However, in September, when the Antarctic sea ice reaches its annual maximum, the SIC simulated by BESM-OA2.5 indicated the largest area covered by ice compared to satellite, particularly on the Polar Front. Similar results were found in the CMIP5 models evaluated here. We suggest that the large bias simulated in the Polar Front is related to the inability of the sea ice model to represent the complex ocean-atmosphere-sea ice interactions. The subject is considered a hot topic in climate change studies and lacks conclusive answers.展开更多
One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, th...One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better under展开更多
Long-term drought has occurred in all regions of Brazil, and its effects have been more intense in recent decades. Poor management of drought can exacerbate significant consequences, severely compromising water, food,...Long-term drought has occurred in all regions of Brazil, and its effects have been more intense in recent decades. Poor management of drought can exacerbate significant consequences, severely compromising water, food, energy, economic security, natural systems, and high fire risk that can affect biomes. It also slowly and indirectly affects the society living on vulnerable geographic space. This article discusses a methodology for assessing the drought risk management capacity at the municipal level in Brazil, and this new approach is statistically based using environmental data provided by the municipalities, from observational networks to data banks and remotely sensed data. It presents a method to indicate the steps of priority actions for the phases of drought management. It also characterized the long-term drought in Brazil (hydrological drought) between 1982 and 2022. The proposed approach provides a better understanding and the use of various drought indices to develop the most appropriate action steps for mitigation and adaptation. The final goal is to increase the resilience for those affected by drought. The work was developed based on the actions defined by the Brazilian Federal Government (Preparation, Prevention, Mitigation, Response, Recovery, and Restoration). This aims to improve the management of risk and disaster typified as drought in Brazil and to contribute with scientific knowledge to legislators regarding adaptation and resilience policies to drought extremes in parts of the country. At the end, we expect to highlight to managers and decision-makers the critical points in the government’s proactive and reactive actions to drought that need to be better managed.展开更多
The electron density and temperature key properties of the neutral-magnetized plasma in the solar corona, which are predicted with a novel model, provide an interesting window along the whole solar cycle. In this work...The electron density and temperature key properties of the neutral-magnetized plasma in the solar corona, which are predicted with a novel model, provide an interesting window along the whole solar cycle. In this work, we test the quantitative validity of the model and prove that the Coronal Density and Temperature (CODET) is reliable. Furthermore, this work contrasts the CODET model results with alternative observational remote and in-situ datasets during the simplest conditions of the quiescent corona near the solar minimum. This successful outcome/validation of the CODET model allowed a good qualitative density and temperature retrieval in the solar corona covering a large portion of time interval from solar cycles 23 and 24.展开更多
MC （Monte Carlo） simulation code, JA-IPU is used to study radiation damage of SiC irradiated to spallation neutron and AmBe neutron spectra. The code is based on the major physical processes of radiation damage on i...MC （Monte Carlo） simulation code, JA-IPU is used to study radiation damage of SiC irradiated to spallation neutron and AmBe neutron spectra. The code is based on the major physical processes of radiation damage on incorporation of atomic collision cascade and limited to 10 MeV neutron energy. A phenomenological relation for radiation swelling is also derived. Based on the calculation of swelling, DPA （displacement per atom）, defect production efficiency and effective threshold energy, Efff from the data of MC simulation, SiC is inferred to be a highly radiation resistant material when compared with Nb and Ni metals which are used in composition of several reactor steels. Experimental results of hill-hock density measured using AFM （atomic force microscopy）, also confirm radiation resistant behavior of SiC.展开更多
<p align="justify"> <span style="font-family:Verdana;">Physical concepts based on the Clausius-Clapeyron relation and on the thermodynamics and aerosol characteristics associated with u...<p align="justify"> <span style="font-family:Verdana;">Physical concepts based on the Clausius-Clapeyron relation and on the thermodynamics and aerosol characteristics associated with updrafts, global climate models assuming different parametrizations and lightning-related output variables, and lightning-related data (thunderstorm days) are being used to infer the lightning incidence in a warmer planet, motivated by the global warming observed. In all cases, there are many gaps to be overcome making the lightning response to the global temperature increase still unpredicted. Values from almost 0% (no increase) to 100% have been estimated, being 10% the most common value. While the physical concepts address only part of the problem and the global climate models need to make many simple assumptions, lightning-relate data have strong time and space limitations. In this context, any new evidence should be considered as an