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Assessing the Needs of NASA's Near Real-Time Earth Observation Products

"The 2017-2027 Decadal Survey for Earth Science and Applications from Space stated that NASA's Earth Science with planned implementation of applications provides sustained earth observations for societal benefits [1]. The Decadal Survey indicated that data latency is invaluable for time-sensitive applications including disaster risk reduction, wildland fire carbon emissions quantification, real-time measurements of the state of the hydrologic systems and many more. Data latency refers to the time between earth observation and data products available to users. During the past 13 years, NASA's Land, Atmosphere Near Real-Time Capability for Earth Observing Systems (LANCE) continues to provide free access to earth observation products that are made available much quicker than routine processing allows. The latency of most LANCE data products is Near Real-time (NRT) which is defined as less than three hours from satellite observations [2]. LANCE is managed by the Earth Science Data and Information System (ESDIS) Project at NASA Goddard Space Flight Center [3], and a User Working Group (UWG) is responsible for providing guidance to LANCE. LANCE data are used by direct users and brokers who add value to the data [4]. NASA Earth Applied Sciences Program (ASP) is one of the primary users of LANCE, which collaborates with partner organizations and provides support to scientists to solve problems in applications of earth observations. ASP promotes the use of LANCE NRT data products to demonstrate applications in decision making, facilitates end-user feedback to the science team to improve data products, and provides information on future demands for research. LANCE supports applications that need a rapid response including detecting wildland fires and volcanic eruptions, tracking smoke, ash and dust plumes, monitoring air quality and tracking extreme weather events such as hurricanes, landslides, and floods. To gather feedback regarding the availability, accessibility and actionability of NASA's NRT data products for societal benefit, three surveys and a few discussions with experts involved in the topic within ASP were conducted from the perspective of users. Feedback has been collected from users who are interested in using low latency NASA data within application communities of agriculture, disasters, water resources, health and air quality, ecological conservation, wildland fires and capacity building. Analysis-ready NRT data products in a variety of formats have been mentioned many times in the collected feedback, especially for applied users with little to no experience using research-grade earth observation products. Users prefer to have products that can be easily integrated into their existing workflows and take their analysis to the data. HDF5 is a commonly used data format for research, but typically requires some conversion to a more friendly format for applications and regular use in decision-making. Users prefer the GeoTIFF data format that can be directly ingested into a GIS mapping software and platform for data analysis and visualization. For example, LANCE’s fire, flood, SO2 and Black Marble Nighttime Blue/Yellow Composite data products have been integrated into NASA Disasters Mapping Portal, which is an GIS-based open data portal, for users in the disaster management community. There are 291 LANCE NRT layers available through GIBS and Worldview, where users can download a snapshot in GeoTIFF format. Operational users expect data to be processed as close to the user as possible. The collected feedback indicates that LANCE fire products within 3 hours latency would meet the needs of the wildland fire community. The ideal latency for volcanic application is 10-15 minutes. Users in Volcanic Ash Advisory Centers (VAAC) reported that the first forecast volcanic product should be issued within 75 minutes from the volcano eruption [5]. Overall, for disaster applications, data latency within 3 hours is useful while latency greater than 12 hours is not timely enough for operational use. Capacity building and training are critical for users to be able to access, interpret and use data products and tools for their decision making, especially for applied users with limited experience using earth observation products. LANCE data products have been used in a number of capacity building projects domestically and internationally [6]. As LANCE continues to bring new products into the system, users request training to utilize LANCE new and upcoming data products and capabilities in their applications. Due to the limitation of bandwidth and downstream flow paths, users in some developing countries need tools to select and download data for a specific area of interest instead of bulk downloads. The collected feedback also shows the lack of available SAR satellite low latency data products. The advantages of SAR to monitor conditions and changes on the ground through darkness, clouds, volcanic ash, and other atmospheric conditions, are appealing to low latency users. For example, terabytes of low latency but cloudy optical images are not helpful in rapidly identifying the extent of flood or fire impacts. LANCE could be complemented with low latency measurements via the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission [7]. Requests for higher spatial resolution products are expressed. A user from the wildland fire management community reported that products with 30-m spatial resolution could be used to detect small fires. The 30-m Landsat OLI fire data is now part of NASA’s Fire Information for Resource Management System (FIRMS) US/Canada [8]. Within the open and free NASA resources, LANCE disseminates NRT data products in a manner that allows them to be accessible and understandable to both scientific and applied users. In many application areas, latency plays an important or even decisive role where low latency earth observations help people to observe areas of interest, detect and track changes in the environment and make timely decisions. NASA’s Earth Applied Sciences Program promotes the use of LANCE NRT products and builds a bridge between application users and research teams. The collected feedback indicates data latency within 3 hours is useful for most of the applications, and shows the needs of user-friendly, analysis-ready products, and requests training on LANCE’s new and upcoming data products. User feedback has been provided to LANCE UWG for guidance and recommendations, and for translating findings into something actionable.

Tian Yao↗

Process Improvement Through Tool Integration in Aero-Mechanical Design

Emerging capabilities in commercial design tools promise to significantly improve the multi-disciplinary and inter-disciplinary design and analysis coverage for aerospace mechanical engineers. This paper explores the analysis process for two example problems of a wing and flap mechanical drive system and an aircraft landing gear door panel. The examples begin with the design solid models and include various analysis disciplines such as structural stress and aerodynamic loads. Analytical methods include CFD, multi-body dynamics with flexible bodies and structural analysis. Elements of analysis data management, data visualization and collaboration are also included.

Integrated design and analysis↗

Affordability Engineering: Bridging the Gap Between Design and Cost

Affordability is a commonly used term that takes on numerous meanings depending on the context used. Within conceptual design of complex systems, the term generally implies comparisons between expected costs and expected resources. This characterization is largely correct, but does not convey the many nuances and considerations that are frequently misunderstood and underappreciated. In the most fundamental sense, affordability and cost directly relate to engineering and programmatic decisions made throughout development programs. Systems engineering texts point out that there is a temporal aspect to this relationship, for decisions made earlier in a program dictate design implications much more so than those made during latter phases. This paper explores affordability engineering and its many sub-disciplines by discussing how it can be considered an additional engineering discipline to be balanced throughout the systems engineering and systems analysis processes. Example methods of multidisciplinary design analysis with affordability as a key driver will be discussed, as will example methods of data visualization, probabilistic analysis, and other ways of relating design decisions to affordability results.

Reeves, J. D.↗

A General Framework for Progressive Data Compression and Retrieval

In scientific simulations, observations, and experiments, the transfer of data to and from disk and across networks has become a major bottleneck for data analysis and visualization. Compression techniques have been employed to tackle this challenge, but traditional lossy methods often demand conservative error tolerances to meet the numerical accuracy requirements of both anticipated and unknown data analysis tasks. Progressive data compression and retrieval has emerged as a promising solution, where each analysis task dictates its own accuracy needs. However, few analysis algorithms inherently support progressive data processing, and adapting compression techniques, file formats, client/server frameworks, and APIs to support progressivity can be challenging. Here, this paper presents a framework that enables progressive-precision data queries for any data compressor or numerical representation. Our strategy hinges on a multi-component representation that successively reduces the error between the original and compressed field, allowing each field in the progressive sequence to be expressed as a partial sum of components. We have implemented this approach with four established scientific data compressors and assessed its effectiveness using real-world data sets from the SDRBench collection. The results show that our framework competes in accuracy with the standalone compressors it is based upon. Additionally, (de)compression time is proportional to the number of components requested by the user. Finally, our framework allows for fully lossless compression using lossy compressors when a sufficient number of components are employed.

97 MATHEMATICS AND COMPUTING↗

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗

A browser-based tool for visualization and analysis of diffusion MRI data

Human neuroscience research faces several challenges with regards to reproducibility. While scientists are generally aware that data sharing is important, it is not always clear how to share data in a manner that allows other labs to understand and reproduce published findings. Here we report a new open source tool, AFQ-Browser, that builds an interactive website as a companion to a diffusion MRI study. Because AFQ-Browser is portable—it runs in any web-browser—it can facilitate transparency and data sharing. Moreover, by leveraging new web-visualization technologies to create linked views between different dimensions of the dataset (anatomy, diffusion metrics, subject metadata), AFQ-Browser facilitates exploratory data analysis, fueling new discoveries based on previously published datasets. In an era where Big Data is playing an increasingly prominent role in scientific discovery, so will browser-based tools for exploring high-dimensional datasets, communicating scientific discoveries, aggregating data across labs, and publishing data alongside manuscripts.

