Projects per year
Description
This repository provides a PyTorch implementation of a Bayesian Convolutional Neural Network (BCNN) designed to predict GRACE/FO Terrestrial Water Storage Anomaly (TWSA) fields during the typical 3-month latency period before GRACE/FO data becomes available.
| Date made available | Feb 2025 |
|---|---|
| Publisher | Github |
Projects
- 2 Active
-
A Novel Synergy of Physics-based and Data-driven Methods for Reliable Hydrological Predictions under Changing Climate
Schumacher, M. (PI), Forootan, E. (CoI), Döll, P. (CoI), Wedi, N. (CoI), Bates, P. (CoI), Jagdhuber, T. (CoI) & van Dijk, A. I. (CoI)
01/04/2024 → 31/03/2029
Project: Research
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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), Schumacher, M. (CoI), Yang, F. (Project Participant) & Retegui Schiettekatte, L. A. (Project Participant)
Uddannelses- og forskningsministeriet
01/09/2022 → 31/08/2026
Project: Research
Research output
- 1 Journal article
-
Near-real-time monitoring of global terrestrial water storage anomalies and hydrological droughts
Mo, S., Schumacher, M., van Dijk, A. I., Shi, X., Wu, J. & Forootan, E., 16 Apr 2025, In: Journal of Geophysical Research. 52, 7, e2024GL112677.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile3 Downloads (Pure)