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A Comprehensive Meteorological Modeling System - RAMS
... O uso de nuvens para computação de alto desempenho vem sendo explorada nessaárea, com estudos que analisam a viabilidade de utilização com vários modelos [Montes et al. 2020] [Powers et al. 2021] [Koop and Raman 2021]. Este trabalho será focado no BRAMS (Brazilian developments on the Regional Atmospheric Modeling System), um modelo numérico de previsão do tempo e clima de escala regional mantido pelo INPE/CPTEC (Instituto Nacional de Pesquisas Espaciais/Centro de Previsão de Tempo e Estudos Climáticos), baseado no modelo RAMS (Regional Atmospheric Modeling System) [Pielke et al. 1992]. O objetivo principal desta pesquisaé analisar os tempos e custos financeiros de execução do BRAMS no ParallelCluster, usando instâncias On-demand e Spot. ...
... 2. Modelo regional BRAMS O modelo regional Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) [Freitas et al. 2017] foi desenvolvido principalmente pelo INPE/CPTEC, baseando-se no modelo Regional Atmospheric Modeling System (RAMS) [Pielke et al. 1992], lançado na década de 1980 pela Colorado State University nos Estados Unidos. O BRAMS oferece um modelo especializado na previsão regional de tempo e de clima no Brasil [Freitas et al. 2016], diferenciando-se do RAMS sobretudo ao introduzir novas funcionalidades que melhor representam os fenômenos meteorológicos tropicais [Freitas et al. 2009]. ...
Este trabalho teve como objetivo analisar o desempenho do modelo de previsão numérica do tempo BRAMS em execução em um cluster AWS criado com o AWS ParallelCluster em diferentes mercados de instâncias, comparandoo com a execução no supercomputador Santos Dumont. Foi proposta uma metodologia para executar uma versão tolerante a falhas do BRAMS no mercado de Spot, onde as instâncias podem ser revogadas, embora ofereçam custos mais baixos. Os tempos de execução na nuvem foram satisfatórios quando comparados ao Santos Dumont. Em geral, a solução Spot reduziu o custo financeiro quando comparado ao uso de instâncias regulares On-Demand. Apenas em um cenário com muitas revogações, o que consequentemente aumenta o tempo de execução e o custo, a opção de usar o mercado On-Demand foi mais adequada.
... O objetivo principal desta pesquisaé analisar e comparar diversos ambientes oferecidos pela nuvem computacional Amazon Web Services (AWS) e comparar com supercomputadores tradicionais (on-premise) no que diz respeito ao custo e desempenho. A aplicação que será utilizada será o BRAMS (Brazilian developments on the Regional Atmospheric Modeling System) 1 , um modelo numérico de previsão do tempo e clima de escala regional mantido pelo INPE/CPTEC (Instituto Nacional de Pesquisas Espaciais/Centro de Previsão de Tempo e Estudos Climáticos), baseado no modelo RAMS (Regional Atmospheric Modeling System) [Pielke et al. 1992]. ...
... 2. Modelo regional BRAMS O modelo regional Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) [Freitas et al. 2017] foi desenvolvido principalmente pelo INPE/CPTEC, baseando-se no modelo Regional Atmospheric Modeling System (RAMS) [Pielke et al. 1992], lançado na década de 1980 pela Colorado State University nos Estados Unidos. ...
A utilização de nuvens computacionais em computação de alto desempenho tem levantado questões sobre a vantagem desse tipo de sistema em relação a sistemas tradicionais (on premise). Porém, comparar esses dois sistemas não é trivial, devido a diversos fatores como a quantidade de soluções que os provedores de nuvens oferecem e também do comportamento da aplicação. Este estudo visou analisar o modelo numérico de previsão de tempo e clima BRAMS nos dois sistemas. Observou-se que para um estudo de caso pequeno, a aplicação possui desempenho semelhantemente nos sistemas. Também foi analisado o custo da execução da aplicação em diferentes mercados ofertados pela AWS, que para o problema utilizado, é aconselhado o uso do mercado spot.
