Uncertainty of Atmospheric Winds in Three Widely Used Global Reanalysis Datasets

Abstract

Abstract Atmospheric winds are crucial to the transport of heat, moisture, momentum, and chemical species, facilitating Earth’s climate system interactions. Existing weather and climate studies rely heavily on the wind fields from reanalysis datasets. In this study, we analyze the uncertainty of instantaneous atmospheric winds in three reanalysis (ERA5, MERRA-2, and CFSv2) datasets. We show that the mean wind vector differences (WVDs) between the reanalysis datasets are about 3–6 m s−1 in the troposphere. The mean absolute wind direction differences can be more than 50°. Large WVDs greater than 5 m s−1 are found for 30%–50% of the time when the observed precipitation rate is larger than 0.1 mm h−1 over the eastern Pacific Ocean, Indian Ocean, Atlantic Ocean, and some mountain areas. The mean WVDs exhibit seasonal variations but no significant diurnal variations. The uncertainty of vertical wind shear has a correlation of 0.59 with the uncertainty of winds at 300 hPa. The magnitudes of vorticity and horizontal divergence uncertainties are on the order of 1 × 10−5 s−1, which is comparable to the mean values of vorticity and horizontal divergence. In comparison with some limited observations from field campaigns, the reanalysis datasets exhibit a mean WVD ranging from 2 to 4.5 m s−1. Among the three reanalysis datasets, ERA5 shows the closest agreement with the observations while MERRA-2 has the largest discrepancy. The substantial uncertainty and errors of the reanalysis wind products highlight the critical need for new satellite missions dedicated to 3D wind measurements.