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Advanced Research Computing (ARC)

Our goal is to provide high-performance computing (HPC) capabilities and expertise to scientists for the acceleration and expansion of scientific discovery. 

Hovenweep High Performance Computing System
USGS Hovenweep HPC System

The Advanced Research Computing (ARC) group is part of the Core Science Systems (CSS) mission area under the Science Analytics and Synthesis (SAS) program.

We provide access to high-performance computing resources as well as training and consultation for effective use of these powerful systems.

For more information or help getting started, contact us at



USGS New Supercomputer Helps Scientists Wrangle Data

USGS New Supercomputer Helps Scientists Wrangle Data

Using Artificial Intelligence to Help Protect Trout and Drinking Water Supplies

Using Artificial Intelligence to Help Protect Trout and Drinking Water Supplies


CONUS404: The NCAR-USGS 4-km long-term regional hydroclimate reanalysis over the CONUS

A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The p

R. M. Rasmussen, F. Chen, C. H. Liu, K. Ikeda, A. Prein, J. Kim, T. Schneider, A. Dai, D. Gochis, A. Dugger, Y. Zhang, A. Jaye, J. Dudhia, C. He, M. Harrold, L. Xue, S. Chen, A. Newman, E. Dougherty, R. Abolafia-Rozenzweig, N. Lybarger, Roland J. Viger, David P. Lesmes, Katherine Skalak, John Brakebill, Donald Walter Cline, Krista A. Dunne, K. Rasmussen, G. Miguez-Macho

Evaluating deep learning architecture and data assimilation for improving water temperature forecasts at unmonitored locations

Deep learning (DL) models are increasingly used to forecast water quality variables for use in decision making. Ingesting recent observations of the forecasted variable has been shown to greatly increase model performance at monitored locations; however, observations are not collected at all locations, and methods are not yet well developed for DL models for optimally ingesting recent observations
Jacob Aaron Zwart, Jeremy Alejandro Diaz, Scott Douglas Hamshaw, Samantha K. Oliver, Jesse Cleveland Ross, Margaux Jeanne Sleckman, Alison P. Appling, Hayley Corson-Dosch, Xiaowei Jia, Jordan S Read, Jeffrey M Sadler, Theodore Paul Thompson, David Watkins, Elaheh (Ellie) White

Preliminary machine learning models of manganese and 1,4-dioxane in groundwater on Long Island, New York

Manganese and 1,4-dioxane in groundwater underlying Long Island, New York, were modeled with machine learning methods to demonstrate the use of these methods for mapping contaminants in groundwater in the Long Island aquifer system. XGBoost, a gradient boosted, ensemble tree method, was applied to data from 910 wells for manganese and 553 wells for 1,4-dioxane. Explanatory variables included soil
Leslie A. DeSimone