Python software SaGEA: an efficient and accurate Python tool for uncertainty quantification of GRACE based TWS
Python software PyHawk: An efficient gravity recovery solver for low-low satellite-to-satellite tracking gravity missions
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Python software DL4TWS for Bayesian Convolutional Neural Network (BCNN) and "near-real-time monitoring of global terrestrial water storage anomalies and hydrological droughts"
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PyGLDA: a fine-scale Python-based Global Land Data Assimilation system for integrating satellite gravity data into hydrological models
Our group has developed a Python-based open-source PyGLDA system that allows performing hydrological Data Assimilation (DA) globally at a fine scale with high numerical efficiency. The main novelties of the system include (i) implementing a globe-scale patch-wise DA via domain localization and neighbouring-weighted global aggregation and (2) its great compatibility between basin-scale and grid-scale DAs. This PyGLDA system represents a considerable functional advancement on previous implementations with wide and flexible options offered to allow for various user-specific studies. The modular structure of PyGLDA provides users with various possibilities to interact with (and add/remove) individual water storage compartments, change the representation of observations, and, therefore, the ability to choose different Global Hydrological Models (GHMs).
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https://doi.org/10.5281/zenodo.12206756
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Yang, F., Schumacher, M., Retegui-Schiettekatte, L., van Dijk, A. I. J. M., and Forootan, E.: PyGLDA: a fine-scale Python-based Global Land Data Assimilation system for integrating satellite gravity data into hydrological models, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2024-125, in review, 2024.
MCFVC-GFO: An error propagation software for GRACE and GRACE-FO products
Our group has developed a Monte Carlo Full Variance-Covariance (MCFVC) error propagation approach to precisely compute uncertainties of GRACE and GRACE-FO level 2 products in terms of Terrestrial Water Storage (TWS). The software gives a reliable estimation of gridded TWS fields after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%.
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Figshare. https://doi.org/10.6084/m9.figshare.24272485.v2Yang
How to cite:
Yang, F., Forootan, E., Liu, S., & Schumacher, M. (2024). A Monte Carlo propagation of the full variance-covariance of GRACE-like level-2 data with applications in hydrological data assimilation and sea-level budget studies. Water Resources Research, 60, e2023WR036764. https://doi.org/10.1029/2023WR036764
R-scripts for the cartographic enhancement of buildings
‘buildenh’ – an open-source package of R-scripts for the cartographic enhancement of buildings generated by classification of remote sensing imagery, 2024. https://github.com/ImgEnh/buildenh_v1.4.0