MDG625: A daily high-resolution meteorological dataset derived by geopotential-guided attention network in Asia (1940–2023)

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Zijiang Song, Zhixiang Cheng, Yuying Li, Shanshan Yu, Xiaowen Zhang, Lina Yuan, Min Liu
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引用次数: 0

Abstract

Abstract. The long-term and reliable meteorological reanalysis dataset with high spatial-temporal resolution is crucial for various hydrological and meteorological applications, especially in regions or periods with scarce in situ observations and with limited open-access data. Based on the ERA5 (produced by the European Centre for Medium-Range Weather Forecasts, 0.25°×0.25°, since 1940) and CLDAS (China Meteorological Administration Land Data Assimilation System, 0.0625°×0.0625°, since 2008), we proposed a novel downscaling method Geopotential-guide Attention Network (GeoAN) leveraging the high spatial resolution of CLDAS and the extended historical coverage of ERA5 and produced the daily multi-variable (2 m temperature, surface pressure, and 10 m wind speed) meteorological dataset MDG625 (Song et al., 2024). MDG625 (0.0625° Meteorological Dataset derived by GeoAN) covers most of Asia from 0.125° S to 64.875° N and 60.125° E to 160.125° E since 1940. Compared with other downscaling methods, GeoAN shows better performance with the R2 (2 m temperature, surface pressure, and 10 m wind speed reached 0.990, 0.998, and 0.781, respectively). MDG625 demonstrates superior continuity and consistency from both spatial and temporal perspectives. We anticipate that this GeoAN method and this dataset MDG625 will aid in climate studies of Asia and will contribute to improving the accuracy of reanalysis products from the 1940s. The dataset (Song et al., 2024) is presented in https://doi.org/10.57760/sciencedb.17408 and the code can be found in https://github.com/songzijiang/GeoAN.
MDG625:由亚洲位势引导关注网络推导出的高分辨率每日气象数据集(1940-2023 年)
摘要具有高时空分辨率的长期可靠的气象再分析数据集对于各种水文和气象应用至关重要,尤其是在原地观测资料匮乏和开放数据有限的地区或时期。基于 ERA5(由欧洲中期天气预报中心制作,0.25°×0.25°,1940 年开始)和 CLDAS(中国气象局陆地数据同化系统,0.0625°×0.利用 CLDAS 的高空间分辨率和 ERA5 的扩展历史覆盖范围,我们提出了一种新的降尺度方法--位势引导注意网络(GeoAN),并生成了日多变量(2 米气温、地面气压和 10 米风速)气象数据集 MDG625(Song et al、2024).MDG625(由 GeoAN 导出的 0.0625°气象数据集)涵盖了自 1940 年以来南纬 0.125°至北纬 64.875°、东经 60.125°至东经 160.125°的亚洲大部分地区。与其他降尺度方法相比,GeoAN 的 R2(2 米气温、地面气压和 10 米风速分别达到 0.990、0.998 和 0.781)表现更佳。从空间和时间角度来看,MDG625 都表现出了卓越的连续性和一致性。我们预计这种 GeoAN 方法和 MDG625 数据集将有助于亚洲的气候研究,并有助于提高 20 世纪 40 年代再分析产品的精度。数据集(Song 等,2024 年)见 https://doi.org/10.57760/sciencedb.17408,代码见 https://github.com/songzijiang/GeoAN。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍: Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.
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