Dale Durran

· Professor of Atmospheric Sciences

University of Washington · Materials Science & Engineering

Active 1976–2026

h-index53

Citations12.0k

Papers22635 last 5y

Funding$4.1M

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

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About

Dale Durran is an Adjunct Professor of Atmospheric Sciences at the University of Washington, affiliated with the Department of Atmospheric Sciences. His fields of interest include atmospheric dynamics and predictability, machine learning, and numerical methods. He is involved in research related to atmospheric sciences, contributing to the understanding of atmospheric behavior and the development of computational techniques in the field.

Research topics

Political Science
Computer Science
Artificial Intelligence
Business
Law
Earth science
Environmental ethics
World Wide Web
Geology

Selected publications

Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models
2021 · 38 citations
Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Advances in Modeling Earth Systems (JAMES). ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction modelsAuthorsJonathan AWeyniDDale RichardDurraniDRichCaruanaNatha…
DOI
Earth Virtualization Engines (EVE)
Earth system science data · 2024-04-30 · 37 citations
articleOpen access
Abstract. To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Publisher OA PDF DOI
Advancing Parsimonious Deep Learning Weather Prediction Using the HEALPix Mesh
Journal of Advances in Modeling Earth Systems · 2024-08-01 · 20 citations
articleOpen access
Abstract We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state‐of‐the‐art (SOTA) machine learning (ML) weather forecast models, such as Pangu‐Weather and GraphCast, our DLWP‐HPX model uses coarser resolution and far fewer prognostic variables. Yet, at 1‐week lead times, its ski…
Publisher DOI
Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model
arXiv (Cornell University) · 2024-06-12 · 11 citations
preprintOpen access
Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the ocean on the atmosphere and coupled interactions in the ocean-atmosphere system. We present the Ocean-linked-atmosphere (Ola) model, a high-resolution (0.25°) Artificial Intelligence/ Machine Learning (AI/ML) coupled earth-system model which separately…
Publisher OA PDF DOI
Earth Virtualization Engines (EVE)
2023 · 9 citations
Abstract. To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Publisher DOI

Recent grants

Troposphere-Stratosphere Coupling Processes
NSF · $438k · 2002–2007
The Co-Evolution of Mesoscale Airflow over Mountains and the Larger Scale Flow
NSF · $562k · 2005–2009
Mesoscale Airflow over Mountains: Wave Drag and Orographic Precipitation
NSF · $714k · 2011–2016

Frequent coauthors

Russ S. Schumacher
Colorado State University
25 shared
Jeffrey D. Kelley
National Centre for Atmospheric Science
25 shared
David M. Schultz
University of Manchester
25 shared
Gerard H. Roe
University of Washington
17 shared
Jonathan A. Weyn
14 shared
Craig C. Epifanio
Texas A&M University
12 shared
Thomas P. Ackerman
University of North Carolina School of the Arts
11 shared
Rajul Pandya
11 shared

Education

Ph.D., Mathematics
University of Washington
1984
M.S., Mathematics
University of Washington
1980
B.S., Mathematics
University of California, Berkeley
1977

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