Scholarlink

<p>The global climate studies cannot be complete without heterogeneous satellite observations to ensure that the intricate climate processes on the earth are captured. The existing systems of operation are experiencing critical constraints with respect to real-time data fusion, with the traditional statistical procedures unable to manage the amount and type of contemporary multi-satellite measurements. This paper introduces a new framework that incorporates the use of a multi-satellite system with the incorporation of the most advanced deep learning systems and terahertz communication networks. The method uses specialised encoder-decoder networks with cross-attention networks to process optical images, radar images of precipitation, and atmospheric profiles of a variety of satellite missions. The framework includes convolutional neural networks, recurrent neural networks, and generative adversarial networks, which are used to extract spatial features, model temporal dynamics, and enhance spatial resolution, respectively. Terahertz communication connections can be used to make the satellites fast in exchanging information, which minimizes the processing latency and improves coordination among all satellite groups. The solution to the problem of uncertainty quantification is Bayesian deep learning methods, which make probabilistic predictions with confidence intervals that are calibrated. An analysis of five-year multi-satellite data shows that major climate variables have been improved. The accuracy in precipitation nowcasting was increased by 18% and extreme events were also detected. The fusion of sea surface temperature attained a 4 × improvement in the spatial resolution with a 12% better accuracy compared to single satellite products. The interconnected system can process 2.1 terabytes of satellite data in a day and has a 25-min end-to-end latency, which is a 5.8 times increase in speed compared to currently operational schemes. The article illustrates the possibility of using cutting-edge machine learning with the latest satellite communication systems to monitor global climatic conditions more effectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep Learning-Enabled Multi-Satellite Terahertz Networks for Real-Time Global Climate Data Fusion and Analytics

  • 期刊论文

  • 发表日期：

    2026年2月11日

查看全文
  • Ashok K,
  • Abhay Chaturvedi,
  • Sachin Jadhav,
  • J. R. Arunkumar,
  • Peddireddy Veera Venkateswara Rao,
  • Kondru Ayyappa Swamy

摘要

The global climate studies cannot be complete without heterogeneous satellite observations to ensure that the intricate climate processes on the earth are captured. The existing systems of operation are experiencing critical constraints with respect to real-time data fusion, with the traditional statistical procedures unable to manage the amount and type of contemporary multi-satellite measurements. This paper introduces a new framework that incorporates the use of a multi-satellite system with the incorporation of the most advanced deep learning systems and terahertz communication networks. The method uses specialised encoder-decoder networks with cross-attention networks to process optical images, radar images of precipitation, and atmospheric profiles of a variety of satellite missions. The framework includes convolutional neural networks, recurrent neural networks, and generative adversarial networks, which are used to extract spatial features, model temporal dynamics, and enhance spatial resolution, respectively. Terahertz communication connections can be used to make the satellites fast in exchanging information, which minimizes the processing latency and improves coordination among all satellite groups. The solution to the problem of uncertainty quantification is Bayesian deep learning methods, which make probabilistic predictions with confidence intervals that are calibrated. An analysis of five-year multi-satellite data shows that major climate variables have been improved. The accuracy in precipitation nowcasting was increased by 18% and extreme events were also detected. The fusion of sea surface temperature attained a 4 × improvement in the spatial resolution with a 12% better accuracy compared to single satellite products. The interconnected system can process 2.1 terabytes of satellite data in a day and has a 25-min end-to-end latency, which is a 5.8 times increase in speed compared to currently operational schemes. The article illustrates the possibility of using cutting-edge machine learning with the latest satellite communication systems to monitor global climatic conditions more effectively.