Search NASA⌕ Search

SEARCH · Search NASA

Results for “Advanced Workflow”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems

PROTECT (Production and Reuse Of Thermally Efficient Ceramic TPS) is a NASA Early Career Initiative focused on developing the next generation of reusable ceramic thermal protection systems (TPS). This project addresses key challenges in TPS design, including temperature capability, thermal stability, and refurbishment time, by integrating novel material development with predictive modeling. Leveraging enhanced capabilities in NASA’s Porous Microstructure Analysis (PuMA) software, PROTECT introduces new modeling tools to predict the thermal and mechanical behavior of fibrous ceramic materials. These tools enable accurate prediction of performance metrics such as thermal conductivity and structural integrity, reducing reliance on costly physical testing. Preliminary advances in these areas will be presented. To support model validation, PROTECT is building a comprehensive database of raw material properties using advanced characterization techniques, including micro computed tomography (CT) scanning in collaboration with the University of Illinois Urbana-Champaign (UIUC). The presentation will detail the sampling workflows and analysis methods used to generate this detailed microstructural data and how it is used to develop improved models in PuMA. This multi-center collaboration, spanning NASA (JSC, ARC, KSC, GRC), Oak Ridge National Laboratory, UIUC, and SpaceX, is developing tailored TPS solutions for LEO, lunar, and Martian missions. By bridging heritage systems with the demands of modern spaceflight, PROTECT contributes to the advancement of reusable TPS technologies for future exploration missions.

Propulsion, Refractory, and Coating Materials↗
NASA NTRS2026

PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems

PROTECT (Production and Reuse Of Thermally Efficient Ceramic TPS) is a NASA Early Career Initiative focused on developing the next generation of reusable ceramic thermal protection systems (TPS). This project addresses key challenges in TPS design, including temperature capability, thermal stability, and refurbishment time, by integrating novel material development with predictive modeling. Leveraging enhanced capabilities in NASA’s Porous Microstructure Analysis (PuMA) software, PROTECT introduces new modeling tools to predict the thermal and mechanical behavior of fibrous ceramic materials. These tools enable accurate prediction of performance metrics such as thermal conductivity and structural integrity, reducing reliance on costly physical testing. Preliminary advances in these areas will be presented. To support model validation, PROTECT is building a comprehensive database of raw material properties using advanced characterization techniques, including micro computed tomography (CT) scanning in collaboration with the University of Illinois Urbana-Champaign (UIUC). The presentation will detail the sampling workflows and analysis methods used to generate this detailed microstructural data and how it is used to develop improved models in PuMA. This multi-center collaboration, spanning NASA (JSC, ARC, KSC, GRC), Oak Ridge National Laboratory, UIUC, and SpaceX, is developing tailored TPS solutions for LEO, lunar, and Martian missions. By bridging heritage systems with the demands of modern spaceflight, PROTECT contributes to the advancement of reusable TPS technologies for future exploration missions.

Propulsion, Refractory, and Coating Materials↗
NASA NTRSDate not supplied

Lunar Gateway Charging and Effects on Low-Energy Electron Measurements

The Electron Electrostatic Analyzer (EEA) is part of the HERMES instrument suite on Lunar Gateway and is designed to measure electrons in the energy range from 1 eV to 18 keV. Electrons in this range can be deflected or blocked from entering a detector by electric fields from a charged spacecraft platform, so many heliophysics missions (MMS, Cluster, etc.) use active potential control to minimize stray electric fields which would corrupt low-energy plasma measurements. Gateway, however, does not have such capabilities so the surface charging effects will influence the electron data. Further, some elements of Gateway partially block the instrument’s field of view to space. The effects of the Gateway platform on the low-energy electron population must be understood to maximize the accuracy of the EEA data. To investigate the electrostatic environment near Gateway and its effects on the EEA measurements, we use Nascap-2K to model the expected charging of Gateway under representative solar wind, magnetosheathic, and magnetospheric conditions. Next, we use the potential and electric field outputs from Nascap-2K along with in-house particle tracing codes to investigate how low-energy electrons are perturbed, blocked, or deflected by the spacecraft. We discuss how these perturbations impact the plasma moments computed from the EEA data and possible approaches for correcting the charging effects including application of advanced methods such as neural networks. In addition to electrons from the space environment being influenced by the electrostatic environment around Gateway, photoelectrons generated on Gateway surfaces will also be measured by EEA. We are investigating how knowledge of the potentials of various Gateway surfaces can be extracted from these measured photoelectrons using advanced numerical modeling and analysis. Such information would be valuable for validating charging models and planning mission operations around the platform. Finally, we discuss how the specific workflow developed for Gateway can be extended to a generalized process that is applicable to other future missions. As the space industry prepares for human exploration beyond low-Earth orbit, the need to obtain accurate space weather data from rideshare science payloads is critical. Our process for removing platform effects from the science data serves as a pathfinder for leveraging advanced simulations to maximize science returns and ensure the safety and success of future crewed missions.

Miles Bengtson↗
NASA NTRS2007

Sensor Webs with a Service-Oriented Architecture for On-demand Science Products

This paper describes the work being managed by the NASA Goddard Space Flight Center (GSFC) Information System Division (ISD) under a NASA Earth Science Technology Ofice (ESTO) Advanced Information System Technology (AIST) grant to develop a modular sensor web architecture which enables discovery of sensors and workflows that can create customized science via a high-level service-oriented architecture based on Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) web service standards. These capabilities serve as a prototype to a user-centric architecture for Global Earth Observing System of Systems (GEOSS). This work builds and extends previous sensor web efforts conducted at NASA/GSFC using the Earth Observing 1 (EO-1) satellite and other low-earth orbiting satellites.

Mandl, Daniel↗
NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the xEVAS and HLS contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. We describe the current iterations of a suite of tools to support EVA procedure authoring and execution, for both ISS and Artemis missions, as well as a software solution to establish and interact with mission context and data products. These tools have been developed iteratively and continue to be tested in several NASA facilities such as the Neutral Buoyancy Lab (NBL), Artemis field testing, and in present-day International Space Station (ISS) operations on orbit. Our solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support future human spaceflight missions.

EVA Mission System Software↗
NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the Exploration Extravehicular Activity Services (xEVAS) and Human Landing System (HLS) contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the flight operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. This paper describes the current iterations of a suite of tools to support EVA procedure authoring and execution, and mission context creation for both International Space Station (ISS) and Artemis missions. These tools have been developed iteratively and continue to be used in present-day ISS operations on orbit and in several NASA facilities such as the Neutral Buoyancy Lab (NBL) and Artemis field testing. These solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support both current and future human spaceflight missions.

Matthew J. Miller↗
NASA NTRSDate not supplied

Supporting Exploration Missions by Enabling Exploration Mission System Software

Future exploration missions will consist of a multitude of data sources, systems, and operators collaborating to complete mission objectives. Presently, NASA is instantiating the contractual mechanisms, such as the Exploration Extravehicular Activity Services (xEVAS) and Human Landing System (HLS) contracts, to produce these mission assets. Architectural planning is also underway to establish the networking protocols and infrastructure to digitally create and connect mission elements, such as LunaNET. However, without new horizontally integrated data systems, these advancements will be limited in their ability to get mission data appropriately integrated into the plan, train, fly, explore workflow of the flight operations workforce. Here we describe several mission system software development efforts underway that are designed to support human spaceflight missions. This paper describes the current iterations of a suite of tools to support EVA procedure authoring and execution, and mission context creation for both International Space Station (ISS) and Artemis missions. These tools have been developed iteratively and continue to be used in present-day ISS operations on orbit and in several NASA facilities such as the Neutral Buoyancy Lab (NBL) and Artemis field testing. These solutions demonstrate how software development can be aligned with ongoing operations development activities to discover the features that best support both current and future human spaceflight missions.