important contribution to better understand how will be the lightning incidence in the future. In this article</span></span></span></a><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span><span><span style="font-family:'Minion Pro Capt','serif';"><span style="font-family:Verdana;"> we described new results about the occurrence of thunderstorms from 1850 to 2010 (a period of 160 years) in the city of Rio de Janeiro, in the Southeast of Brazil. During this period thunderstorm days were recorded in the same location, making this time series one of the longest series of this type available worldwide. The data support an increase of 21% in the mean annual thunderstorm days during the period, while surface temperature i</span><span><span style="font-family:Verdana;">ncreased by 0.6</span><span style="text-align:left;widows:2;text-transform:none;background-color:#ffffff;font-style:normal;text-indent:0px;display:inline !important;font-family:Verdana;white-space:normal;orphans:2;float:none;letter-spacing:normal;color:#4f4f4f;font展开更多
Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain er...Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change （IPCC） AIB greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 2o-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index （MFI） shows a statistically significant downward trend in the Mantaro River Basin, whieh suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index （PCI） shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.展开更多
In simulations of fluidized beds using computational fluid dynamics （CFD）, the description of gas-solid flow hydrodynamics relies on a drag model to account for the momentum transfer between gas and solid phases. Al...In simulations of fluidized beds using computational fluid dynamics （CFD）, the description of gas-solid flow hydrodynamics relies on a drag model to account for the momentum transfer between gas and solid phases. Although several studies of drag models have been published, there have been few investigations of the application of lattice Boltzmann method （LBM）-based drag models to bubbling fluidized bed simu- lations. In the present study, a comprehensive comparison of empirical and LBM-based drag models was carried out to assess the performance of these models during simulations of gas-solid flow hydrodynam- ics in a bubbling fluidized bed. A CFD model using the MFIX code based on the Eulerian-Eulerian approach and the kinetic theory of granular flow was used to simulate a 2D bubbling fluidized bed with Geldart B particles. The simulation results were validated by comparison with experimental data. Statistical anal- ysis of the results shows that LBM-based drag models can reliably model gas-solid flow hydrodynamics in a bubbling fluidized bed.展开更多
It is known that the Amazon region plays an important role in the global energy, hydrological cycle and carbon balance. This region has been suffering from the course of the past 40 years intense land use and land cov...It is known that the Amazon region plays an important role in the global energy, hydrological cycle and carbon balance. This region has been suffering from the course of the past 40 years intense land use and land cover changes. With this in mind, this study has examined possible associations between change in spatial and temporal rainfall variability and land cover change in the Amazon, using the PRECIS regional modelling system. It has been found that the impacts of land cover change by forest removal are more intense in the so-called “Arc of deforestation” over central and southern Amazonia. However, the relative impact of the simulated rainfall changes seems to be more important in the JJA dry season. In addition, the simulations under the deforestation scenarios also show the occurrence of extreme rainfall events as well as more frequent dry periods. Therefore, the results found show to be potentially important in the modulation of regional climate variations which have several environmental and socio-economic impacts.展开更多
Recent studies showed that the Himalayan glaciers are reducing alarmingly. This is attributed to global warming. Since the melt water of Himalayan glaciers and snow is the principal source of water for several rivers,...Recent studies showed that the Himalayan glaciers are reducing alarmingly. This is attributed to global warming. Since the melt water of Himalayan glaciers and snow is the principal source of water for several rivers, a decrease of this source is a calamity for the large fraction of global population living in nearby regions such as India. In Asia for the 60% global population only 36% of global water is available. Any further decrease of this vital necessity makes the very existence of billions of people doubtful. Here we show, using both observations and one IPCC-AR4 model with high horizontal resolution, that the Himalayan region in fact underwent a maximum warming of 2.5°C from 1950 to 1999 and would reach the highest temperature rise of 9°C in 2100. Temperature and rainfall variations determine a simple climate classification proposed by Köppen. We show changes that occur in climate and biosphere using this classification. Also we discussed the impact of warming and resulting changes in Köppen climates on the floods and malaria in India.展开更多
ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the kno...ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.展开更多
The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is n...The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statistical-dynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.展开更多
Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesae...Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesaegypti, such as high-temperature days interlaced with afternoon or nocturnal rainstorms in summer. This work has the objective of investigating the relationships between variabilities of the El Ni?o-South Oscillation (ENSO) and greater epidemics of dengue in Rio de Janeiro city. To accomplish this goal, the analysis and signal decomposition by cross-wavelet transform (WT) was applied to obtain the cross variability associated with variations of power and phase of both signals by characteristic periods and along with the time series. Data considered in the analysis are (the decimal logarithm of normalized value) of the monthly available notifications of dengue worsening, provided by the public health system of Brazil, and the Southern Oscillation Index (SOI) Ni?o 3.4 data, provided by the National Oceanic and Atmospheric Administration (NOAA), in the period 2000-2017. A maximum cross-wavelet power close to 0.45 was obtained for the representative period of 1 year and also to periods between 3 and 4 years, associated with the positive phase of the SOI index (i.e. , La Ni?a) or with a transition to the positive phase. The evolution of the combined variability of SOI and dengue can be expressed by progressive differences in phase along the time, eventually resulting in yielding phases (i.e., La Niña-Dengue epidemic).展开更多
New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, wh...New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.展开更多
Flight simulators can provide a suitable alternative to real flight, mainly to increase safety through the training of crew, and evaluation data from simulator can be used to validation and certification of aircraft s...Flight simulators can provide a suitable alternative to real flight, mainly to increase safety through the training of crew, and evaluation data from simulator can be used to validation and certification of aircraft systems. However, it must convey some degree of realism to the user to be effective. For that reason, it is necessary to calibrate the simulator software. Calibration for flight simulation is parameter identification process. The process is formulated as an optimization problem, and it is solved by using a new approach named Multiple Particle Collision Algorithm （MPCA）. Results show a good performance for the employed approach.展开更多
Given the high and increasing lightning incidence over the Southeast of Brazil and the various impacts that this phenomenon generates to society, there is a growing need in predicting its occurrence, in order to minim...Given the high and increasing lightning incidence over the Southeast of Brazil and the various impacts that this phenomenon generates to society, there is a growing need in predicting its occurrence, in order to minimize its consequences. In this context, this work presents the development of a methodology for the projection of lightning in the State of S?o Paulo (Southeastern Brazil), using the HadGEM2-ES and CSIRO-Mk3.6 models in two IPCC climate change scenarios: RCP4.5 and RCP8.5. Since lightning is not an output variable of climate models, tests were carried out to evaluate the relationship between the observed data of oceanic and atmospheric fields, which are known as outputs of the models, and the lightning from the RINDAT and BrasilDAT detection networks. As result, a correlation of 0.84 was obtained. In the projections, it was verified that, while during a large portion of the current climate we observed events of lightning below the average, the future climate reveals the preponderance of anomalously above average events, both in the scenario of intermediate-low emissions (RCP4.5) and in the scenario of high emissions (RCP8.5), suggesting a change in the pattern of the lightning incidence in the State of S?o Paulo.展开更多
Seasonal changes exhibit climate changes, so models can predict future climate change accurately only if they can reproduce seasonal cycle accu-rately. Further, seasonal changes are much larger than the changes even i...Seasonal changes exhibit climate changes, so models can predict future climate change accurately only if they can reproduce seasonal cycle accu-rately. Further, seasonal changes are much larger than the changes even in long period of centuries. Thus it is unwise to ignore large ones compared to small climate change. In this paper, we determine how accurately a suite of ten coupled general circulation models reproduce the observed seasonal cycle in rainfall of the tropics. The seasonal cycles in rainfall of global tropics are known as monsoons. We found that the models can reasonably reproduce the seasonal cycle in rainfall, thus are useful in climate prediction and simulation of global monsoons.展开更多
文摘Renewable energies are highly dependent on local weather conditions, with photovoltaic energy being particularly affected by intermittent clouds. Anticipating the impact of cloud shadows on power plants is crucial, as clouds can cause partial shading, excessive irradiation, and operational issues. This study focuses on analyzing cloud tracking methods for short-term forecasts, aiming to mitigate such impacts. We conducted a systematic literature review, highlighting the most significant articles on cloud tracking from ground-based observations. We explore both traditional image processing techniques and advances in deep learning models. Additionally, we discuss current challenges and future research directions in this rapidly evolving field, aiming to provide a comprehensive overview of the state of the art and identify opportunities for significant advancements in the next generation of cloud tracking systems based on computer vision and deep learning.
文摘In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.
文摘The present study focuses on the impacts of extreme drought and flooding situations in Amazonia, using level/discharge data from some rivers in the Amazon region as indicators of impacts. The last 10 years have featured various “once in a century” droughts and floods in the Amazon basin, which have affected human and natural systems in the region. We assess a history of such hazards based on river data, and discuss some of the observed impacts in terms of vulnerability of human and natural systems, as well as some of adaptation strategies implemented by regional and local governments to cope with them. A critical perspective of mitigation of drought and flood policies in Amazonia suggests that they have been mostly ineffective in reducing vulnerability for the majority of the population, constituting, perhaps, examples of maladaptation via the undermining of resilience.
文摘Sea ice is an important and complex component of the Earth’s system, acting as both an indicator and an amplifier of climate change. Here, we investigated the ability of the Brazilian Earth System Model (BESM-OA2.5) and four state-of-the-art climate models participating in the fifth phase of the Coupled Model Intercomparison Project, Version 5 (CMIP5) to represent the Antarctic Sea Ice Concentration (SIC) seasonal cycle. We validated the sea ice model’s performance using satellite data from 1980 to 2005 and calculated the skill and RMSE of each model. BESM-OA2.5 results for melt-freeze transitions in the Southern Ocean are consistent with CMIP5 models and satellite data. In February, when the sea ice reaches its annual minimum, the BESM-OA2.5 has the best fit among the models. However, in September, when the Antarctic sea ice reaches its annual maximum, the SIC simulated by BESM-OA2.5 indicated the largest area covered by ice compared to satellite, particularly on the Polar Front. Similar results were found in the CMIP5 models evaluated here. We suggest that the large bias simulated in the Polar Front is related to the inability of the sea ice model to represent the complex ocean-atmosphere-sea ice interactions. The subject is considered a hot topic in climate change studies and lacks conclusive answers.
文摘One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better under
文摘Long-term drought has occurred in all regions of Brazil, and its effects have been more intense in recent decades. Poor management of drought can exacerbate significant consequences, severely compromising water, food, energy, economic security, natural systems, and high fire risk that can affect biomes. It also slowly and indirectly affects the society living on vulnerable geographic space. This article discusses a methodology for assessing the drought risk management capacity at the municipal level in Brazil, and this new approach is statistically based using environmental data provided by the municipalities, from observational networks to data banks and remotely sensed data. It presents a method to indicate the steps of priority actions for the phases of drought management. It also characterized the long-term drought in Brazil (hydrological drought) between 1982 and 2022. The proposed approach provides a better understanding and the use of various drought indices to develop the most appropriate action steps for mitigation and adaptation. The final goal is to increase the resilience for those affected by drought. The work was developed based on the actions defined by the Brazilian Federal Government (Preparation, Prevention, Mitigation, Response, Recovery, and Restoration). This aims to improve the management of risk and disaster typified as drought in Brazil and to contribute with scientific knowledge to legislators regarding adaptation and resilience policies to drought extremes in parts of the country. At the end, we expect to highlight to managers and decision-makers the critical points in the government’s proactive and reactive actions to drought that need to be better managed.
文摘The electron density and temperature key properties of the neutral-magnetized plasma in the solar corona, which are predicted with a novel model, provide an interesting window along the whole solar cycle. In this work, we test the quantitative validity of the model and prove that the Coronal Density and Temperature (CODET) is reliable. Furthermore, this work contrasts the CODET model results with alternative observational remote and in-situ datasets during the simplest conditions of the quiescent corona near the solar minimum. This successful outcome/validation of the CODET model allowed a good qualitative density and temperature retrieval in the solar corona covering a large portion of time interval from solar cycles 23 and 24.
文摘MC （Monte Carlo） simulation code, JA-IPU is used to study radiation damage of SiC irradiated to spallation neutron and AmBe neutron spectra. The code is based on the major physical processes of radiation damage on incorporation of atomic collision cascade and limited to 10 MeV neutron energy. A phenomenological relation for radiation swelling is also derived. Based on the calculation of swelling, DPA （displacement per atom）, defect production efficiency and effective threshold energy, Efff from the data of MC simulation, SiC is inferred to be a highly radiation resistant material when compared with Nb and Ni metals which are used in composition of several reactor steels. Experimental results of hill-hock density measured using AFM （atomic force microscopy）, also confirm radiation resistant behavior of SiC.
文摘<p align="justify"> <span style="font-family:Verdana;">Physical concepts based on the Clausius-Clapeyron relation and on the thermodynamics and aerosol characteristics associated with updrafts, global climate models assuming different parametrizations and lightning-related output variables, and lightning-related data (thunderstorm days) are being used to infer the lightning incidence in a warmer planet, motivated by the global warming observed. In all cases, there are many gaps to be overcome making the lightning response to the global temperature increase still unpredicted. Values from almost 0% (no increase) to 100% have been estimated, being 10% the most common value. While the physical concepts address only part of the problem and the global climate models need to make many simple assumptions, lightning-relate data have strong time and space limitations. In this context, any new evidence should be considered as an important contribution to better understand how will be the lightning incidence in the future. In this article</span></span></span></a><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">,</span></span></span><span><span><span style="font-family:'Minion Pro Capt','serif';"><span style="font-family:Verdana;"> we described new results about the occurrence of thunderstorms from 1850 to 2010 (a period of 160 years) in the city of Rio de Janeiro, in the Southeast of Brazil. During this period thunderstorm days were recorded in the same location, making this time series one of the longest series of this type available worldwide. The data support an increase of 21% in the mean annual thunderstorm days during the period, while surface temperature i</span><span><span style="font-family:Verdana;">ncreased by 0.6</span><span style="text-align:left;widows:2;text-transform:none;background-color:#ffffff;font-style:normal;text-indent:0px;display:inline !important;font-family:Verdana;white-space:normal;orphans:2;float:none;letter-spacing:normal;color:#4f4f4f;font
基金FAPEMIG (PPM X 45-16)CNPqpartially funded by CNPq 308035/2013-5
文摘Projections of climate change are essential to guide sustainable development plans in the tropical Andean countries such as Peru. This study assessed the projections of precipitation and potential evaporation, rain erosive potential, and precipitation concentration in the Mantaro River Basin, in the Peruvian Andes, which is important for agriculture and energy production in Peru. We assumed the Intergovernmental Panel on Climate Change （IPCC） AIB greenhouse gas emission scenario and simulated the global climate change by the HadCM3 global climate model. Due to the steepness of the mountain slopes and the narrowness of the river valley, this study uses the downscaling of the global model simulations by the regional Eta model down to 2o-km resolution. The downscaling projections show decrease in the monthly precipitation with respect to the baseline period, especially during the rainy season, between February and April, until the end of the 21st century. Meanwhile, a progressive increase in the monthly evaporation from the baseline period is projected. The Modified Fournier Index （MFI） shows a statistically significant downward trend in the Mantaro River Basin, whieh suggests a possible reduction in the rain erosive potential. The Precipitation Concentration Index （PCI） shows a statistically significant increasing trend, which indicates increasingly more irregular temporal distribution of precipitation towards the end of the century. The results of this study allow us to conclude that there should be a gradual increase in water deficit and precipitation concentration. Both changes can be negative for agriculture, power generation, and water supply in the Mantaro River Basin in Peru.
