Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products

Tuesday, 16 December 2014
Lei Ji, ASRC InuTeq, USGS EROS Center, Sioux Falls, SD, United States, Gabriel B Senay, USGS EROS, Sioux Falls, SD, United States and James P Verdin, USGS/EROS, Boulder, CO, United States
There is a high demand for agro-hydrologic models to use gridded surface air temperature data as the model input for estimating regional and global water budget and cycle. The Global Land Data Assimilation System (GLDAS) developed by combining simulation models with observations provides a long-term gridded meteorological dataset at the global coverage. However, the GLDAS air temperature products have not been comprehensively evaluated, although the accuracy of the products was assessed in limited areas. In this study, we compared the daily 0.25° resolution GLDAS air temperature data with two reference datasets: (1) 1-km resolution gridded Daymet data (2002 and 2010) for the Conterminous United States, and (2) global meteorological observations (2000 - 2011) archived from the Global Historical Climatology Network (GHCN). The comparison of the GLDAS datasets with the GHCN datasets including 13,511 weather stations indicates a fairly high accuracy of the GLDAS data for daily maximum temperature [bias is 1.2 C°, root mean square error (RMSE) is 3.9 C°, and R2 is 0.92] and daily minimum temperature (bias is -1.4 C°, RMSE is 5.4 C°, and R2 is 0.82). The quality of the GLDAS air temperature data, however, is not always consistent in different regions of the world; for example, some areas in Africa and South America show relatively low accurate estimates. Spatial and temporal analyses reveal a high agreement between GLDAS and Daymet daily air temperature datasets, although spatial details in high mountainous areas are not sufficiently estimated by the GLDAS data. Our evaluation of the GLDAS data demonstrates that the air temperature estimates are generally accurate, but cautions should be taken when the data are used in mountainous areas or places with sparse weather stations.