Matthew Miller↗
NASA NTRSDate not supplied

AI Curation Methods for NASA Scientific Data

The NASA Open Science Data Repository (OSDR) serves as a central hub for sharing and accessing NASA's vast collection of scientific data, supporting researchers across diverse fields. To enhance the efficiency, accuracy, and accessibility of this data, we are leveraging advanced artificial intelligence (AI) techniques as part of the AI for Curation project. By integrating large language models (LLMs) into our data curation workflow, we aim to streamline the entire process—from data submission to user interaction. This initiative focuses on improving key areas, including data ingestion, curation, and user engagement with curated datasets, impacting multiple domains and a wide user base. First, we are developing tools that can automatically parse data in various formats, using LLMs to convert unstructured data into structured, standardized formats. This reduces the manual effort required for curation, allowing curators to focus on more critical scientific analyses. Additionally, AI and machine learning (ML) models are being implemented to automate data validation and verification, ensuring the highest standards of data quality and reliability. Finally, we are creating a conversational AI agent to interact with the curated scientific studies in OSDR, helping users easily navigate the repository and access relevant data. By enhancing data discoverability and accessibility, these advancements will foster new research opportunities and promote the principles of open science.

Walter Alvarado↗
NASA NTRS2010

SensorWeb 3G: Extending On-Orbit Sensor Capabilities to Enable Near Realtime User Configurability

This research effort prototypes an implementation of a standard interface, Web Coverage Processing Service (WCPS), which is an Open Geospatial Consortium(OGC) standard, to enable users to define, test, upload and execute algorithms for on-orbit sensor systems. The user is able to customize on-orbit data products that result from raw data streaming from an instrument. This extends the SensorWeb 2.0 concept that was developed under a previous Advanced Information System Technology (AIST) effort in which web services wrap sensors and a standardized Extensible Markup Language (XML) based scripting workflow language orchestrates processing steps across multiple domains. SensorWeb 3G extends the concept by providing the user controls into the flight software modules associated with on-orbit sensor and thus provides a degree of flexibility which does not presently exist. The successful demonstrations to date will be presented, which includes a realistic HyspIRI decadal mission testbed. Furthermore, benchmarks that were run will also be presented along with future demonstration and benchmark tests planned. Finally, we conclude with implications for the future and how this concept dovetails into efforts to develop "cloud computing" methods and standards.

Mandl, Daniel↗
NASA NTRS2021

Lunar Terrain Coverage Analysis Data Delivery Workflow

In this work, we are developing a lunar terrain database to enable fast rendering of sun illumination and earth visibility for a proposed coverage analysis tool. This development will advance lunar mission design and formulation for current and future communications architectures, and will aid in lunar surface mission planning and communications/navigation operations. Our effort can be described in three steps: (1) we parallelize a brute force algorithm, which computes elevation masks from laser altimetry data acquired by the Lunar Reconnaissance Orbiter’s (LRO) Lunar Orbiter Laser Altimeter (LOLA); (2) we investigate parallel I/O methods to store terrain mask information from step (1) into a parallel file system; and (3) we finally deliver data to the terrain coverage analysis tool.

Michels, Dominik↗
NASA NTRSDate not supplied

Enhanced Simulation Techniques in Predicting Sonic Boom Loudness Using CFD

This paper outlines advancements in predicting sonic boom loudness within the Launch, Ascent, and Vehicle Aerodynamics (LAVA) computational framework. Traditionally, a two step process consisting of a steady state computational fluid dynamics problem for near-field analysis and a far-field propagation solver for calculation of loudness metrics has been used. Improvements to this process made in this work include utilizing a high-order space marching method for mid-field computations, developing a novel output-based mesh adaptation method targeting error in near-field pressure sig-natures, and developing a robust scripting system using curvilinear grids to increase robustness and simplify the process of running large databases of simulation cases. These advancements are detailed and applied to the simulation of the X-59, presenting comparative cost and timing analyses between the prior two step workflow and the current three step procedure. We achieve increased accuracy and robustness for loudness predictions with at least a50%computational cost reduction.