文摘In simulations of fluidized beds using computational fluid dynamics （CFD）, the description of gas-solid flow hydrodynamics relies on a drag model to account for the momentum transfer between gas and solid phases. Although several studies of drag models have been published, there have been few investigations of the application of lattice Boltzmann method （LBM）-based drag models to bubbling fluidized bed simu- lations. In the present study, a comprehensive comparison of empirical and LBM-based drag models was carried out to assess the performance of these models during simulations of gas-solid flow hydrodynam- ics in a bubbling fluidized bed. A CFD model using the MFIX code based on the Eulerian-Eulerian approach and the kinetic theory of granular flow was used to simulate a 2D bubbling fluidized bed with Geldart B particles. The simulation results were validated by comparison with experimental data. Statistical anal- ysis of the results shows that LBM-based drag models can reliably model gas-solid flow hydrodynamics in a bubbling fluidized bed.
文摘It is known that the Amazon region plays an important role in the global energy, hydrological cycle and carbon balance. This region has been suffering from the course of the past 40 years intense land use and land cover changes. With this in mind, this study has examined possible associations between change in spatial and temporal rainfall variability and land cover change in the Amazon, using the PRECIS regional modelling system. It has been found that the impacts of land cover change by forest removal are more intense in the so-called “Arc of deforestation” over central and southern Amazonia. However, the relative impact of the simulated rainfall changes seems to be more important in the JJA dry season. In addition, the simulations under the deforestation scenarios also show the occurrence of extreme rainfall events as well as more frequent dry periods. Therefore, the results found show to be potentially important in the modulation of regional climate variations which have several environmental and socio-economic impacts.
文摘Recent studies showed that the Himalayan glaciers are reducing alarmingly. This is attributed to global warming. Since the melt water of Himalayan glaciers and snow is the principal source of water for several rivers, a decrease of this source is a calamity for the large fraction of global population living in nearby regions such as India. In Asia for the 60% global population only 36% of global water is available. Any further decrease of this vital necessity makes the very existence of billions of people doubtful. Here we show, using both observations and one IPCC-AR4 model with high horizontal resolution, that the Himalayan region in fact underwent a maximum warming of 2.5°C from 1950 to 1999 and would reach the highest temperature rise of 9°C in 2100. Temperature and rainfall variations determine a simple climate classification proposed by Köppen. We show changes that occur in climate and biosphere using this classification. Also we discussed the impact of warming and resulting changes in Köppen climates on the floods and malaria in India.
文摘ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.
文摘The cause-effect relationship is not always possible to trace in GCMs because of the simultaneous inclusion of several highly complex physical processes. Furthermore, the inter-GCM differences are large and there is no simple way to reconcile them. So, simple climate models, like statistical-dynamical models (SDMs), appear to be useful in this context. This kind of models is essentially mechanistic, being directed towards understanding the dependence of a particular mechanism on the other parameters of the problem. In this paper, the utility of SDMs for studies of climate change is discussed in some detail. We show that these models are an indispensable part of hierarchy of climate models.
