Spatial interpolation dataset of average conditions of meteorological elements in China

Mondo Military Updated on 2024-02-02

Summary:

Based on the daily observation data of meteorological element stations of more than 2400 stations in China, the spatial interpolation dataset of meteorological elements in China is based on the annual value of each meteorological element, and the spatial interpolation data of meteorological elements in each year from 1960 to 2021 is generated based on ANUSPL interpolation software, and then the spatial interpolation dataset of meteorological elements in each decade from 1960 to 2020 is generated by statistical analysis.

Key words: China Meteorological background Temperature Precipitation Accumulated temperature Wetness index

Data description

Weather is one of the eternal topics of human beings. In the long process of historical changes, climate and meteorology have had an extremely significant impact on human society. Meteorological conditions refer to the hydrothermal conditions of various weather phenomena. Climate impact is a common topic of discussion in the 21st century, which refers to the general state of the atmosphere in a certain area of the earth and the impact of its changes on the environment of human life, production and social activities. The topic of climate impacts is both global and regional. Since the 20th century, with the rapid development of the world economy, the acceleration of industrialization, the rapid growth of population, the overexploitation of mineral fuels and non-renewable energy, the irrational use of land, and the large-scale deforestation of forests, resulting in a sharp increase in greenhouse gases such as CO2, CH4, O3 and hydrochlorofluorocarbons in the atmosphere, and changes in the global climate. Climate change is directly or indirectly impacting natural ecosystems. Studies have shown that climate change has affected a variety of natural and biological systems, such as glacier retreat, melting permafrost, sea level rise, hurricanes, floods, blizzards, land droughts, forest fires, species variation and extinction, famine and disease, as well as extended growing seasons in mid- and high-latitude regions, affecting species distribution areas, biological population structure and diversity, ecosystem vulnerability, etc., climate change transcends national borders and endangers all living beings, including human beings themselves. Relevant scientific research and facts show that global warming will be a large-scale disaster for mankind. It will cause a northward shift in climate zones, redistribute global precipitation, melt glaciers and permafrost, cause sea levels to rise, and have a direct impact on water resources, as well as global vegetation distribution, ecosystem structure and soil development over longer time scales, and ultimately on the diversity of biological species. These negative impacts of climate change are of greater concern.

Based on the daily observation data of meteorological element stations of more than 2400 stations in China, the spatial interpolation dataset of meteorological elements in China is based on the annual value of each meteorological element, and the spatial interpolation data of meteorological elements in each year from 1960 to 2021 is generated based on ANUSPL interpolation software, and then the spatial interpolation dataset of meteorological elements in each decade from 1960 to 2020 is generated by statistical analysis.

The spatial interpolation dataset of the mean condition of meteorological elements in China adopts the equal area secant conic projection. The national unified ** warp and double standard latitude, ** longitude is 105 ° east longitude, and the double standard latitude is 25 ° north latitude and 47 ° north latitude respectively, using Krasovsky ellipsoid. The spatial interpolation dataset of the average state of meteorological elements in China is 1km raster data, the data format is TIFF and ArcGIS Grid format, and the data file name and data content are as follows (xxxx is the year, 60-10s represents 1960s-2010s).

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