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Matter and motion terms of effective angular momentum functions (EAM) due to mass transport processes in atmosphere, oceans, and the continental hydrosphere are provided with a temporal resolution of down to 3 hours as GFZ's contribution to the Global Geophysical Fluid Center (GGFC) of the International Earth Rotation and Reference Systems Service (IERS).

Available are individual EAM for atmosphere (AAM), dynamic ocean (OAM), continental hydrosphere (HAM), and barystatic sea-level (SLAM). All time-series start in January 1976 and are routinely updated every day at about 10:00 UTC with all 8 time-steps of the previous day. In addition, 3-hourly forecasts of AAM, OAM, HAM, and SLAM for up to 6 days; and combined EAM forecast for up to 90 days into the future are routinely provided.

For more information on the individual EAM datasets please download and read the Product Description Document

UPDATE (2019-03-04):
SLAM v1.1: The new version uses the sea-level equation, considering gravimetric effects from loading and self-attraction acting on the global ocean, instead of simple homogeneous global mean sea-level to balance the excess masses from continental hydrology and atmosphere. The effects of loading and self-attraction are mainly visible in the sub-monthly polar motion excitation. In the seasonal signal differences are in the order of max. 2%.

UPDATE (2019-05-30):
SLAM v1.2: The new version considers also rotational deformation in the sea-level equation changing the amplitudes and phase of the annual polar motion signal, mainly in CHI2.

UPDATE (2021-11-30):
AAM Windcorrected: Alternative AAM data set, where CHI1 and CHI2 motion terms are changed. The equatorial motion terms show artificial signals in the forecasts that do not exist in the subsequently processed AAM analysis data. These signals are reduced by a post-processing step based on a neural network trained on forecasts and corresponding analysis data.

R. Dill, J. Saynisch-Wagner, C. Irrgang, M. Thomas (2021), Improving atmospheric angular momentum by machine learning. Earth and Spaxe Science, ?, pp. -, doi: ???

When using EAM data, the data and the approach should be cited as

Dobslaw, H., Dill, R., (2018): Predicting Earth Orientation Changes from Global Forecasts of Atmosphere-Hydrosphere Dynamics. - Adv. Space Res., 61(4), 1047-1054.

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