文摘Dengue is one of the most prominent tropical epidemic diseases present in the Rio de Janeiro city and Southeast part of Brazil, due to the widespread conditions of occurrence of the dengue vector, the mosquito Aedesaegypti, such as high-temperature days interlaced with afternoon or nocturnal rainstorms in summer. This work has the objective of investigating the relationships between variabilities of the El Ni?o-South Oscillation (ENSO) and greater epidemics of dengue in Rio de Janeiro city. To accomplish this goal, the analysis and signal decomposition by cross-wavelet transform (WT) was applied to obtain the cross variability associated with variations of power and phase of both signals by characteristic periods and along with the time series. Data considered in the analysis are (the decimal logarithm of normalized value) of the monthly available notifications of dengue worsening, provided by the public health system of Brazil, and the Southern Oscillation Index (SOI) Ni?o 3.4 data, provided by the National Oceanic and Atmospheric Administration (NOAA), in the period 2000-2017. A maximum cross-wavelet power close to 0.45 was obtained for the representative period of 1 year and also to periods between 3 and 4 years, associated with the positive phase of the SOI index (i.e. , La Ni?a) or with a transition to the positive phase. The evolution of the combined variability of SOI and dengue can be expressed by progressive differences in phase along the time, eventually resulting in yielding phases (i.e., La Niña-Dengue epidemic).
文摘New sequencing technologies such as Illumina/Solexa, SOLiD/ABI, and 454/Roche, revolutionized the biological researches. In this context, the SOLiD platform has a particular sequencing type, known as multiplex run, which enables the sequencing of several samples in a single run. It implies in cost reduction and simplifies the analysis of related samples. Meanwhile, this sequencing type requires an additional filtering step to ensure the reliability of the results. Thus, we propose in this paper a probabilistic model which considers the intrinsic characteristics of each sequencing to characterize multiplex runs and filter low-quality data, increasing the data analysis reliability of multiplex sequencing performed on SOLiD. The results show that the proposed model proves to be satisfactory due to: 1) identification of faults in the sequencing process;2) adaptation and development of new protocols for sample preparation;3) the assignment of a degree of confidence to the data generated;and 4) guiding a filtering process, without discarding useful sequences in an arbitrary manner.
文摘Flight simulators can provide a suitable alternative to real flight, mainly to increase safety through the training of crew, and evaluation data from simulator can be used to validation and certification of aircraft systems. However, it must convey some degree of realism to the user to be effective. For that reason, it is necessary to calibrate the simulator software. Calibration for flight simulation is parameter identification process. The process is formulated as an optimization problem, and it is solved by using a new approach named Multiple Particle Collision Algorithm （MPCA）. Results show a good performance for the employed approach.
文摘Given the high and increasing lightning incidence over the Southeast of Brazil and the various impacts that this phenomenon generates to society, there is a growing need in predicting its occurrence, in order to minimize its consequences. In this context, this work presents the development of a methodology for the projection of lightning in the State of S?o Paulo (Southeastern Brazil), using the HadGEM2-ES and CSIRO-Mk3.6 models in two IPCC climate change scenarios: RCP4.5 and RCP8.5. Since lightning is not an output variable of climate models, tests were carried out to evaluate the relationship between the observed data of oceanic and atmospheric fields, which are known as outputs of the models, and the lightning from the RINDAT and BrasilDAT detection networks. As result, a correlation of 0.84 was obtained. In the projections, it was verified that, while during a large portion of the current climate we observed events of lightning below the average, the future climate reveals the preponderance of anomalously above average events, both in the scenario of intermediate-low emissions (RCP4.5) and in the scenario of high emissions (RCP8.5), suggesting a change in the pattern of the lightning incidence in the State of S?o Paulo.
文摘Seasonal changes exhibit climate changes, so models can predict future climate change accurately only if they can reproduce seasonal cycle accu-rately. Further, seasonal changes are much larger than the changes even in long period of centuries. Thus it is unwise to ignore large ones compared to small climate change. In this paper, we determine how accurately a suite of ten coupled general circulation models reproduce the observed seasonal cycle in rainfall of the tropics. The seasonal cycles in rainfall of global tropics are known as monsoons. We found that the models can reasonably reproduce the seasonal cycle in rainfall, thus are useful in climate prediction and simulation of global monsoons.
