1 citations · 1 across the 4 of their papers we have counts for
8 papers
Joint distribution of upstream runoff governs downstream river-discharge prediction uncertainty in distributed ML models
Karan Ruparell, Tristan Hascoet, Takemasa Miyoshi +4
Uncertainty quantification of hydrological predictions is necessary to inform operational decisions. Recent generative machine-learning methods have advanced probabilistic streamfl…
From Licensing to Open Access: Designing a Sustainable Transition in Operational Weather Data
Emma Pidduck, Umberto Modigliani, Victoria L. Bennett +3
This translational article documents the European Centre for Medium-Range Weather Forecasts (ECMWF) transition from a restricted data licensing model to open access under CC BY 4.0…
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Maria Luisa Taccari, Kenza Tazi, OisÃn M. Morrison +7
Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transition…
Rainfall forecasts in daily use over East Africa improved by machine learning
Fenwick C. Cooper, Shruti Nath, Andrew T. T. McRae +20
Ensemble forecasting has proven over the years to be a vital tool for predicting extreme or only partially predictable weather events. In particular life-threatening weather events…
The ecological forecast limit revisited: Potential, actual and relative system predictability
Marieke Wesselkamp, Jakob Albrecht, Ewan Pinnington +3
Ecological forecasts are model-based statements about currently unknown ecosystem states in time or space. For a model forecast to be useful to inform decision makers, model valida…
Hydra-LSTM: A semi-shared Machine Learning architecture for prediction across Watersheds
Karan Ruparell, Robert J. Marks, Andy Wood +5
Long Short Term Memory networks (LSTMs) are used to build single models that predict river discharge across many catchments. These models offer greater accuracy than models trained…