3 citations · 4 across the 7 of their papers we have counts for
9 papers
Bolzano: From Expert-Guided Proof Search to Automated Open-Problem Solving
Adrián Zámečník, Matěj Kripner, Martin Koutecký +5
Large language models are increasingly contributing to mathematical research, where progress often depends on efficient proof search, incremental improvements and careful verificat…
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…
Controllable Probabilistic Forecasting with Stochastic Decomposition Layers
John S. Schreck, William E. Chapman, Charlie Becker +6
AI weather prediction ensembles with latent noise injection and optimized with the continuous ranked probability score (CRPS) have produced both accurate and well-calibrated predic…
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…
Community Research Earth Digital Intelligence Twin (CREDIT)
John Schreck, Yingkai Sha, William Chapman +7
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional…