506 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- P. Dueben2 profiles13 · h 20
- P. Smolarkiewicz2 profiles11 · h 59
- T. Palmer3 profiles10 · h 86
- G. Guerrero2 profiles7 · h 11
- J. Bidlot7 · h 50
- Ø. Breivik6 · h 34
- A. Kosovichev2 profiles5 · h 54
- F. Pappenberger2 profiles5 · h 80
- M. Chantry5 · h 20
- P. Janssen4 · h 47
- E. M. de Gouveia Dal Pino3 · h 22
- L. Magnusson3 · h 32
- University of OxfordGB10 papers
- Norwegian Meteorological InstituteNO8 papers
- University of ReadingGB8 papers
- Imperial College LondonGB7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Universidade Federal de Minas GeraisBR6 papers
- New Jersey Institute of TechnologyUS5 papers
- Ames Research CenterUS4 papers
- NSF National Center for Atmospheric ResearchUS4 papers
- University of DebrecenHU4 papers
- Deutscher WetterdienstDE3 papers
- ETH ZurichCH3 papers
14 papers · 1 filter
High-resolution Probabilistic Precipitation Prediction for use in Climate Simulations
Sherman Lo, Peter Watson, Peter Dueben +1
The accurate prediction of precipitation is important to allow for reliable warnings of flood or drought risk in a changing climate. However, to make trust-worthy predictions of pr…
Copula-based synthetic data augmentation for machine-learning emulators
David Meyer, Thomas Nagler, Robin J. Hogan
Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be…
Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows
Jan Ackmann, Peter D. Düben, Tim N. Palmer +1
It is tested whether machine learning methods can be used for preconditioning to increase the performance of the linear solver -- the backbone of the semi-implicit, grid-point mode…
Using machine learning to correct model error in data assimilation and forecast applications
Alban Farchi, Patrick Laloyaux, Massimo Bonavita +1
The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences, in which the key output is a surrogate mo…
Extreme precipitation events in the Mediterranean: Spatiotemporal characteristics and connection to large-scale atmospheric flow patterns
Nikolaos Mastrantonas, Pedro Herrera-Lormendez, Linus Magnusson +2
The Mediterranean is strongly affected by Extreme Precipitation Events (EPEs), sometimes leading to negative impacts on society, economy, and the environment. Understanding such na…
A comparative study of convolutional neural network models for wind field downscaling
Kevin Höhlein, Michael Kern, Timothy Hewson +1
We analyze the applicability of convolutional neural network (CNN) architectures for downscaling of short-range forecasts of near-surface winds on extended spatial domains. Short-r…
The 2019/20 Australian wildfires generated a persistent smoke-charged vortex rising up to 35 km altitude
Sergey Khaykin, Bernard Legras, Silvia Bucci +9
The Australian bushfires around the turn of the year 2020 generated an unprecedented perturbation of stratospheric composition, dynamical circulation and radiative balance. Here we…
What limits the number of observations that can be effectively assimilated by EnKF?
Daisuke Hotta, Yoichiro Ota
The ability of ensemble Kalman filter (EnKF) algorithms to extract information from observations is analyzed with the aid of the concept of the degrees of freedom for signal (DFS).…
Deep Learning for Post-Processing Ensemble Weather Forecasts
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun +4
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which c…
Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer +21
Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of…
A new low-cost technique improves weather forecasts across the world
Tim D. Hewson, Fatima M. Pillosu
Computer-generated forecasts divide the earth's surface into gridboxes, each now ~25% of the size of London, and predict one value per gridbox. If weather varies markedly within a…
Beyond skill scores: exploring sub-seasonal forecast value through a case study of French month-ahead energy prediction
Joshua Dorrington, Isla Finney, Tim Palmer +1
We quantify the value of sub-seasonal forecasts for a real-world prediction problem: the forecasting of French month-ahead energy demand. Using surface temperature as a predictor,…
WeatherBench: A benchmark dataset for data-driven weather forecasting
Stephan Rasp, Peter D. Dueben, Sebastian Scher +3
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could al…
Statistical post-processing of heat index ensemble forecasts: is there a royal road?
Sándor Baran, Ágnes Baran, Florian Pappenberger +1
We investigate the effect of statistical post-processing on the probabilistic skill of discomfort index (DI) and indoor wet-bulb globe temperature (WBGTid) ensemble forecasts, both…