CST↗
NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗
NASA NTRSDate not supplied

Advancing Wildfire Monitoring with TEMPO and ML tools: Hourly Smoke and Fire‑Front Mapping and Near‑Surface NO₂ Predictions

Wildfires impose substantial impacts on communities and regions downwind of wildfire smoke. We present a TEMPO‑enabled workflow that generates value‑added Level‑3 smoke‑plume masks and fire‑front maps for large wildfires, such as 2024 Park Fire, using the self‑supervised deep learning system SIT‑FUSE, along with near‑surface NO₂ predictions produced by a foundation model (Microsoft Aurora). We conclude by outlining a roadmap for expanding these capabilities to additional Western U.S. wildfire events and for delivering actionable tools to stakeholders. This open-source, reproducible workflow provides a scalable framework for cross-agency wildfire monitoring to overcome traditional limitations in smoke-cloud discrimination and air-quality forecasting by incorporating TEMPO data and beyond.

Xiaohua Pan↗
NASA NTRS2024

The Knowledge-based Digital Platform Concept for Advanced Air Mobility Research and Development

National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.

ATM↗
NASA NTRS2018

The InSAR Scientific Computing Environment 3.0: A Flexible Framework for NISAR Operational and User-Led Science Processing

The InSAR Scientific Computing Environment (ISCE) was first developed under the NASA Advanced Information Systems Technology as a flexible, extensible object-oriented framework for Interferometric Synthetic Aperture Radar (InSAR) processing. The ISCE framework uses Python 3 at the workflow level, controlling modules of compiled code for functional processing, and managing inputs, outputs, and other flow control services. The currently released version, called ISCE 2.1, is distributed to the research community through the Western North America InSAR Consortium under a research license. The ISCE team is working on the next generation of the code in order to prepare for the NASAISRO SAR (NISAR) mission operational processing. Innovations in this code include augmentation or conversion of the custom Python framework elements in ISCE with the Pyre framework, new workflows for interferometric and polarimetric stack processing, a more intuitive and graphically based user interface, and flow control for hybrid computing environments including CPU/GPU clusters, logging and error tracking facilities, and new more efficient computational modules that exploit graphical processor units (GPUs) when available. The ISCE 3.0 framework is designed to work in an operational environment as well as on a single user’s laptop or compute cluster, with services to discover capabilities and scale computations accordingly.

Buckley, Sean M.↗
NASA NTRSDate not supplied

NASA’s Moon Trek Portal: New Capabilities Supporting Mission Planning and Engagement

