Publications
Showing 33 results for Author: Valentine G. Anantharaj
Jun, 2026
Journal
Effects of Atmosphere and Ocean Horizontal Model Resolution on Tropical Cyclone and Upper-Ocean Response Forecasts in Four Major Hurricanes
A coupled atmosphere-ocean model is necessary for tropical cyclone (TC) prediction to accurately characterize ocean feedback on atmospheric processes within the TC environment. Here, the ECMWF coupled global model is run at horizontal resolutions from 9 km to 1.4 km in the atmosphere, as well as 25 km and 8 km in the ocean, to identify how resolution impacts forecast accur…
Jun, 2026
Book Chapter
High-performance computing for geospatial intelligence
This chapter explores the role of high-performance computing (HPC) in advancing geospatial artificial intelligence (GeoAI) through the processing and analysis of a vast array of geospatial datasets, particularly remote sensing imagery. HPC plays a vital role in the synthesis of geospatial intelligence and reasoning enabled by deep learning. Modern HPC ecosystems are define…
May, 2026
Journal
Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials
This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readiness model that…
May, 2026
Conference Paper
Data Readiness for Scientific AI at Scale
This paper examines how Data Readiness for AI (DRAI) principles apply to leadership-scale scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, bio/health, and materials—to identify common preprocessing patterns and domain-specific constraints. We introduce a two-dimensional readines…
Mar, 2026
Conference Paper
Scalable Multi-Facility Workflows for Artificial Intelligence Applications in Climate Research
Earth observation satellites and earth system models are sources of vast, multi-modal datasets that are invaluable for advancing climate and environmental research. However, their scale and complexity pose significant challenges for processing and analysis. In this paper we discuss our experiences in developing and using a scientific research application using an automated…
Sep, 2025
Journal
yProv4ML: Effortless provenance tracking for machine learning systems
The rapid growth in interest in deep learning and foundation models (FMs) in particular, has attracted the attention of a diverse range of researchers thanks to their generalization ability. However, the advent of these techniques has also brought to light the lack of transparency and rigor in the way development is pursued. In particular, the inability to determine the nu…
Mar, 2025
Journal
Influence of Atmospheric Slant Path on Geostationary Hyperspectral Infrared Sounder Radiance Simulations
Accurately simulating a geostationary hyperspectral infrared sounder is critical for quantitative applications. Traditional radiation simulations of such instruments often overlook the influence of slant observation geometry by using vertical profile assumption, leading to inadequate simulation accuracy. By using global atmospheric profiles with 1 km spatial resolution, th…
Nov, 2024
ORNL Report
OLCF’s Advanced Computing Ecosystem (ACE): FY24 Efforts for the DOE Integrated Research Infrastructure (IRI) Program
This report highlights significant strides made by Oak Ridge National Laboratory’s Oak Ridge Leadership Computing Facility (OLCF) in advancing computational research and infrastructure. Through the Advanced Computing Ecosystem (ACE) strategic initiative, OLCF has been successfully integrated with DOE’s Integrated Research Infrastructure (IRI) program, establishing itself a…
Sep, 2024
Journal
Gravity Wave Momentum Fluxes from 1 km Global ECMWF Integrated Forecast System
Progress in understanding the impact of mesoscale variability, including gravity waves (GWs), on atmospheric circulation is often limited by the availability of global fine-resolution observations and simulated data. This study presents momentum fluxes due to atmospheric GWs extracted from four months of an experimental “nature run", integrated at a 1 km resolution (XNR1K)…