60 APPLIED LIFE SCIENCES↗

Connecting Users and Applications with Po.daac Hosted GHRSST Data

The 80+ GHRSST public datasets represent a rich resource for sea surface temperature research and applications given their time series length, resolution, spatial coverage, varying measurement types and processing levels, and availability in the full spectrum of PO.DAAC tools and services ecosystem. The PO.DAAC has created a publicly accessible recipe suite for the user community to perform straightforward yet powerful computations on GHRSST data using python recipes, Jupyter notebooks, R, Matlab, and the NCO programming language. These recipes include numerical computations for regional and global SST trends, anomaly derivations, EOF analysis, climate signal reproduction, and ocean phenology. For example, one recipe reproduces a famous SST based warming figure from the Fourth National Climate Assessment (USA) while another focuses on quantifying the regional changes in ocean SST phenology. Most are python-based while some contain hybrid calls and leverage the NCO programming interface too. All are available on the PO.DAAC user forum (https://podaac.jpl.nasa.gov/forum/) and/or via the open source NASA GitHub repository (https://github.com/nasa/podaac_tools_and_services). Several are available in the Jupyter notebook framework including podaacypy (https://github.com/nasa/podaacpy), a recipe for GHRSST granule metadata discovery and application, and more recently a Jupyter notebook developed to support data analysis and visualization of a cloud-based Zarr formatted Level 4 MUR dataset in the AWS Open Data Registry. Throughout the summer of 2020, the PO.DAAC intends to add and migrate more of its numerical recipes to the Jupyter notebook framework and publish them on its open source GitHub repository.

Gentemann, Chelle↗

Profiling of Histone Post-Translational Modifications in Mouse Brain with High-Resolution Top-Down Mass Spectrometry

Histones play central roles in most chromosomal functions and both their basic biology and roles in disease have been the subject of intense study. Since multiple PTMs along the entire protein sequence are potential regulators of histones, a top-down approach, where intact proteins are analyzed, is ultimately required for complete characterization of proteoforms. However, significant challenges remain for top-down histone analysis primarily because of deficiencies in separation/resolving power and effective identification algorithms. Here, we used state of the art mass spectrometry and a bioinformatics workflow for targeted data analysis and visualization. The workflow uses ProMex for intact mass deconvolution, MSPathFinder as search engine, and LcMsSpectator as a data visualization tool. ProMex sums across retention time to maximize sensitivity and accuracy for low abundance species in MS1deconvolution. MSPathFinder searches the MS2 data against protein sequence databases with user-defined modifications. LcMsSpectator presents the results from ProMex and MSPathFinder in a format that allows quick manual evaluation of critical attributes for high-confidence identifications. When complemented with the open-modification tool TopPIC, this workflow enabled identification of novel histone PTMs including tyrosine bromination on histone H4 and H2A, H3 glutathionylation, and mapping of conventional PTMs along the entire protein for many histone subunits.

Environmental Molecular Sciences Laboratory↗

Using Selection Pressure as an Asset to Develop Reusable, Adaptable Software Systems

The Goddard Earth Sciences Data and Information Services Center (GES DISC) at NASA has over the years developed and honed several reusable architectural components for supporting large-scale data centers with a large customer base. These include a processing system (S4PM) and an archive system (S4PA) based upon a workflow engine called the Simple Scalable Script based Science Processor (S4P) and an online data visualization and analysis system (Giovanni). These subsystems are currently reused internally in a variety of combinations to implement customized data management on behalf of instrument science teams and other science investigators. Some of these subsystems (S4P and S4PM) have also been reused by other data centers for operational science processing. Our experience has been that development and utilization of robust interoperable and reusable software systems can actually flourish in environments defined by heterogeneous commodity hardware systems the emphasis on value-added customer service and the continual goal for achieving higher cost efficiencies. The repeated internal reuse that is fostered by such an environment encourages and even forces changes to the software that make it more reusable and adaptable. Allowing and even encouraging such selective pressures to software development has been a key factor In the success of S4P and S4PM which are now available to the open source community under the NASA Open source Agreement

