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NASA NTRS2018

How NASA is Building a Petabyte Scale Geospatial Archive in the Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) is working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This free and open source system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk outlines the motivation for this work, present the achievements and hurdles of the past 18 months and charts a course for the future expansion of Cumulus. We explore not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud. The NASA EOSDIS archive is currently at nearly 30 PBs and will grow to over 300PBs in the coming years. We've presented progress on this effort at AWS re:Invent and the American Geophysical Union (AGU) Fall Meeting in 2017 and hope to have the opportunity to share with FOSS4G attendees information on the availability of the open sourced software and how NASA intends on making its Earth Observing Geospatial data available for free to the public in the cloud.

NASA Archive↗

NASA NTRS2017

NASA's EOSDIS Cumulus: Ingesting, Archiving, Managing, and Distributing Earth Science Data from the Commercial Cloud

NASA's Earth Observing System Data and Information System (EOSDIS) has been working towards a vision of a cloud-based, highly-flexible, ingest, archive, management, and distribution system for its ever-growing and evolving data holdings. This system, Cumulus, is emerging from its prototyping stages and is poised to make a huge impact on how NASA manages and disseminates its Earth science data. This talk will outline the motivation for this work, present the achievements and hurdles of the past 18 months and will chart a course for the future expansion of the Cumulus expansion. We will explore on not just the technical, but also the socio-technical challenges that we face in evolving a system of this magnitude into the cloud and how we are rising to meet those challenges through open collaboration and intentional stakeholder engagement.

Earth Scienc↗

NASA NTRSDate not supplied

Advancing Open Science Through Innovative Data System Solutions: The Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)'s Data Ecosystem

Collaborative open science practices are changing the way research is conducted. These changes affect how scientists work together on data, code and information. Data systems enhance open science by offering forward thinking technological solutions, such as providing data and computation on the cloud, to enable collaboration, sharing and analysis. In this paper, we present our vision for a conceptual data system on the cloud that enables open science. We also present our work on the Multi-Mission Algorithm and Analysis Platform (MAAP)which has served as a pathfinder data system for this conceptual approach.

Kaylin Bugbee↗

NASA NTRS2020

A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover’s stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides roverterrain settling, realistic slip modelling, and camera rendering capability based on the rover’s NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statisticallymeaningful way. This paper provides an overview of the ROSbased simulator used for testing the M2020 ENav algorithms.

Toupet, Olivier↗

NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗

NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided many statistics on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus was segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution possible for capturing cloud initiation and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool, strategies to identify development stages from cloud tracks, and preliminary results that document cumulus lifecycle properties from satellite. The application of AHI 0.5-km reflectance has both strengths and limitations when attempting to track lifecycles of the smallest resolvable clouds. We show that by aggregating cloud tracks from a few case studies of CAMP2EX, we can discern differences in cloud lifetime and development according to ensembles selected from areas of interest. The results demonstrate an ability to quantify lifetimes and assess rates of change in cloud characteristics that are likely controlled by the surrounding environment and meteorology.

Cloud Tracking↗

NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗

NASA NTRSDate not supplied

Automated Tracking of Shallow Maritime Clouds on Geostationary Imagery to Extract Lifecycle Characteristics

Shallow moist convection is ubiquitous throughout the tropics and represents a key player in boundary layer processes. Satellites have provided valuable insight on shallow clouds, such as size, structure, and geographical coverage, from static views of recurring cloud fields. But determining why certain cloud features appear and persist for different periods requires a time-evolving view of their behaviors. Geostationary satellites provide a unique opportunity to follow the time evolution of individual convective features, given their enhanced spatial and temporal sampling. A cloud-tracking tool was developed to identify properties of cloud lifecycle from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2EX) field campaign of 2019. The mission conducted intensive sampling of shallow cumulus in the West Pacific Ocean, in tandem with Rapid Scan imagery from the Advanced Himawari Imager (AHI) on the Japan Meteorological Agency’s (JMA) Himawari-8 satellite. Shallow cumulus were segmented according to thresholds in 0.5-km visible reflectance and with blurring techniques. Despite being limited to daytime hours, the segmentations yielded the best resolution available for capturing cloud initiation, growth, and decay. The tracking procedure is based on a computer vision package that includes Kalman filters for motion prediction, object overlap search, and the Hungarian (or Kuhn-Munkres) matching algorithm for track designation. AHI radiances available within the tracked cloud boundaries are assembled to form individual spectral histories. The resulting catalog provides thousands of cloud histories for domains measuring only a few degrees in latitude and longitude. We present an overview of the cloud-tracking tool and its results for cloud fields sampled throughout CAMP2EX by the airborne P-3. Cloud tracks were selected from about 10 flights to form ensembles, specific groups of tracks occurring in a region with airborne sampling. Cloud lifecycle properties, including duration and maximum area, are calculated for all ensemble members, and analyzed for cloud behavior and P-3 coincidences. By following this strategy, we quantitatively assess the degree of airborne sampling for specific cloud classes defined by the lifecycle calculations. We can summarize which cloud classes had more sampling, the stage of development during sampling, and general differences in character (e.g., isolated congestus vs. cold-pool producer).

