Dale Durran  

 



  ![](/sites/default/files/person/Dale_headshot.jpeg)

  

 Durran has a 25% appointment as a Principal Research Scientist in Climate Modeling at NVIDIA and a 60% appointment as a Professor of Atmospheric Sciences at the University of Washington. At NVIDIA his research focus in on deep learning earth-system modeling for sub-seasonal and seasonal forecasting, forecast ensembles, and generative methods for fine-scale modeling of convective precipitation and other mesoscale fields.

At the University of Washington, Durran is a professor and past Chair of Department of Atmospheric Sciences. His UW research foci include atmospheric predictability, mountain meteorology, mesoscale meteorology, and numerical weather prediction. Most recently he has been exploring how deep learning can change our current paradigm for numerical weather prediction, sub-seasonal, and seasonal forecasting. He is a fellow of the American Meteorological Society (AMS) and a recipient of the AMS’s Jule Charney Award. He has written over 120 scientific publications, the graduate-level textbook “*Numerical methods for* *Fluid Dynamics with Applications to Geophysics”* and “perspective” articles about climate change for the *Washington Post*. His sculpture was included in the first ArtScience Virtual Exhibit exhibit of American Geophysical Union’s 2022 Fall Meeting.



   Research Area(s)

[Algorithms and Numerical Methods](/index.php/research-area/algorithms)

[Climate Simulation](/index.php/research-area/climate-simulation)

 

 

  

 Main Field of Interest

[Climate Simulation](/index.php/research-area/climate-simulation)

 

  

 Google Scholar

[https://scholar.google.com/citations?user=DxlLiUMAAAAJ&hl=en](https://scholar.google.com/citations?user=DxlLiUMAAAAJ&hl=en)

 

  

 

 

 



 ### Publications

 

### 2026 

[Learning Accurate Storm-Scale Evolution from Observations](/publication/2026-01_learning-accurate-storm-scale-evolution-observations)

[Jaideep Pathak](/person/jaideep-pathak), Mohammad Shoaib Abbas, Peter Harrington, [Zeyuan Hu](/person/zeyuan-hu), [Noah Brenowitz](/person/noah-brenowitz), Suman Ravuri, Alberto Carpentieri, Jussi Leinonen, Corey Adams, Oliver Hennigh, Nicholas Geneva, [Dale Durran](/person/dale-durran), [Mike Pritchard](/person/mike-pritchard)













[Demystifying Data-Driven Probabilistic Medium-Range Weather Forecasting](/publication/2026-01_demystifying-data-driven-probabilistic-medium-range-weather-forecasting)

[Jean Kossaifi](/person/jean-kossaifi), [Nikola Kovachki](/person/nikola-kovachki), [Morteza Mardani](/person/morteza-mardani), [Daniel Leibovici](/person/daniel-leibovici), Suman Ravuri, Ira Shokar, Edoardo Calvello, Mohammad Shoaib Abbas, Peter Harrington, Ashay Subramaniam, [Noah Brenowitz](/person/noah-brenowitz), [Boris Bonev](/person/boris-bonev), [Wonmin Byeon](/person/wonmin-byeon), [Karsten Kreis](/person/karsten-kreis), [Dale Durran](/person/dale-durran), [Arash Vahdat](/person/arash-vahdat), [Mike Pritchard](/person/mike-pritchard), [Jan Kautz](/person/jan-kautz)













### 2024 

[Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling](/publication/2024-08_kilometer-scale-convection-allowing-model-emulation-using-generative-diffusion)

[Jaideep Pathak](/person/jaideep-pathak), Yair Cohen, Piyush Garg, Peter Harrington, [Noah Brenowitz](/person/noah-brenowitz), [Dale Durran](/person/dale-durran), [Morteza Mardani](/person/morteza-mardani), [Arash Vahdat](/person/arash-vahdat), Shaoming Xu, Karthik Kashinath, [Mike Pritchard](/person/mike-pritchard)