Projekter pr. år
Beskrivelse
The level-2 time-variable gravity fields obtained from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) mission are widely used in multi-discipline geo-science studies. However, the post-processing of those gravity fields to obtain a desired signal is rather challenging for users that are not familiar with the level-2 products. In addition, the error assessment/quantification of those derived signals, which is of increasing demand in science application, is still a challenging issue even among the professional GRACE(-FO) users. In this effort, the common post-processing steps and the assessment of complicated error (uncertainty) of GRACE(-FO), are integrated into an open-source, cross-platform and Python-based toolbox called SAGEA (SAtellite Gravity Error Assessment). With diverse options, SAGEA provides flexibility to generate signal along with the full error from level-2 products, so that any non-expert user can easily obtain advanced experience of GRACE(-FO) processing. Please contact Shuhao Liu ([email protected]) and Fan Yang ([email protected]) for more information.
Dato for tilgængelighed | dec. 2024 |
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Forlag | Github |
Projekter
- 1 Igangværende
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DANSk-LSM: Developing efficient multi-sensor Data Assimilation frameworks for integrating Earth ObservatioN Satellite data into Land Surface Models (DANSk-LSM)
Forootan, E. (PI (principal investigator)), Schumacher, M. (CoI (co-investigator)), Yang, F. (Projektdeltager) & Retegui Schiettekatte, L. A. (Projektdeltager)
Uddannelses- og Forskningsministeriet
01/09/2022 → 31/08/2026
Projekter: Projekt › Forskning
Publikation
- 3 Tidsskriftartikel
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SAGEA: A toolbox for comprehensive error assessment of GRACE and GRACE-FO based mass changes
Liu, S., Yang, F. & Forootan, E., feb. 2025, I: Computers & Geosciences. 196, 105825.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
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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
Yang, F., Forootan, E., Liu, S. & Schumacher, M., sep. 2024, I: Water Resources Research. 60, 9, 31 s., e2023WR036764.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil2 Citationer (Scopus)12 Downloads (Pure) -
PyGLDA: a fine-scale Python-based Global Land Data Assimilation system for integrating satellite gravity data into hydrological models
Yang, F., Schumacher, M., Retegui-Schiettekatte, L., van Dijk, A. I. & Forootan, E., 19 jul. 2024, (Afsendt) I: Geoscientific Model Development Discussions. 2024, s. 1-34 34 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning