3 citations · 6 across the 4 of their papers we have counts for
5 papers
ClimateBench v2.0: Probabilistic Climate Model Benchmarking
Duncan Watson-Parris, Willa Tobin, Aytaç Paçal +32
We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional tempe…
Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
Ankur Mahesh, William D. Collins, Travis A. O'Brien +10
The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typic…
Constraining Atmospheric River Uncertainty Using Instantaneous Poleward Latent Heat Transport
Ankur Mahesh, William D. Collins, William R. Boos +2
Atmospheric rivers (ARs) are extreme weather events that play a crucial role in the global hydrological cycle. As a key mechanism of latent heat transport (LHT), they help maintain…
FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh +7
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed…
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction
Jared D. Willard, Peter Harrington, Shashank Subramanian +3
The rapid rise of deep learning (DL) in numerical weather prediction (NWP) has led to a proliferation of models which forecast atmospheric variables with comparable or superior ski…