19 citations · 33 across the 7 of their papers we have counts for
8 papers · 1 filter
WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
Stephan Rasp, Boris Babenko, Dominic Masters +22
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spati…
Caravan MultiMet: Extending Caravan with Multiple Weather Nowcasts and Forecasts
Guy Shalev, Frederik Kratzert
The Caravan large-sample hydrology dataset (Kratzert et al., 2023) was created to standardize and harmonize streamflow data from various regional datasets, combined with globally a…
AI Increases Global Access to Reliable Flood Forecasts
Grey Nearing, Deborah Cohen, Vusumuzi Dube +15
Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely war…
Flood forecasting with machine learning models in an operational framework
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal +28
The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large,…
Accurate Hydrologic Modeling Using Less Information
Guy Shalev, Ran El-Yaniv, Daniel Klotz +3
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. R…
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Frederik Kratzert, Daniel Klotz, Guy Shalev +3
Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade si…