A 4 km daily gridded meteorological dataset for China from 2000 to 2020.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-11-14 DOI:10.1038/s41597-024-04029-x

Jielin Zhang, Bo Liu, Siqing Ren, Wenqi Han, Yongxia Ding, Shouzhang Peng

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Abstract

Multi-variate gridded meteorological data with high spatial resolution play a key role in studies related to climate change. This study constructed a 4 km daily gridded meteorological dataset for mainland of China (China Daily Meteorological Dataset; CDMet) from 2000 to 2020. The dataset includes nine meteorological variables: 2-meter air temperature (maximum, minimum, and mean temperatures), total precipitation, skin temperature, 10-meter wind speed, relative humidity, surface pressure, and sunshine duration. CDMet was generated using an adaptive interpolation scheme, which employed thin-plate spline and random forest methods to construct the interpolation model. Six combinations of location and terrain information were designed and used as covariates in the model together with reanalysis data. Validation with independent observation stations and existing datasets showed that CDMet has acceptable accuracy, reasonable seasonal variability, and precise spatial distribution, and its accuracy is comparable to that of other datasets. Due to its comprehensive variables and high resolution, CDMet can be used as input data for hydrological, agricultural, and ecological models.

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2000 年至 2020 年中国 4 公里日网格气象数据集。

高空间分辨率的多变量网格气象数据在气候变化相关研究中发挥着关键作用。本研究构建了 2000 年至 2020 年中国大陆 4 公里日网格气象数据集（China Daily Meteorological Dataset; CDMet）。数据集包括九个气象变量：2 米气温（最高气温、最低气温和平均气温）、总降水量、皮肤温度、10 米风速、相对湿度、地面气压和日照时间。CDMet 采用自适应插值方案生成，该方案使用薄板样条和随机森林方法构建插值模型。设计了六种位置和地形信息组合，并与再分析数据一起用作模型的协变量。独立观测站和现有数据集的验证表明，CDMet 具有可接受的精度、合理的季节变化和精确的空间分布，其精度与其他数据集相当。由于 CDMet 变量全面、分辨率高，可用作水文、农业和生态模型的输入数据。

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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.