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Publication 2022: EM-Earth: The Ensemble Meteorological Dataset for Planet Earth
Section 1: Publication
Tang, G., Clark, M.P. and Papalexiou, S.M.
EM-Earth: The Ensemble Meteorological Dataset for Planet Earth
Bulletin of the American Meteorological Society
Tang, G., Clark, M.P. and Papalexiou, S.M., 2022. EM-Earth: The Ensemble Meteorological Dataset for Planet Earth. Bulletin of the American Meteorological Society.
Section 2: Abstract
Gridded meteorological estimates are essential for many applications. Most existing meteorological datasets are deterministic and have limitations in representing the inherent uncertainties from both the data and methodology used to create gridded products. We develop the Ensemble Meteorological Dataset for Planet Earth (EM-Earth) for precipitation, mean daily temperature, daily temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications. To produce EM-Earth, we first developed a station-based Serially Complete Earth (SC-Earth) dataset, which removes the temporal discontinuities in raw station observations. Then, we optimally merged SC-Earth station data and ERA5 reanalysis estimates to generate EM-Earth deterministic estimates and their uncertainties. The EM-Earth ensemble members are produced by sampling from parametric probability distributions using spatiotemporally correlated random fields. The EM-Earth dataset is evaluated by leave-one-out validation, using independent evaluation stations, and comparing it with many widely used datasets. The results show that EM-Earth is better in Europe, North America, and Oceania than in Africa, Asia, and South America, mainly due to differences in the available stations and differences in climate conditions. Probabilistic spatial meteorological datasets are particularly valuable in regions with large meteorological uncertainties, where almost all existing deterministic datasets face great challenges in obtaining accurate estimates.
Section 3: Download
Section 4: Computed Information
T-2022-02-23-q1ZNO0q3lTL02IvrcQRTgq2UQ Publication 1.0