MambaDS:利用地形约束选择性状态空间建模进行近地表气象场降维分析

Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Wanli Ouyang, Zhengxia Zou, Zhenwei Shi
{"title":"MambaDS:利用地形约束选择性状态空间建模进行近地表气象场降维分析","authors":"Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Wanli Ouyang, Zhengxia Zou, Zhenwei Shi","doi":"arxiv-2408.10854","DOIUrl":null,"url":null,"abstract":"In an era of frequent extreme weather and global warming, obtaining precise,\nfine-grained near-surface weather forecasts is increasingly essential for human\nactivities. Downscaling (DS), a crucial task in meteorological forecasting,\nenables the reconstruction of high-resolution meteorological states for target\nregions from global-scale forecast results. Previous downscaling methods,\ninspired by CNN and Transformer-based super-resolution models, lacked tailored\ndesigns for meteorology and encountered structural limitations. Notably, they\nfailed to efficiently integrate topography, a crucial prior in the downscaling\nprocess. In this paper, we address these limitations by pioneering the\nselective state space model into the meteorological field downscaling and\npropose a novel model called MambaDS. This model enhances the utilization of\nmultivariable correlations and topography information, unique challenges in the\ndownscaling process while retaining the advantages of Mamba in long-range\ndependency modeling and linear computational complexity. Through extensive\nexperiments in both China mainland and the continental United States (CONUS),\nwe validated that our proposed MambaDS achieves state-of-the-art results in\nthree different types of meteorological field downscaling settings. We will\nrelease the code subsequently.","PeriodicalId":501166,"journal":{"name":"arXiv - PHYS - Atmospheric and Oceanic Physics","volume":"59 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling\",\"authors\":\"Zili Liu, Hao Chen, Lei Bai, Wenyuan Li, Wanli Ouyang, Zhengxia Zou, Zhenwei Shi\",\"doi\":\"arxiv-2408.10854\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In an era of frequent extreme weather and global warming, obtaining precise,\\nfine-grained near-surface weather forecasts is increasingly essential for human\\nactivities. Downscaling (DS), a crucial task in meteorological forecasting,\\nenables the reconstruction of high-resolution meteorological states for target\\nregions from global-scale forecast results. Previous downscaling methods,\\ninspired by CNN and Transformer-based super-resolution models, lacked tailored\\ndesigns for meteorology and encountered structural limitations. Notably, they\\nfailed to efficiently integrate topography, a crucial prior in the downscaling\\nprocess. In this paper, we address these limitations by pioneering the\\nselective state space model into the meteorological field downscaling and\\npropose a novel model called MambaDS. This model enhances the utilization of\\nmultivariable correlations and topography information, unique challenges in the\\ndownscaling process while retaining the advantages of Mamba in long-range\\ndependency modeling and linear computational complexity. Through extensive\\nexperiments in both China mainland and the continental United States (CONUS),\\nwe validated that our proposed MambaDS achieves state-of-the-art results in\\nthree different types of meteorological field downscaling settings. We will\\nrelease the code subsequently.\",\"PeriodicalId\":501166,\"journal\":{\"name\":\"arXiv - PHYS - Atmospheric and Oceanic Physics\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Atmospheric and Oceanic Physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.10854\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Atmospheric and Oceanic Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.10854","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在极端天气频发和全球变暖的时代,获得精确、精细的近地面天气预报对人类活动越来越重要。降尺度(DS)是气象预报中的一项重要任务,它可以从全球尺度的预报结果中重建目标区域的高分辨率气象状态。以前的降尺度方法受到基于 CNN 和 Transformer 的超分辨率模型的启发,但这些方法缺乏针对气象学的定制设计,而且在结构上存在局限性。值得注意的是,它们未能有效地整合地形,而地形是降尺度过程中的一个关键先验因素。本文针对这些局限性,率先将这些选择性状态空间模型引入气象领域降尺度,并提出了一种名为 MambaDS 的新型模型。该模型增强了对多变量相关性和地形信息的利用,这些都是降尺度过程中的独特挑战,同时保留了 Mamba 在远距离依赖建模和线性计算复杂性方面的优势。通过在中国大陆和美国大陆(CONUS)的广泛试验,我们验证了我们提出的 MambaDS 在三种不同类型的气象现场降尺度设置中取得了最先进的结果。我们将随后发布代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling
In an era of frequent extreme weather and global warming, obtaining precise, fine-grained near-surface weather forecasts is increasingly essential for human activities. Downscaling (DS), a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions from global-scale forecast results. Previous downscaling methods, inspired by CNN and Transformer-based super-resolution models, lacked tailored designs for meteorology and encountered structural limitations. Notably, they failed to efficiently integrate topography, a crucial prior in the downscaling process. In this paper, we address these limitations by pioneering the selective state space model into the meteorological field downscaling and propose a novel model called MambaDS. This model enhances the utilization of multivariable correlations and topography information, unique challenges in the downscaling process while retaining the advantages of Mamba in long-range dependency modeling and linear computational complexity. Through extensive experiments in both China mainland and the continental United States (CONUS), we validated that our proposed MambaDS achieves state-of-the-art results in three different types of meteorological field downscaling settings. We will release the code subsequently.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river Super Resolution On Global Weather Forecasts Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability? Using Generative Models to Produce Realistic Populations of the United Kingdom Windstorms Integrated nowcasting of convective precipitation with Transformer-based models using multi-source data
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1