Meteorological indicators are very commonly used in daily research. We have previously shared with you the meteorological indicator raster data provided by the National Qinghai-Tibet Plateau Scientific Data Center:
- 1km resolution monthly average air temperature raster data from 1901 to 2022 (see the previous article for details)
What we are sharing this time is also the high-precision meteorological index grid data from the National Qinghai-Tibet Plateau Scientific Data Center—the monthly minimum temperature grid data with a resolution of 1km from 1901 to 2022 ! The data is continuously updated, and the data for 2022 was just updated on May 31!
The unit of monthly maximum temperature data downloaded from the official website is 0.1 ℃, and the data format is NETCDF, which is .nc format. For your convenience, we have performed some processing on the original data, converted the unit into Celsius (°C), and converted the format into raster (.tif) format. In addition, the nationwide data is very large and inconvenient to use. We have divided the national data into provincial data! It is important to note that the monthly minimum temperature here is the monthly average of the daily minimum temperature in that month !
The following is a detailed introduction of the data:
01Data preview
nationwide data
First of all, let's take a look at the nationwide data. We will provide three kinds of data:
①Data in original nc format
② data in tif format with a spatial range larger than the national border of China
Let's take the national minimum temperature in December 2022 as an example to preview. The range of the .tif format data converted from the original .nc format data is a rectangular range, which is larger than the national border of China:
③ tif format data within China's borders
We extract the minimum temperature data within the national boundary range:
Data by province
For the data by province, let's take the minimum temperature of Hubei Province and Shandong Province in December 2022 as an example to preview:
02 Data details Data source:
The data comes from the data shared by Peng Shouzhang on the platform of the National Qinghai-Tibet Plateau Scientific Data Center.
the data shows:
The dataset is explained on the official website. The data is generated by downscaling in China through the Delta spatial downscaling scheme based on the global 0.5° climate dataset released by CRU and the global high-resolution climate dataset released by WorldClim. Moreover, the data of 496 independent meteorological observation points are used for verification, and the verification results are credible. The geographical spatial scope included in this dataset is the main land of the country (including Hong Kong, Macao and Taiwan), excluding areas such as South China Sea islands and reefs. The data coordinate system is recommended to use WGS84.
Data Format:
Raster format (.tif) and NETCDF (.nc) formats
Data unit:
Raster (.tif) format: Celsius (°C)
NETCDF (.nc) format: 0.1 °C
time limit:
1901-2022 (Monthly)
Data coordinates:
for GCS_WGS_1984
Space range:
National/Provincial
Spatial resolution:
0.0083333° (about 1km)
Data references:
Peng Shouzhang. (2020). 1km monthly maximum temperature dataset for China (1901-2022). National Tibetan Plateau Scientific Data Center. Peng, S. (2020). 1-km monthly maximum temperature dataset for China (1901-2022) .National Tibetan Plateau Data Center.
https://doi.org/10.5281/zenodo.3114194.
Citation from the article publishing the data:
1.Peng, S.Z., Ding, Y.X., Wen, Z.M., Chen, Y.M., Cao, Y., & Ren, J.Y. (2017). Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011–2100. Agricultural and Forest Meteorology, 233, 183–194.
2.Ding, Y.X., & Peng, S.Z. (2020). Spatiotemporal trends and attribution of drought across China from 1901–2100. Sustainability, 12(2), 477.
3.Peng, S.Z., Ding, Y.X., Liu, W.Z., & Li, Z. (2019). 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 11, 1931–1946. https://doi.org/10.5194/essd-11-1931-2019
4.Peng, S. , Gang, C. , Cao, Y. , & Chen, Y. . (2017). Assessment of climate change trends over the loess plateau in china from 1901 to 2100. International Journal of Climatology.
If you have data usage needs, please refer to it according to the requirements of the official platform. For more data details, you can check the official website to learn!
At the bottom of the article is our official account business card. We will regularly introduce various types of urban data and data visualization and analysis technology. Regarding the 1km resolution monthly minimum temperature grid data from 1901 to 2022, you are welcome to pay more attention to us to learn more!