A global dataset of air temperature derived from satellite remote sensing and weather stations

Air temperature at 2 m above the land surface is a key variable used to assess climate change. However, observations of air temperature are typically only available from a limited number of weather stations distributed mainly in developed countries, which in turn may often report time series with missing values. As a consequence, the record of air temperature observations is patchy in both space and time. Satellites, on the other hand, measure land surface temperature continuously in both space and time. In order to combine the relative strengths of surface and satellite temperature records, we develop a dataset in which monthly air temperature is predicted from monthly land surface temperature for the years 2003 to 2016, using a statistical model that incorporates information on geographic and climatic similarity. We expect this dataset to be useful for various applications involving climate monitoring and land-climate interactions.