3 citations · 4 across the 7 of their papers we have counts for
6 papers · 1 filter
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…
Hard conservation correctors can hide a degrading model when training autoregressive emulators
William E. Chapman, John Schreck, Yingkai Sha
AI weather and climate emulators increasingly incorporate physical principles into their formulation. One approach is to apply hard correctors that modify network outputs so that g…
Conditional Tropical Cyclogenesis Rates via Rare-Event Sampling in a Neural Weather Emulator
John S. Schreck, William Chapman, Charlie Becker +1
We couple Forward Flux Sampling (FFS), a non-equilibrium rare-event technique from statistical mechanics, to a neural weather emulator (SDL-WXFormer, 1° grid spacing) to estimate c…
CAMulator: Fast Emulation of the Community Atmosphere Model
William E. Chapman, John S. Schreck, Yingkai Sha +5
We introduce CAMulator version 1, an auto-regressive machine-learned (ML) emulator of the Community Atmosphere Model version 6 (CAM6) that simulates the next atmospheric state give…
Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
Yingkai Sha, John S. Schreck, William Chapman +1
Artificial Intelligence (AI) weather prediction (AIWP) models often produce ``blurry'' precipitation forecasts. This study presents a novel solution to tackle this problem -- integ…
Improving AI weather prediction models using global mass and energy conservation schemes
Yingkai Sha, John S. Schreck, William Chapman +1
Artificial Intelligence (AI) weather prediction (AIWP) models are powerful tools for medium-range forecasts but often lack physical consistency, leading to outputs that violate con…