... Data-assimilated wind analysis products such as the real-time hurricane wind analysis system (HWind) [4] combine various observations of wind velocity from land-, air-, and space-based platforms [5]. Alternatively, atmospheric models such as the Weather Research and Forecasting (WRF) Model [7] and the Regional Atmospheric Modeling System (RAMS) [6] are based on the solution of physicsbased governing equations. Such models are often further coupled with ocean circulation/wave models to provide a better representation of wind fields as in the Unified Wave INterface-Coupled Model (UWIN-CM) [8] and the Northeast Coastal Ocean Forecasting System (NECOFS). ...
This work assesses the impact of the CYGNSS (full DDM) informed wind fields on storm surge simulations using the constellation's storm observations. In order to assess the impact of the CYGNSS-enhanced wind fields, storm surge simulations are performed with the ADCIRC (ADvanced CIRCulation) model and validation studies are performed with high water mark (HWM) data provided by the US Geological Survey (USGS). To provide context for the CYGNSS-based results, comparisons to storm surge predictions using Modern-Era Retrospective analysis for Research and Applications, Version 2, (MERRA-2) wind fields are also performed using the example of Hurricane Harvey. In this initial assessment, it is observed that augmenting existing wind estimates with information provided by GNSS-R systems like CYGNSS has the potential of improving surge predictions relative to existing sources of wind information.
... Neste trabalho o modelo numérico utilizado é a versão brasileira do RAMS, o Brazilian developments on the Regional Atmospheric Modeling System -BRAMS (PIELKE et al., 1992;COTTON et al., 2003). A versão do modelo de interação Solo-Vegetação-Atmosfera atualmente implementado no BRAMS é o LEAF-3 (WALKO e TREMBACK, 2005). ...
A distribuição espacial da precipitação para todo o entorno da Região Metropolitana do recife - RMR é estudada neste trabalho, através da modelagem numérica da atmosfera, utilizando o modelo Brazilian Developments on the Regional Atmospheric Modeling System – BRAMS acoplado ao Town Energy Budget –TEB. Foram estudados dois casos distintos, um para o período de tempo seco e outro para um episódio chuvoso. Para entender o efeito individual da dinâmica da cidade e o da convecção rasa, assim como a sua interação não linear, foi utilizado o método de separação de fatores proposto por Stein e Alpert (1993). A consideração do efeito conjunto da dinâmica urbana e dos cúmulos rasos produzem resultados mais realistas, mostrando que a cidade pode contribuir para aumentar ou diminuir a precipitação, dependendo do momento em que os eventos chuvosos ocorrem.
... The model used in this study has two major components (Zhang et al., 2002): RAMS (Pielke et al., 1992) and Models-3 CMAQ (Byun and Ching, 1999). CMAQ is an Eulerian-type model for concurrently simulating all atmospheric and land processes that affect the transport, transformation, and deposition of air pollutants and their precursors on both regional aod urban scales. ...
The regional air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System and Models-3 Community Multi-scale Air Quality) was developed by incorporating a vegetation photosynthesis and respiration module (VPRM) and used to simulate temporal-spatial variations in atmospheric CO2 concentrations in East Asia, with prescribed surface CO2 fluxes (i.e., fossil fuel emission, biomass burning, sea-air CO2 exchange, and terrestrial biosphere CO2 flux). Comparison of modeled CO2 mixing ratios with eight ground-based in-situ measurements demonstrated that the model was able to capture most observed CO2 temporal-spatial features. Simulated CO2 concentrations were generally in good agreement with observed concentrations. Results indicated that the accumulated impacts of anthropogenic emissions contributed more to increased CO2 concentrations in urban regions relative to remote locations. Moreover, RAMS-CMAQ analysis demonstrates that surface CO2 concentrations in East Asia are strongly influenced by terrestrial ecosystems.
... It is based on NWP, which uses a complex calculation of corner description [12] . This system fails to give precise accuracy due to the less number of vectors [13] . Conditional Nonlinear Optimal Perturbation is a robust forecasting process that provides a higher degree of accuracy, but fails to classify more features in images [14] . ...
