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<p>Since more than 10 years, the Earth system modeling group at GFZ (ESMGFZ) provides effective angular momentum (EAM) functions for Earth orientation parameter assessment on a routinely daily basis. In addition to EAM of the individual Earth’s subsystems atmosphere, ocean, and hydrology, the global mass balance is calculated as barystatic sea level variation by solving explicitly the sea-level equation. ESMGFZ provides also 6-day forecasts for all of these EAM products. EAM forecasts are naturally degraded by forecast errors that typically grow with increasing forecast length, but they also show recurring patterns with occasionally higher errors at very short forecast horizons. To characterize such errors which are not randomly distributed in time, we divided the errors into a systematic and a stochastic contribution. In an earlier study, we were able to detect and remove the large systematic fraction occurring in the atmospheric angular momentum (AAM) wind term forecast errors with a cascading forward neural network model, thereby reducing the total forecast error by about 50%. In contrast, we were not able to remove the random error component assed in this study. Nevertheless, we show that machine learning methods are able to predict quasi-daily variations in time variable EAM forecasts error levels. We plan to provide these forecast error estimates along with the deterministic EAM forecast products for subsequent use in, for example, EOP Kalman filter prediction schemes.</p>
</abstract>

Since more than 10 years, the Earth system modeling group at GFZ (ESMGFZ) provides effective angular momentum (EAM) functions for Earth orientation parameter assessment on a routinely daily basis. In addition to EAM of the individual Earth’s subsystems atmosphere, ocean, and hydrology, the global mass balance is calculated as barystatic sea level variation by solving explicitly the sea-level equation. ESMGFZ provides also 6-day forecasts for all of these EAM products. EAM forecasts are naturally degraded by forecast errors that typically grow with increasing forecast length, but they also show recurring patterns with occasionally higher errors at very short forecast horizons. To characterize such errors which are not randomly distributed in time, we divided the errors into a systematic and a stochastic contribution. In an earlier study, we were able to detect and remove the large systematic fraction occurring in the atmospheric angular momentum (AAM) wind term forecast errors with a cascading forward neural network model, thereby reducing the total forecast error by about 50%. In contrast, we were not able to remove the random error component assed in this study. Nevertheless, we show that machine learning methods are able to predict quasi-daily variations in time variable EAM forecasts error levels. We plan to provide these forecast error estimates along with the deterministic EAM forecast products for subsequent use in, for example, EOP Kalman filter prediction schemes.

ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors

Artificial Satellites

This work is licensed under the Creative Commons Attribution 4.0 International License.

Since more than 10 years, the Earth system modeling group at GFZ (ESMGFZ) provides effective angular momentum (EAM) functions for Earth orientation parameter...

ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors

Pubblicato online: 19 gen 2024

Pagine: 330 - 340

Ricevuto: 25 mag 2023

Accettato: 04 dic 2023

DOI: https://doi.org/10.2478/arsa-2023-0013

Parole chiave
effective angular momentum function, machine learning, forecast error, level of uncertainty

© 2023 Robert Dill et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

ESMGFZ EAM Products for EOP Prediction: Toward the Quantification of Time Variable EAM Forecast Errors

Pubblicato online: 19 gen 2024

Pagine: 330 - 340

Ricevuto: 25 mag 2023

Accettato: 04 dic 2023

DOI: https://doi.org/10.2478/arsa-2023-0013

Parole chiaveeffective angular momentum function, machine learning, forecast error, level of uncertainty

© 2023 Robert Dill et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
effective angular momentum function, machine learning, forecast error, level of uncertainty