Projekter pr. år
Beskrivelse
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.
| Dato for tilgængelighed | feb. 2025 |
|---|---|
| Forlag | Github |
Projekter
- 2 Igangværende
-
A Novel Synergy of Physics-based and Data-driven Methods for Reliable Hydrological Predictions under Changing Climate
Schumacher, M. (PI (principal investigator)), Forootan, E. (CoI (co-investigator)), Döll, P. (CoI (co-investigator)), Wedi, N. (CoI (co-investigator)), Bates, P. (CoI (co-investigator)), Jagdhuber, T. (CoI (co-investigator)) & van Dijk, A. I. (CoI (co-investigator))
01/04/2024 → 31/03/2029
Projekter: Projekt › Forskning
-
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
- 1 Tidsskriftartikel
-
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, I: Journal of Geophysical Research. 52, 7, e2024GL112677.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil3 Downloads (Pure)