Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Yang, Fan, Schumacher, Maike, Retegui-Schiettekatte, Leire, van Dijk, Albert I. J. M., and Forootan, Ehsan, 2025. PyGLDA: a fine-scale python-based global land data assimilation system for integrating satellite gravity data into hydrological models. Geoscientific Model Development, 18(18):6195–6217, doi:10.5194/gmd-18-6195-2025.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025GMD....18.6195Y,
author = {{Yang}, Fan and {Schumacher}, Maike and {Retegui-Schiettekatte}, Leire and {van Dijk}, Albert I.~J.~M. and {Forootan}, Ehsan},
title = "{PyGLDA: a fine-scale python-based global land data assimilation system for integrating satellite gravity data into hydrological models}",
journal = {Geoscientific Model Development},
year = 2025,
month = sep,
volume = {18},
number = {18},
pages = {6195-6217},
abstract = "{Data assimilation (DA) of time-variable satellite gravity observations,
such as those from the Gravity Recovery and Climate Experiment
(GRACE), GRACE Follow-On (GRACE-FO), and future gravity
missions, can be used to constrain simulations of the vertical
sum of water storage in Global Hydrological Models (GHMs).
However, current DA implementations of these Terrestrial Water
Storage (TWS) changes are often performed at regional scales or,
if applied globally, at low spatial resolutions. This limitation
is primarily due to the high computational demands of DA and
numerical challenges, such as instabilities in covariance matrix
inversion. To fully exploit the potential of satellite gravity
observations and the high spatial resolution of GHMs, we
developed PyGLDA, an open-source Python-based system that
enables fine-scale and computationally efficient global DA. The
key innovations of PyGLDA include (1) a global patch-wise DA
approach using domain localization and neighboring-weighted
global aggregation and (2) seamless compatibility between basin-
scale and grid-scale DA implementations. PyGLDA represents a
significant functional improvement over previous DA systems,
offering wide-ranging and flexible options for user-specific
applications. The modular structure of the system allows users
to customize water storage compartments, modify observation
representations, and potentially select different GHMs. This
paper provides a comprehensive description of PyGLDA and its
application in a case study of the Danube River Basin, along
with a demonstration of global DA, where experiments involve
integrating monthly GRACE TWS fields (2002{\textendash}2010)
with the daily W3RA water balance model at 0.1{\textdegree}
spatial resolution.}",
doi = {10.5194/gmd-18-6195-2025},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025GMD....18.6195Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner