Search NASA⌕ Search

DOE OSTI · 1571182

A Non-Hydrostatic Variable Resolution Atmospheric Model in ACME (Final Technical Report)

Abstract

The project goals are divided into five main tasks to be accomplished over three years: Task 1: Implement a nonhydrostatic atmospheric dynamical core in ACME (via HOMME) Task 2: Improve load balancing in variable resolution simulations by extending Zoltan. Task 3: Optimize implicit-explicit time integrators for overcoming the vertical acoustic wave CFL limitation Task 4: Validate the NH dynamical core with DCMIP and AMIP tests on uniform and variable resolution grids Task 5: Apply the NH dynamical core to study and parameterize turbulent mountain stress and orographic gravity wave drag with ultra-high variable resolution grids This report only concerns contributions from the UC Davis team, which includes Co-PI Paul Ullrich, GSR/Postdoc Jorge Guerra, GSR Alan Rhoades, GSR Shiheng Duan, and GSR Jennifer Piaseczny. From the above list, the UC Davis team is responsible for contributions to Tasks 1, 3, 4 and 5.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ullrich, Paul Aaron. 2019-11-26. A Non-Hydrostatic Variable Resolution Atmospheric Model in ACME (Final Technical Report). https://doi.org/10.2172/1571182

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

KEEP EXPLORING

Related reports

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↗

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↗

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↗