AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM

Caldwell, Peter; Golaz, Chris; Bogenschutz, Peter; Lier-Walqui, Marcus; Donahue, Aaron; Vogl, Chris; Rountree, Barry; Marathe, Aniruddha; Patki, Tapasya

doi:10.2172/1769663

DOE OSTI · 1769663

AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM

Caldwell, Peter·Golaz, Chris·Bogenschutz, Peter·Lier-Walqui, Marcus·Donahue, Aaron·Vogl, Chris·Rountree, Barry·Marathe, Aniruddha·Patki, Tapasya

View original source ↗Read source document ↗Export metadata ↓

Abstract

Focal Area: This idea is best aligned with predictive modeling. It targets Earth System Model improvement and uncertainty quantification

Explore related subjects

54 ENVIRONMENTAL SCIENCES 58 GEOSCIENCES 97 MATHEMATICS AND COMPUTING

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Caldwell, Peter, Golaz, Chris, Bogenschutz, Peter, Lier-Walqui, Marcus, Donahue, Aaron, Vogl, Chris, Rountree, Barry, Marathe, Aniruddha, Patki, Tapasya. 2021-04-15. AI-Assisted Parameter Tuning Will Speed Development and Clarify Uncertainty in E3SM. https://doi.org/10.2172/1769663

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