资源环境科技发展态势分析平台

High-resolution gridded climate data products are crucial to research and practical applications in climatology, hydrology, ecology, agriculture, and public health. Previous works to produce multiple data sets were limited by the availability of input data as well as computational techniques. With advances in machine learning and the availability of several daily satellite data sets providing unprecedented information at 1 km or higher spatial resolutions, it is now possible to improve upon earlier data sets in terms of representing spatial variability. We developed the NEX (NASA Earth Exchange) Gridded Daily Meteorology (NEX-GDM) model, which can estimate the spatial pattern of regional surface climate variables by aggregating several dozen two-dimensional data sets and ground weather station data. NEX-GDM does not require physical assumptions and can easily extend spatially and temporally. NEX-GDM employs the random forest algorithm for estimation, which allows us to find the best estimate from the spatially continuous data sets. We used the NEX-GDM model to produce historical 1-km daily spatial data for the conterminous United States from 1979 to 2017, including precipitation, minimum temperature, maximum temperature, dew point temperature, wind speed, and solar radiation. In this study, NEX-GDM ingested a total of 30 spatial variables from 13 different data sets, including satellite, reanalysis, radar, and topography data. Generally, the spatial patterns of precipitation and temperature produced were similar to previous data sets with the exception of mountain regions in the western United States. The analyses for each spatially continuous data set show that satellite and reanalysis led to better estimates and that the incorporation of satellite data allowed NEX-GDM to capture the spatial patterns associated with urban heat island effects. The NEX-GDM data is available to the community through the NEX data portal.