The search for data on climate change by researchers, government agencies or private companies is a recurrent demand. However, it is hampered by the means of access to this type of information, mainly due to the complexity of extracting, reformatting, and making this data available, which can exceed terabytes in size. The PROJETA platform aims to automate the process of extracting and making available the dataset of global climate change projections downscaling to 20 km over South America generated by the model Eta at CPTEC/INPE. The data request, processing, and conversion process, which used to be done manually and in a oneto-one data delivery basis. The objective of this work is to describe the methodology used to create the platform PROJETA and the information made available. It is a service that allows access to a broad set of different climatic variables. This dataset is available to different users via the Web or API, in a flexible way in terms of data format and data volume. In addition, it integrates technologies that allow the access to the database in an efficient and easy way for use in studies of impact, vulnerability, and adaptation to climate change in various socio-economic sectors.

Search-Based Software Engineering (SBSE) is widely used in different fields of Software Engineering, notoriously, in Enterprise Application Integrations (EAIs). EAI encompasses methodologies, techniques and tools that a software engineer can use to create integration solutions. SBSE is currently an active research topic of increasing interest. The number and diversity of publications produced yearly are large to the extent that it is hard to identify the active research groups, their locations, techniques used and research topics that have not received enough attention. To answer these questions categorically, we have conducted systematic mapping study of the literature. In this paper, we report our methodology and findings. In our study, we used systematic search strategies that resulted in the retrieval of 560 articles, of which we first selected 25. Second, on the basis of the authors’ experience, we included eight additional articles. Finally, we used a snowballing sample technique to include another 12 articles. The results demonstrate that during the last two decades (1999–2020) EAI has benefited from the use of Search-Based Software Engineering techniques.

A previsão do tempo e clima são ferramentas muito importantes para tomada de decisão em diversasáreas como, por exemplo, na agricultura, na hidrologia e na geração de energia. A combinação de tecnologias modernas e a disponibilização de dados meteorológicos fornecem um mecanismo para aumentar a produtividade e minimizar riscos relacionados ao clima. Neste artigo é descrito o desenvolvimento de uma plataforma para automatizar o processo de requisição, de disponibilização e de visualização de dados gerados pelos modelos de previsão de mudanças climáticas do CPTEC/INPE.

Este trabalho apresenta uma abordagem computacional paralela que visa a ampliação da área de cobertura e/ou da série temporal a ser simulada pelo modelo de crescimento da cultura do trigo chamado de CSM-Cropsim: Wheat. A abordagem de paralelização da execução do modelo escolhida foi a mestre-escravo, juntamente com a utilização da biblioteca de comunicação MPI, que mostrou ser adequada, visto que cada uma das execuções (rodadas do modelo) são independentes dos resultados das outras no modelo avaliado. Os resultados obtidos com os testes realizados demonstraram-se satisfatórios, demonstrando a validade da aplicação desta abordagem na execução em larga escala do CSM-Cropsim: Wheat.

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