As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.
The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem. The classification model performance for CH4 detection was evaluated using accuracy, F1 score, Matthew’s Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R 2 score was used to evaluate the regression model performance for CH4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH4 detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.
The electrification of end-uses is causing a
substantial increase in electrical demand in urban distribution
systems. In this framework, an accurate forecast of load
consumption patterns plays a key role in ensuring an efficient
and reliable system operation, proper planning of grid
infrastructures, and reduction of operational costs. To this
purpose, this work introduces different methods to predict the
electric consumption of the medium voltage feeders of a primary
substation: two approaches based on machine-learning theory
(Random Forest and Generalized Boosting Regressor) and one
statistical forecasting model (functional Principal Component
Analysis). An extensive analysis of their performance has been
conducted considering the urban scenario of Milan as a
reference. The methods are trained on a specific database that
includes the electric demand of the last three years of a portion
of the Milan metropolitan area, as well as exogenous variables
such as auto-regressors, meteorological and calendar variables.
Among the selected models, the functional Principal Component
Analysis provided the most accurate prediction, reducing the
index of error compared to the machine-learning models.
Anthropogenic modification of natural landscapes to urban environments impacts land-atmosphere interactions in the boundary layer. Ample research has demonstrated the effect of such landscape transitions on development of the near-surface urban heat island (UHI), while considerably less attention has been given to impacts on regional wind flow. Here we use a set of high-resolution (1 km grid-spacing) regional climate modeling simulations with the Weather Research and Forecasting (WRF) model coupled to a multi-layer urban canopy scheme to investigate the dynamical interaction between the urban boundary layer (UBL) of the Phoenix Metro (U.S.) area and the thermal circulation of the complex terrain it resides within. We conduct paired simulations for the extremely hot and dry summer of 2020, using a contemporary urban representation and a pre-settlement landscape representation to examine the effect of the built environment on local to regional scale wind flow. Analysis of our simulation results shows that, for a majority of the diurnal cycle, 1) the thermo-topographical circulation dominates, 2) the built environment obstructs wind flow in the inertial sublayer during the nighttime, and (3) the built environment of Phoenix Metro produces an UHI circulation of limited vertical extent that interacts with the background flow to modulate its intensity. Such interaction is modulated by greater daytime urban sensible heat flux and dampens the urban roughness induced drag effect by promoting a deeper UBL through vigorous mixing. Our results highlight the need for future research – both observational and simulation based - into urbanizing regions where multi-scale flows are dominant.
In this study we analyse the climatic impact over South America due to Amazon deforestation for positive (PDO+) and negative (PDO−) PDO phases. The regional climatic model RegCM4 was run from 1968 to 2003 for the domain including a great area of the Pacific Ocean and South America, using 50 km as spatial resolution. The Amazon deforestation scenario is that one extrapolated to the 2050 year. Under deforestation, the results show precipitation depletion over the extreme northern South America and precipitation increase over areas to the south, including deforested pixels and east‐southern sectors. These changes are more intense and occupy larger areas during the rainy season. Simulations also indicate that deforestation may impose a drier corridor following the Andes Mountains oriental slopes, from northwestern to south‐southeastern South America, in the rainy season. The impact from deforestation in distinct PDO phases presents the greatest signals over central‐southern South America, in a northwest–southeast direction. Over southeast Brazil, deforestation presents a slightly northeastward displacement of the PDO+ positive precipitation anomalies, intensifying the precipitation anomalies in the north sector and weakening over the south sector. The deforestation effects are more intense in the rainy season and have opposite signals between PDO+ and PDO−. Over the southeast South America, deforestation provides a weaker signal on PDO+ negative precipitation than in PDO−. The increased air temperature over the northern South America, near the northern coast, due to the joint effect of deforestation and PDO variability is more evident during PDO+ dry season than PDO− rainy season. This study illustrates the importance of considering long‐term climate simulations to better understand the role of deforestation in the lower frequency variability over South America.
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