3 citations · 5 across the 8 of their papers we have counts for
9 papers
Suitable Measures for the Potential Operational Utility of AI NWP Rainfall Forecasts Over Africa
Shruti Nath, Docko Sow, Koomi Toussaint Amoussouvi +5
Artificial intelligence (AI)-based weather prediction is approaching the skill of physical numerical weather prediction (NWP) systems at a fraction of the computational cost. This…
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, Oisín 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…
Improved rainfall forecasts in daily use over East Africa
Fenwick C. Cooper, Shruti Nath, Andrew T. T. McRae +21
Ensemble forecasting has proven to be a vital tool for predicting extreme, life-threatening or only partially predictable weather events. As well as providing probabilistic product…
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…
Robustness of AI-based weather forecasts in a changing climate
Thomas Rackow, Nikolay Koldunov, Christian Lessig +9
Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics…
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington +7
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive…