Fetching the paper…
Reading the bibliography…
The state of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs.
Statistical downscaling of general circulation model output: A comparison of methods
Robert L Wilby, TML Wigley, D Conway, PD Jones, BC Hewitson, J Main, and DS Wilks · 1998
Earlier work this paper cites.
Turbulent flows
Stephen B Pope · 2000
Earlier work this paper cites.
Using bayesian model averaging to calibrate forecast ensembles
Adrian E Raftery, Tilmann Gneiting, Fadoua Balabdaoui, and Michael Polakowski · 2005
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
Earlier work this paper cites.
Usability of best track data in climate statistics in the western north pacific
Monika Barcikowska, Frauke Feser, and Hans Von Storch · 2012
Earlier work this paper cites.
Why should ensemble spread match the RMSE of the ensemble mean?
V Fortin, M Abaza, F Anctil, and R Turcotte · 2014
Earlier work this paper cites.
Ncep gfs 0.25 degree global forecast grids historical archive, 2015
National Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Department of Commerce · 2015
Earlier work this paper cites.
Large-eddy simulation in an anelastic framework with closed water and entropy balances
Kyle G Pressel, Colleen M Kaul, Tapio Schneider, Zhihong Tan, and Siddhartha Mishra · 2015
Earlier work this paper cites.
Upscale error growth in a high-resolution simulation of a summertime weather event over europe
Tobias Selz and George C Craig · 2015
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
On convergence and stability of gans
Naveen Kodali, Jacob Abernethy, James Hays, and Zsolt Kira · 2017
Earlier work this paper cites.
The weather research and forecasting model: Overview, system efforts, and future directions
Jordan G Powers, Joseph B Klemp, William C Skamarock, Christopher A Davis, Jimy Dudhia, David O Gill, Janice L Coen, David J Gochis, Ravan Ahmadov, Steven E Peckham, et al · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deepdownscale: a deep learning strategy for high-resolution weather forecast
Eduardo R. Rodrigues, Igor Oliveira, Renato L. F. Cunha, and Marco A. S. Netto · 2018
Earlier work this paper cites.
Introduction to probability
Joseph K Blitzstein and Jessica Hwang · 2019
Earlier work this paper cites.
Regional data assimilation with the ncmrwf unified model (ncum): impact of doppler weather radar radial wind
Devajyoti Dutta, Ashish Routray, D Preveen Kumar, and John P George · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Dyamond: the dynamics of the atmospheric general circulation modeled on non-hydrostatic domains
Bjorn Stevens, Masaki Satoh, Ludovic Auger, Joachim Biercamp, Christopher S Bretherton, Xi Chen, Peter Düben, Falko Judt, Marat Khairoutdinov, Daniel Klocke, et al · 2019
Earlier work this paper cites.
Configuration and intercomparison of deep learning neural models for statistical downscaling
Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez · 2020
Earlier work this paper cites.
End-to-end object detection with transformers
Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko · 2020
Earlier work this paper cites.
Improving afternoon thunderstorm prediction over taiwan through 3dvar-based radar and surface data assimilation
I-Han Chen, Jing-Shan Hong, Ya-Ting Tsai, and Chin-Tzu Fong · 2020
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
Earlier work this paper cites.
Radar super resolution using a deep convolutional neural network
Andrew Geiss and Joseph C Hardin · 2020
Earlier work this paper cites.
The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information
William J Gutowski, Paul Aaron Ullrich, Alex Hall, L Ruby Leung, Travis Allen O’Brien, Christina M Patricola, RW Arritt, MS Bukovsky, Katherine V Calvin, Zhe Feng, et al · 2020
Earlier work this paper cites.
The era5 global reanalysis
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network
Jussi Leinonen, Daniele Nerini, and Alexis Berne · 2020
Cited alongside, same era.
A climate downscaling deep learning model considering the multiscale spatial correlations and chaos of meteorological events
Bin Mu, Bo Qin, Shijin Yuan, and Xiaoyun Qin · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
Cited alongside, same era.
Tru-net: a deep learning approach to high resolution prediction of rainfall
Rilwan A Adewoyin, Peter Dueben, Peter Watson, Yulan He, and Ritabrata Dutta · 2021
Cited alongside, same era.
Conditional image generation with score-based diffusion models
Unpaired downscaling of fluid flows with diffusion bridges
Tobias Bischoff and Katherine Deck · 2023
Closest in time.
Spherical fourier neural operators: Learning stable dynamics on the sphere
Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar · 2023
Closest in time.
Fuxi: A cascade machine learning forecasting system for 15-day global weather forecast
Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Yuan Qi, and Hao Li · 2023
Closest in time.
Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah · 2023
Closest in time.
Enhancing spatial variability representation of radar nowcasting with generative adversarial networks
Aofan Gong, Ruidong Li, Baoxiang Pan, Haonan Chen, Guangheng Ni, and Mingxuan Chen · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
G. Batzolis, J. Stanczuk, C.-B. Schönlieb, and C. Etmann · 2021
Cited alongside, same era.
How well is outer tropical cyclone size represented in the era5 reanalysis dataset?
Gu-Feng Bian, Gao-Zhen Nie, and Xin Qiu · 2021
Cited alongside, same era.
An operational multi-radar multi-sensor qpe system in taiwan
Pao-Liang Chang, Jian Zhang, Yu-Shuang Tang, Lin Tang, Pin-Fang Lin, Carrie Langston, Brian Kaney, Chia-Rong Chen, and Kenneth Howard · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
Cited alongside, same era.
Skilful precipitation nowcasting using deep generative models of radar
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, et al · 2021
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiefeng Song, Chaoyue Meng, and Stefano Ermon · 2021
Cited alongside, same era.
Diffusion models for high-resolution solar forecasts
Yusuke Hatanaka, Yannik Glaser, Geoff Galgon, Giuseppe Torri, and Peter Sadowski · 2023
Closest in time.
Icon-sapphire: simulating the components of the earth system and their interactions at kilometer and subkilometer scales
Cathy Hohenegger, Peter Korn, Leonidas Linardakis, René Redler, Reiner Schnur, Panagiotis Adamidis, Jiawei Bao, Swantje Bastin, Milad Behravesh, Martin Bergemann, et al · 2023
Closest in time.
simple diffusion: End-to-end diffusion for high resolution images
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans · 2023
Closest in time.
Learning skillful medium-range global weather forecasting
Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Ferran Alet, Suman Ravuri, Timo Ewalds, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Oriol Vinyals, Jacklynn Stott, Alexander Pritzel, Shakir Mohamed, and Peter Battaglia · 2023
Closest in time.
Jussi Leinonen, Ulrich Hamann, Daniele Nerini, Urs Germann, and Gabriele Franch · 2023
Closest in time.
Seeds: Emulation of weather forecast ensembles with diffusion models
Lizao Li, Rob Carver, Ignacio Lopez-Gomez, Fei Sha, and John Anderson · 2023
Closest in time.
On distillation of guided diffusion models
Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2023
Closest in time.
Forecasting tropical cyclones with cascaded diffusion models
Pritthijit Nath, Pancham Shukla, and César Quilodrán-Casas · 2023
Closest in time.
Comparison of a novel machine learning approach with dynamical downscaling for australian precipitation
Nidhi Nishant, Sanaa Hobeichi, Steven C Sherwood, Gab Abramowitz, Yawen Shao, Craig Bishop, and Andy J Pitman · 2023
Closest in time.
Gencast: Diffusion-based ensemble forecasting for medium-range weather
Ilan Price, Alvaro Sanchez-Gonzalez, Ferran Alet, Timo Ewalds, Andrew El-Kadi, Jacklynn Stott, Shakir Mohamed, Peter Battaglia, Remi Lam, and Matthew Willson · 2023
Closest in time.
Machine-learning-based downscaling of hourly era5-land air temperature over mountainous regions
Badr-eddine Sebbar, Saïd Khabba, Olivier Merlin, Vincent Simonneaux, Chouaib El Hachimi, Mohamed Hakim Kharrou, and Abdelghani Chehbouni · 2023
Closest in time.
Physics-informed deep learning framework to model intense precipitation events at super resolution
B Teufel, F Carmo, L Sushama, L Sun, MN Khaliq, S Bélair, A Shamseldin, D Nagesh Kumar, and J Vaze · 2023
Closest in time.
Deep learning for downscaling tropical cyclone rainfall to hazard-relevant spatial scales
Emily Vosper, Peter Watson, Lucy Harris, Andrew McRae, Raul Santos-Rodriguez, Laurence Aitchison, and Dann Mitchell · 2023
Closest in time.
Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2023
Closest in time.
Aurora: A foundation model of the atmosphere
Cristian Bodnar, Wessel P Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan Weyn, Haiyu Dong, Anna Vaughan, et al · 2024
Closest in time.
Guiding a diffusion model with a bad version of itself
Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine · 2024
Closest in time.
Aifs-ecmwf’s data-driven forecasting system
Simon Lang, Mihai Alexe, Matthew Chantry, Jesper Dramsch, Florian Pinault, Baudouin Raoult, Mariana CA Clare, Christian Lessig, Michael Maier-Gerber, Linus Magnusson, et al · 2024
Closest in time.
Enhancing regional climate downscaling through advances in machine learning
Neelesh Rampal, Sanaa Hobeichi, Peter B Gibson, Jorge Baño-Medina, Gab Abramowitz, Tom Beucler, Jose González-Abad, William Chapman, Paula Harder, and José Manuel Gutiérrez · 2024
Closest in time.
Diffobs: Generative diffusion for global forecasting of satellite observations
Jason Stock, Jaideep Pathak, Yair Cohen, Mike Pritchard, Piyush Garg, Dale Durran, Morteza Mardani, and Noah Brenowitz · 2024
Closest in time.