Cloud Tracking↗

NASA NTRS1982

Evolution of an impact-generated dust cloud and its effects on the atmosphere

A simulation is carried out of the evolution of an optically thick dust cloud in the earth's atmosphere, and calculations are made of the effects that such a dust cloud would have on the amount of visible light reaching the surface and the temperature at the earth's surface. It is found that large quantities of dust remain in the atmosphere for periods of only three to six months. This duration is fixed by the physical processes of coagulation; these cause the rapid formation of micron-sized particles and sedimentation that quickly removes the particles from the atmosphere. The duration of the event is found to be nearly independent of the initial altitude, initial particle size, initial mass, atmospheric vertical diffusive mixing rate, and rainout rate. It depends to a slight extent on the particle density and the probability that colliding particles stick together to form a larger particle. In addition, the duration is limited by the rate at which the debris spreads from the initial impact site. A doubling code is used to calculate the visible radiative transfer in the dust clouds. It is found that light levels are too low for vision for one to six months and too low for photosynthesis for two months to one year.

Toon, O. B.↗

NASA NTRS2015

Automated Point Cloud Correspondence Detection for Underwater Mapping Using AUVs

An algorithm for automating correspondence detection between point clouds composed of multibeam sonar data is presented. This allows accurate initialization for point cloud alignment techniques even in cases where accurate inertial navigation is not available, such as iceberg profiling or vehicles with low-grade inertial navigation systems. Techniques from computer vision literature are used to extract, label, and match keypoints between "pseudo-images" generated from these point clouds. Image matches are refined using RANSAC and information about the vehicle trajectory. The resulting correspondences can be used to initialize an iterative closest point (ICP) registration algorithm to estimate accumulated navigation error and aid in the creation of accurate, self-consistent maps. The results presented use multibeam sonar data obtained from multiple overlapping passes of an underwater canyon in Monterey Bay, California. Using strict matching criteria, the method detects 23 between-swath correspondence events in a set of 155 pseudo-images with zero false positives. Using less conservative matching criteria doubles the number of matches but introduces several false positive matches as well. Heuristics based on known vehicle trajectory information are used to eliminate these.

Sonar↗

NASA NTRS2024

3D Cloud Masking Across A Broad Swath Using Multi-Angle Polarimetry and Deep Learning

Understanding the 3-dimensional structure of clouds is of crucial importance to modeling our changing climate. Active sensors, such as radar and lidar, provide accurate vertical cloud profiles, but are mostly restricted to along-track sampling. Passive sensors can capture a wide swath, but struggle to see beneath cloud tops. In essence, both types of products are restricted to two dimensions: as a cross-section in the active case, and an image in the passive case. However, multi-angle sensor configurations contain implicit information about 3D structure, due to parallax and atmospheric path differences. Extracting that implicit information can be challenging, requiring computationally expensive radiative transfer techniques. Machine learning, as an alternative, may be able to capture some of the complexity of a full 3D radiative transfer solution with significantly less computational expense. In this work, we make three contributions towards understanding 3D cloud structure from multi-angle polarimetry. First, we introduce a large-scale, publicly available dataset that fuses existing cloud products into a format more amenable to machine learning. This dataset treats multi-angle polarimetry as an input, and radar-based vertical cloud profiles as an output. Second, we describe and evaluate strong baseline machine learning models based that predict these profiles from the passive imagery. Notably, these models are trained only on center-swath labels, but can predict cloud profiles over the entire passive imagery swath. Third, we leverage the information-theoretic nature of machine learning to draw conclusions about the relative utility of various sensor configurations, including spectral channels, viewing angles, and polarimetry. These findings have implications for Earth-observing missions such as NASA’s Plankton, Aerosol, Cloud-ocean Ecosystem (PACE) and Atmosphere Observing System (AOS) missions, as well as in informing future applications of computer vision to atmospheric remote sensing.

clouds↗

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↗

NASA NTRS2012

Remote Measurement of Pollution-A 40-Year Langley Retrospective: Aerosols and Clouds - Part 2

