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
36 papers · 1 filter
Observation-guided Interpolation Using Graph Neural Networks for High-Resolution Nowcasting in Switzerland
Ophélia Miralles, Daniele Nerini, Jonas Bhend +2
Recent advances in neural weather forecasting have shown significant potential for accurate short-term forecasts. However, adapting such gridded approaches to smaller, topographica…
Diagnostic vs dynamic representation of the inverse barometer effect in a global ocean model and its potential for probabilistic storm surge forecasting
Nils Melsom Kristensen, Kristian Mogensen, Sarah-Jane Lock +1
The global ocean model NEMO is run in a series of stand-alone configurations (2015-2022) to investigate the potential for improving global medium-range storm surge forecasts by inc…
Seasonal forecasting using the GenCast probabilistic machine learning model
Bobby Antonio, Kristian Strommen, Hannah M. Christensen
Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. Howev…
Radiosonde-constrained reconstructions reveal a weakening Northern Hadley circulation
Matic Pikovnik, Žiga Zaplotnik
The Northern Hadley cell (NHC) is a fundamental component of Earth's atmospheric circulation, governing precipitation patterns affecting nearly four billion people. Despite its imp…
Ensemble reliability and the signal-to-noise paradox in ECMWF subseasonal forecasts
Christopher David Roberts, Frederic Vitart
Ensemble forecasts can exhibit counterintuitive statistical properties such that the correlation between ensemble means and observations () exceeds the correlation between…
Unbiased calculation, evaluation, and calibration of ensemble forecast anomalies
Christopher D. Roberts, Martin Leutbecher
Long-range ensemble forecasts are typically verified as anomalies with respect to a lead-time dependent climatological mean to remove the influence of systematic biases. However, c…
Neural General Circulation Models for Weather and Climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore +13
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics…
The rise of data-driven weather forecasting
Zied Ben-Bouallegue, Mariana C A Clare, Linus Magnusson +14
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some application…
Deep learning for quality control of surface physiographic fields using satellite Earth observations
Tom Kimpson, Margarita Choulga, Matthew Chantry +4
A purposely built deep learning algorithm for the Verification of Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the global physiographic datasets unde…
Perspectives on the importance of complex systems in understanding ourclimate and climate change -- The Nobel Prize in Physics 2021
Shraddha Gupta, Nikolaos Mastrantonas, Cristina Masoller +1
The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their 'groundbreaking contributions to our understanding of complex systems'…
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
Lucy Harris, Andrew T. T. McRae, Matthew Chantry +2
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is t…
The Impact of a Reduced High-wind Charnock Parameter on Wave Growth With Application to the North Sea, the Norwegian Sea and the Arctic Ocean
Øyvind Breivik, Ana Carrasco, Hilde Haakenstad +8
As atmospheric models move to higher resolution and resolve smaller scales, the maximum modeled wind speed also tends to increase. Wave models tuned to coarser wind fields tend to…
A comparison of data-driven approaches to build low-dimensional ocean models
Niraj Agarwal, Dmitri Kondrashov, Peter Dueben +2
We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The re…
What do large-scale patterns teach us about extreme precipitation over the Mediterranean at medium- and extended-range forecasts?
Nikolaos Mastrantonas, Linus Magnusson, Florian Pappenberger +1
Extreme Precipitation Events (EPEs) can have devastating consequences such as floods and landslides, posing a great threat to society and the economy. Predicting such events long i…
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Maike Sonnewald, Redouane Lguensat, Daniel C. Jones +3
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques…
Machine learning emulation of gravity wave drag in numerical weather forecasting
Matthew Chantry, Sam Hatfield, Peter Duben +2
We assess the value of machine learning as an accelerator for the parameterisation schemes of operational weather forecasting systems, specifically the parameterisation of non-orog…
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…
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…
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…