Evidence map›Paper›PMID 39543106›Full record

ArticleScientific data2024

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

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

Abstract read
In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jielin ZhangState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Soil and Water Conservation Science and Engineering (Institute of Soil and Water Conservation), Northwest A&F University, Yangling, 712100, China.
Bo LiuCollege of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China.
Siqing RenCollege of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China.
Wenqi HanState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Soil and Water Conservation Science and Engineering (Institute of Soil and Water Conservation), Northwest A&F University, Yangling, 712100, China.
Yongxia DingShaanxi Key Laboratory of Disasters Monitoring & Mechanism Simulation, College of Geography and Environment, Baoji University of Arts and Sciences, Baoji, 721013, China.
Shouzhang PengState Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Soil and Water Conservation Science and Engineering (Institute of Soil and Water Conservation), Northwest A&F University, Yangling, 712100, China. szp@nwafu.edu.cn.ORCID http://orcid.org/0000-0002-2358-6329

Funding

National Natural Science Foundation of China (National Science Foundation of China) U2243226, 42401338, 42077451
6 · The paper itself

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.

Identifiers

PMID39543106
PMCPMC11564775

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.