Introduction: NASA’s Moon Trek (https://trek.nasa.gov/moon/) is one of a growing number of interactive, browser-based, online portals for planetary data visualization and analysis produced by NASA’s Solar System Treks Project (SSTP). Moon Trek continues to be enhanced with new data and new capabilities enabling it to facilitate the planning and conducting of upcoming lunar missions by NASA, its commercial partners, and its international partners, as well as advancing its role as a valuable outreach tool. A Comprehensive Online Web Portal: Developed at NASA’s Jet Propulsion Laboratory (JPL) and managed as a project of NASA’s Solar System Exploration Research Virtual Institute (SSERVI) at NASA Ames Research Center, Moon Trek is a browser-based web portal. The portal provides easy-to-use tools for browsing, data layering, data product blending, and feature search among thousands of data products covering topography, mineralogy, elemental abundance, geology, and much more. Visualizations are provided in var-ious map projections, interactive 3D viewing, and in virtual reality. Using an in-house stereo workflow, SSTP is able to produce new NAC-based high-resolution mosaics and DEMs. Diverse Applications for Lunar Exploration: Baseline analytic tools available to all users include dis-tance measurement, elevation profiling, sun angle calculation, and 3D print file generation. More advanced account-level tools allow users to perform more computationally intensive analyses. These include ray-traced lighting analysis for user-specified areas over user-specified time/date ranges and time intervals, electro-static surface potential analysis, subsetting of large data products, slope analysis, and Lunar Laser Ranging geometry calculation. Artificial intelligence (AI) and ma-chine learning (ML) based tools have been implemented for crater detection and hazard analysis, boulder detection and hazard analysis, and rockfall detection. New Tools Facilitating Exploration: Additional, new tools have recently been added and others are in development, offering even greater functionality in con-ducting analyses of potential landing sites and areas of surface operations. The new Line-of-Sight tool facilitates communications planning between locations on the lunar surface, between any given site on the lunar surface and a specified ground station on the Earth, and between a site on the lunar surface and a relay asset in lunar orbit, all taking into account local lunar topography. The new Data Plotter tool provides both tabular and graphical representations of pixel values along a user specified path for a growing number of data products. The new NAC Finder tool will identify and pro-vide access to NAC images that intersect a user-defined path or bounded area. The SSTP development team is looking to leverage the capabilities of its existing AI and ML crater, boulder, and rockfall detection and analysis tools, and extend that technology to a generalized feature detector that can be trained on instances of specific types of landforms and then search the lunar surface for more examples of such features. New traverse planning tools are being developed with use cases in generalized concept studies and specific mission planning in mind. These will facilitate finding optimal traverse paths based on constraints such as slope, lighting, hazard avoidance, and communications. These will be complemented by new traverse visualization capabilities. Users will be able to interactively ride along with a rover, examining 3D views of the terrain while adjusting camera height and viewing angle along with selecting different data layer overlays to drape across the terrain. Engaging the Public: The capabilities being developed for mission planning are being leveraged to further enhance Moon Trek’s proven utility as a valuable public outreach resource. This includes providing multiple lev-els of engagement with different points of entry. At its simplest level, promoting understanding through visualization, media and the public will be able to easily visualize and conduct their own exploration of lunar sites targeted by NASA and its partners. For a more in-depth experience, we are working with our stakeholders to promote understanding through interaction by extend-ing our current landing site and traverse analysis capabilities, making simplified access to these tools available to those who want to explore more deeply key factors in planning a mission through interactive and possibly even gamified experiences. The highest degree of public outreach, focusing on engagement through scientific participation, could be achieved through our work with missions and the NASA Office of the Chief Scientist on Moon Trek’s extension as a tool with specialized capabilities for facilitating citizen science. In such scenarios, participants become members of extended mis-sion science teams, using dedicated and integrated interfaces to analyze mission data to help answer questions key to lunar science and exploration. We are work-ing with NASA’s Office of Communications, museums, planetariums, and the media to help them easily integrate accurate, detailed visualizations of NASA’s lunar destinations and exploration into their content/productions and to engage diverse audiences in diverse venues.

Moon Trek↗
NASA NTRS2020

Sketch-to-Solution: A Case Study in RCS Aerodynamic Interaction

Thanks to recent advances in the fields of anisotropic grid adaptation, error estimation, and geometry modeling, a sketch-to-solution work flow is now possible for viscous computational fluid dynamic (CFD) simulations. With this workflow, a CFD application engineer provides geometry, boundary conditions, and flow parameters; and the sketch-to-solution process yields a CFD simulation through automatic, error-based, grid adaptation. To explore the benefits of this nascent capability, a conventional manual grid generation work flow is compared to this new automatic grid generation work flow for a given engineering question: What are the aerodynamic interactions caused by the reaction control system (RCS) on an entry vehicle? This case study indicates that while the automatic grid generation sketch-to- solution process is not yet mature, it is preferred over a manual grid generation work flow because it greatly reduces manual labor, eliminates many opportunities for human error, and provides grid sensitivity information.

Bill Kleb↗
NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