2026/02/01 by F. Li, Jürgen Kusche · 1 voice
Earth and Planetary Sciences · Engineering · Social Sciences · #GNSS positioning and interference #Geophysics and Gravity Measurements #Historical Geography and Cartography
paper · pdf · doi:10.1029/2025wr041710
openalex publication_date 2026/02/01 · openalex created_date 2026/02/04 · openalex updated_date 2026/07/23
Abstract Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On (GRACE/‐FO) satellite missions have provided unprecedented measurements of terrestrial water storage changes (TWSC). These data are essential for monitoring the global water cycle, supporting drought and flood risk management, and informing water‐related decision‐making. However, GRACE products are typically released with a latency of several months, limiting their utility for real‐time and operational forecasting applications. In this study, we use machine learning to forecast GRACE‐like TWSC up to 12 months ahead, relying solely on observational and reanalysis‐based inputs. The observation‐driven forecast approach is evaluated over the period 2010–2024 and benchmarked against seasonal forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF)’s new long‐range forecasting system (SEAS5). Our results show that the developed method offers improved accuracy and robustness compared to the ECMWF forecasts, providing a viable data‐driven alternative for operational TWSC forecasting. We generate global forecast data sets at 1° resolution, creating a robust, publicly available resource that extends GRACE‐like insights into the near future. The study addresses the latency of GRACE/‐FO products by offering real‐time TWSC forecasts to support applications such as drought early warning, sea level prediction, hydrological model validation, and geodetic applications such as forecasting Earth orientation parameters via hydrological angular momentum excitation or estimating loading corrections in GNSS and altimetry data analysis. The hindcast data set (2010–2024) evaluated in this study and the regularly updated semi‐operational forecast data set (from 2024 onward) are publicly available at: https://doi.pangaea.de/10.1594/PANGAEA.973113 and https://www.igg.uni‐bonn.de/apmg/de/data‐and‐models/grace‐fo‐forecasting .