ERA5-SH: A global grided scale height dataset for tropospheric parameters based on ERA5 reanalysis.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-04 DOI:10.1038/s41597-025-04714-5

Ruixian Hao, Fei Yang, Zhicai Li, Yuhao Zhang, Lv Zhou, Lei Wang

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Abstract

The scale height (SH) represents the height increment for a certain parameter to decrease to 36.7% (1/e) of its value at a certain height. Here we present ERA5-SH, a gridded dataset containing the SH values of six troposphere key parameters (PWV, WVD, T_m, ZTD, ZHD and ZWD) based on ERA5 reanalysis from 2013 to 2022, with a temporal resolution of 1 hour and a spatial resolution of 1°. The dataset was generated using numerical integral and exponential fitting, and exhibits high reliability with mean coefficients of determination being 0.991, 0.957, 0.980, 0.999, 0.999, and 0.995, respectively. Using the global distributed radiosonde sites as references, the mean RMSE for the six parameters were 0.243 km, 0.189 km, 3.290 km, 0.879 km, 0.681 km, and 0.263 km, respectively. This dataset will contribute to a deeper understanding of the tropospheric vertical distribution and to improve the accuracy of atmospheric delay modeling, which are vital for the advancement of the Earth observation technologies with high precision.

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ERA5- sh：基于ERA5再分析的全球对流层参数格网尺度高度数据集。

尺度高度（SH）表示某一参数在某一高度下降到其值的36.7% （1/e）的高度增量。本文基于ERA5再分析，构建了2013 - 2022年对流层6个关键参数（PWV、WVD、Tm、ZTD、ZHD和ZWD）的网格化数据集ERA5-SH，时间分辨率为1 h，空间分辨率为1°。该数据集采用数值积分和指数拟合方法生成，具有较高的信度，平均决定系数分别为0.991、0.957、0.980、0.999、0.999和0.995。以全球分布探空站点为参考，6个参数的平均RMSE分别为0.243 km、0.189 km、3.290 km、0.879 km、0.681 km和0.263 km。该数据集将有助于更深入地了解对流层垂直分布，提高大气延迟模拟的精度，这对推进高精度对地观测技术至关重要。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.