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Updated: Mar 7, 2026

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WxC-Bench:一种新的天气和气候下游任务数据集
Rajat Shinde1, Kumar Ankur2, Christopher E Phillips2
1Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL, USA. rajat.shinde@uah.edu.
Scientific data
|March 5, 2026
概括
WxC-Bench是一个新的多模式数据集,用于在天气和气候研究中开发人工智能 (AI) 模型. 它解决了针对各种大气尺度和应用的精心策划,准备使用ML的数据集的稀缺问题.
科学领域:
- 气象学和气候科学 气象学和气候科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 高质量,可访问的机器学习 (ML) 准备数据集对于在天气和气候分析等科学应用中推进AI至关重要.
- 在精选的,预处理的ML-ready数据集中存在很大的差距,阻碍了对大气研究的新型深度学习模型的开发.
- 跨不同空间和时间尺度的数据模式变化为创建可泛化的AI模型带来了挑战.
研究的目的:
- 介绍WxC-Bench,一个多模式数据集,旨在促进用于天气和气候研究的可通用AI模型的开发.
- 支持创建能够分析各种各样的大气过程在各种尺度的人工智能模型.
- 提供一个标准化资源,用于基准测试和推进气象学中的AI应用.
主要方法:
- 开发了WxC-Bench,这是一个多模式数据集,涵盖了与大气过程相关的数据,从中位-β到同位尺度.
- 包括各种下游用例,如流检测,风监测,天气模拟识别,重力波参数化和自然语言报告生成.
- 通过基线分析进行了技术验证,以证明数据集的实用性.
主要成果:
- WxC-Bench提供了一个全面的多模式数据集,专为天气和气候中的AI模型开发量身定制.
- 该数据集涵盖了广泛的大气现象和尺度,使得对各种应用的研究成为可能.
- 基线分析证实了数据集对于评估和推进人工智能模型的适用性.
结论:
- WxC-Bench解决了在天气和气候AI研究中对精心策划,准备使用ML的数据集的关键需求.
- 该数据集促进了大气科学更可通用和更强大的AI模型的开发.
- 拥抱脸的公开可用的代码和数据集鼓励社区参与和进一步研究.
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