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SI HPCCI 2024 : High-Performance Computing for Climate Informatics | |||||||||||
Link: https://www.degruyter.com/journal/key/comp/html | |||||||||||
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Call For Papers | |||||||||||
๐ฆ๐ฃ๐๐๐๐๐ ๐๐ฆ๐ฆ๐จ๐ ๐ผ๐ป ๐๐ถ๐ด๐ต-๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐๐น๐ถ๐บ๐ฎ๐๐ฒ ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ฐ๐
This special issue in ๐ข๐ฝ๐ฒ๐ป ๐๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ (๐๐ ๐ฎ๐ฌ๐ฎ๐ฎ: ๐ญ.๐ฑ) focuses on High-Performance Computing for Climate Informatics Climate informatics includes a wide range of disciplines, including paleoclimatology, hurricane reconstruction utilizing data from climate downscaling employing large-scale models to predict weather conditions on a hyper-local level, ice cores, and the socio-economic ramifications of climate and weather. Mitigating the effects of climate change and successfully adapting to them necessitates efficient climate change strategic planning by countries worldwide, whose decision-making involves complicated models and data sources. Because weather forecasting is notoriously difficult, increasing accuracy requires computing power and large data. Machine Learning (ML) in High-Performance Computing (HPC) helps scientists look at climate data flexibly, using figures from previous events to make accurate predictions. It aids in analyzing climate systems' complexity and allows researchers to grasp better how little interactions might influence weather. Machine learning models also help multiple imputations, resulting in similar or synthetic data that further speed climate science research. Big Data can handle the systematization, processing, and appraisal of heterogeneous data and information sources that traditional discipline analytic methods can't. The value of big data in climate studies is well recognized, and its forms are regularly used to study and monitor worldwide trends. It makes understanding and predicting easier, allowing for more adaptable decision-making and optimizing models and structures. Artificial intelligence (AI) technology can aid in the fight against climate change. Data bias, privacy erosion, and purposeful exploitation have all been raised as issues with machine learning applications, all of which can lead to prejudice and injustice. While the future may be exciting, it's also crucial to realize that HPC is already solving major global concerns, including climate change, disease diagnosis, and sustainable energy usage. These applications represent important and forward-thinking milestones for various industries, and we're already seeing what's possible in the future. Thus, HPC is a crucial tool for monitoring and researching the planet's climate, from weather forecasting to biosphere modeling and tracking the evolution of natural resources. Planet-scale simulations can help demonstrate the dangers of climate change and future implications like no other instrument could. With technology far ahead of where it was even two years ago, the future of systems like HPC is bright, exciting, and long-term. TOPICS: โ Advanced Machine learning in data assimilation for climate informatics โ Enhanced futuristic large climate predictive model for long- and short-term climate forecasts โ Convergence of Paleoclimate reconstruction with novel computing algorithms for climate informatics modeling โ AI-based High-Performance Computing for advanced climate detection and forecast models โ Data fusion of geospatial modeling and Geographic Information Systems (GIS) in big data analytics for climate informatics โ Analysis on opportunities and challenges in climate science information and decision making โ Assessment of multiple model simulations for climate informatics โ Convergence of geographic information science and advanced informatics for climate change predictions โ high-performance computation-based Data-intensive multi-disciplinary model for climate informatics โ Edge computing paradigm for advanced climate informatics Authors are requested to submit their full revised papers complying with the general scope of the journal. The submitted papers will undergo the standard peer-review process before they can be accepted. Notification of acceptance will be communicated as we progress with the review process. === ๐ฎ๐ผ๐ฌ๐บ๐ป ๐ฌ๐ซ๐ฐ๐ป๐ถ๐น๐บ Hammam Alshazly, South Valley University, Egypt Hela Elmannai, Princess Nourah Bint Abdulrahman University, Saudi Arabia Amir Benzaoui, University of Skikda, Algeria === ๐ซ๐ฌ๐จ๐ซ๐ณ๐ฐ๐ต๐ฌ The deadline for submissions is ๐ข๐๐ง๐ข๐๐๐ฅ ๐ญ๐ฌ, ๐ฎ๐ฌ๐ฎ๐ฏ, but individual papers will be reviewed and published online on an ongoing basis. === ๐ฏ๐ถ๐พ ๐ป๐ถ ๐บ๐ผ๐ฉ๐ด๐ฐ๐ป All submissions to the Special Issue must be made electronically via the online submission system Editorial Manager: ๐ต๐๐๐ฝ๐://๐๐๐.๐ฒ๐ฑ๐ถ๐๐ผ๐ฟ๐ถ๐ฎ๐น๐บ๐ฎ๐ป๐ฎ๐ด๐ฒ๐ฟ.๐ฐ๐ผ๐บ/๐ผ๐ฝ๐ฒ๐ป๐ฐ๐/๐ฑ๐ฒ๐ณ๐ฎ๐๐น๐๐ฎ.๐ฎ๐๐ฝ๐ Please choose the article type โ๐ฆ๐: ๐๐ถ๐ด๐ต-๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ ๐๐น๐ถ๐บ๐ฎ๐๐ฒ ๐๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ฐ๐โ. === ๐ช๐ถ๐ต๐ป๐จ๐ช๐ป ๐ผ๐ฝ๐ฒ๐ป๐ฐ๐ผ๐บ๐ฝ๐๐๐ฒ๐ฟ๐๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ@๐ฑ๐ฒ๐ด๐ฟ๐๐๐๐ฒ๐ฟ.๐ฐ๐ผ๐บ === ๐๐ผ๐ฟ ๐บ๐ผ๐ฟ๐ฒ ๐ถ๐ป๐ณ๐ผ๐ฟ๐บ๐ฎ๐๐ถ๐ผ๐ป, ๐ฝ๐น๐ฒ๐ฎ๐๐ฒ ๐๐ถ๐๐ถ๐ ๐ผ๐๐ฟ ๐๐ฒ๐ฏ๐๐ถ๐๐ฒ. https://www.degruyter.com/journal/key/comp/html#overview |
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