A workshop was convened in 1971 by the National Aeronautics and Space Administration (NASA) on the Remote Measurement of Pollution (RMOP), and the findings and recommendations of its participants are in a NASA Special Publication (NASA SP-285). The three primary workshop panels and their chairmen were focused on trace gas species (Will Kellogg), atmospheric particulates or aerosols (Verner Suomi), and water pollution (Gifford Ewing). Many of the workshop participants were specialists in the techniques that might be employed for regional to global-scale, remote measurements of the atmospheric parameters from Earth-orbiting satellites. In 2011 the author published a 40-year retrospective (or Part I) of the instrumental developments that were an outgrowth of the RMOP panel headed by Will Kellogg, i.e., on atmospheric temperature and gaseous species. The current report (or Part II) is an analogous retrospective of the vision of the panel led by Verner Suomi for the measurement of particulates (or aerosols) and clouds and for their effects on Earth s radiation budget. The class of measurement techniques includes laser radar or lidar, solar occultation, limb emission and scattering, nadir-viewing photometry or radiometry, and aerosol polarimetry. In addition, the retrospective refers to the scientific imperatives that led to those instrument developments of 1971-2010. Contributions of the atmospheric technologists at the Langley Research Center are emphasized, and their progress is placed in the context of the parallel and complementary work from within the larger atmospheric science community.

Remsberg, Ellis E.↗

NASA NTRSDate not supplied

Use NASA Earthdata in the Cloud

With the impending arrival of new, high-data-volume Earth observation missions, NASA’s ability to effectively ingest, process, and archive large amounts of data requires the most cost-effective, flexible, and scalable data-management architectures and technologies. To meet these demands, NASA's Earth Science Data Systems (ESDS) Program is implementing a strategic vision to develop and operate multiple components of NASA's Earth Observing System Data and Information System (EOSDIS) in a commercial cloud environment. As more earth observing data is moved into the cloud, NASA EOSDIS wants to share our reasoning for moving data into the commercial cloud, as well as demonstrate how end-users can take advantage of both data in the cloud and cloud-deployed services. This presentation explains the enterprise reasons for moving data into the cloud, specifically the scalability of cloud systems, the flexibility to pivot to the most optimal language/system/architecture, and the reduced redundancy of a common architecture. Then we primarily focus on the user-driven reasons for moving to the cloud. Equal access to high performance computing helps all parties, especially users that don't have access to their own on-premise computing infrastructure. Rapid deployment allows users to spin up their own systems without needing the extensive platform previously required for large scale development. Cost effectiveness allows users a wide breadth of computing options, from super computer ability to small scale development, which can help underserved communities. Finally we link to internal resources that users can utilize to begin their cloud based development journey without having to be an expert in the field.

Nicholas Doty↗

NASA NTRS2005

Radar Evaluation of Optical Cloud Constraints to Space Launch Operations

Weather constraints to launching space vehicles are designed to prevent loss of the vehicle or mission due to weather hazards (See, e.g., Ref 1). Constraints include Lightning Launch Commit Criteria (LLCC) designed to avoid natural and triggered lightning. The LLCC currently in use at most American launch sites including the Eastern Range and Kennedy Space Center require the Launch Weather Officer to determine the height of cloud bases and tops, the location of cloud edges, and cloud transparency. The preferred method of making these determinations is visual observation, but when that isn't possible due to darkness or obscured vision, it is permissible to use radar. This note examines the relationship between visual and radar observations in three ways: A theoretical consideration of the relationship between radar reflectivity and optical transparency. An observational study relating radar reflectivity to cloud edge determined from in-situ measurements of cloud particle concentrations that determine the visible cloud edge. An observational study relating standard radar products to anvil cloud transparency. It is shown that these three approaches yield results consistent with each other and with the radar threshold specified in Reference 2 for LLCC evaluation.

Merceret, Francis J.↗

NASA NTRS1987

Eyeglass Filters

Biomedical Optical Company of America's suntiger lenses eliminate more than 99% of harmful light wavelengths. NASA derived lenses make scenes more vivid in color and also increase the wearer's visual acuity. Distant objects, even on hazy days, appear crisp and clear; mountains seem closer, glare is greatly reduced, clouds stand out. Daytime use protects the retina from bleaching in bright light, thus improving night vision. Filtering helps prevent a variety of eye disorders, in particular cataracts and age related macular degeneration.

Source record↗

NASA NTRSDate not supplied

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗

NASA NTRS2014

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters.

CyberFlood↗