@COMMENT This file was generated by bib2html_grace.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <https://sourceforge.net/users/patstg/>
@COMMENT This file was prepared using the NASA Astrophysics Data System (ADS)
@COMMENT https://ui.adsabs.harvard.edu/
@ARTICLE{2026WRR....6241710L,
       author = {{Li}, Fupeng and {Kusche}, J{\"u}rgen},
        title = "{Observation-Driven Forecast of Global Terrestrial Water Storage and Evaluation for 2010--2024}",
      journal = {Water Resources Research},
     keywords = {GRACE, total water storage, seasonal forecasting, machine learning, LSTM},
         year = 2026,
        month = feb,
       volume = {62},
       number = {2},
          eid = {e2025WR041710},
        pages = {e2025WR041710},
     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{\textdegree} 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.}",
          doi = {10.1029/2025WR041710},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2026WRR....6241710L},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}