Berrick, Stephen↗

Compilation of Abstracts for SC12 Conference Proceedings

1 A Breakthrough in Rotorcraft Prediction Accuracy Using Detached Eddy Simulation; 2 Adjoint-Based Design for Complex Aerospace Configurations; 3 Simulating Hypersonic Turbulent Combustion for Future Aircraft; 4 From a Roar to a Whisper: Making Modern Aircraft Quieter; 5 Modeling of Extended Formation Flight on High-Performance Computers; 6 Supersonic Retropropulsion for Mars Entry; 7 Validating Water Spray Simulation Models for the SLS Launch Environment; 8 Simulating Moving Valves for Space Launch System Liquid Engines; 9 Innovative Simulations for Modeling the SLS Solid Rocket Booster Ignition; 10 Solid Rocket Booster Ignition Overpressure Simulations for the Space Launch System; 11 CFD Simulations to Support the Next Generation of Launch Pads; 12 Modeling and Simulation Support for NASA's Next-Generation Space Launch System; 13 Simulating Planetary Entry Environments for Space Exploration Vehicles; 14 NASA Center for Climate Simulation Highlights; 15 Ultrascale Climate Data Visualization and Analysis; 16 NASA Climate Simulations and Observations for the IPCC and Beyond; 17 Next-Generation Climate Data Services: MERRA Analytics; 18 Recent Advances in High-Resolution Global Atmospheric Modeling; 19 Causes and Consequences of Turbulence in the Earths Protective Shield; 20 NASA Earth Exchange (NEX): A Collaborative Supercomputing Platform; 21 Powering Deep Space Missions: Thermoelectric Properties of Complex Materials; 22 Meeting NASA's High-End Computing Goals Through Innovation; 23 Continuous Enhancements to the Pleiades Supercomputer for Maximum Uptime; 24 Live Demonstrations of 100-Gbps File Transfers Across LANs and WANs; 25 Untangling the Computing Landscape for Climate Simulations; 26 Simulating Galaxies and the Universe; 27 The Mysterious Origin of Stellar Masses; 28 Hot-Plasma Geysers on the Sun; 29 Turbulent Life of Kepler Stars; 30 Modeling Weather on the Sun; 31 Weather on Mars: The Meteorology of Gale Crater; 32 Enhancing Performance of NASAs High-End Computing Applications; 33 Designing Curiosity's Perfect Landing on Mars; 34 The Search Continues: Kepler's Quest for Habitable Earth-Sized Planets.

HPC↗

Coastal Online Analysis and Synthesis Tool 2.0 (COAST)

The Coastal Online Assessment and Synthesis Tool (COAST) 3D geobrowser has been developed to integrate disparate coastal datasets from NASA and other sources into a desktop tool that provides new data visualization and analysis capabilities for coastal researchers, managers, and residents. It is built upon the widely used NASA-developed open source World Wind geobrowser from NASA Ames (Patrick Hogan et al.) .Net and C# version is used for development. It is leveraged off of World Wind community shared code samples and COAST 2.0 enhancement direction is based on Coastal science community feedback and needs assessment (GOMA). The main objective is to empower the user to bring more user-meaningful data into multi-layered, multi-temporal spatial context.

Brown, Richard B.↗

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

PIPER: Performance Insights for Programmers and Exascale Runtimes

During the lifetime of the project, the PIPER at the University of Utah worked in collaboration with the team from the partner institutions to establish a set of software tools for analyzing and improving the performance parallel computing tools. These activities complement and complete the work performed by the other X-Stack 2 projects with the definition of a common data models and computing infrastructures, which have been a central element in the targeted performance analysis pipeline and new programming models. The University of Utah team have focused mainly on developing data analysis and visualization systems to facilitate exploration, comparison and evaluation of the behavior and performance of complex algorithms in modern HPC infrastructures. This is involved several demonstrations of the performance achieved and presentation of the results in a way that facilitates the users understand bottlenecks and ways to overcome them. One major focus of the University of Utah activities has been the analysis of the performance characteristics of Fat-Tree and Dragonfly topology networks. The work involved the development of practical visualization systems especially in collaboration with the team at LLNL, and published several papers and gave invited talks at prestigious venues such as the Salishan Conference on High-Speed Computing or the International Conference on High Performance Computing, Data, and Analytics. In addition, the university of Utah team has advanced the core activities on analyzing the performance characteristics of I/O and other data movement systems and their practical use for scaling Uintah and other simulation codes poised for use in the exascale computing environments of the future.

42 ENGINEERING↗

ISAVS: Interactive Scalable Analysis and Visualization System

Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. Here, we demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.

97 MATHEMATICS AND COMPUTING↗

Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. Here, this paper introduces “molecular hypernetworks” (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

RadLab: A Comprehensive Database and Analysis Toolkit for Space Radiation Measurements Relevant to Space Radiation Biology

RadLab, a new addition to the NASA Open Science Data Repository (OSDR), is a public platform for space radiation data relevant to human space exploration. RadLab consists of a database, a submission portal, and user-friendly visualization and data analysis tools, including a graphical user interface (GUI) and an application programming interface (API). Investigators from ISS partners including Germany, Italy, Canada, Hungary, the Czech Republic, Russia, Japan have committed to providing data from their instruments. RadLab will also include data from other spacecraft in LEO: the Space Shuttle, the Mir space station, biosatellites; and beyond LEO: the lunar and the Martian surface, the heliocentric orbit at 1 AU, Mars orbit, and Earth-Mars space. Once fully operational, RadLab will provide open, centralized access to space radiation physics data relevant to human space exploration; a platform for submission of data by agencies and research institutions responsible for radiation detectors deployed in space; analysis tools to facilitate detector and dataset intercomparison to better understand space habitat radiation environments; capabilities for space biology investigators to determine the radiation environment to which samples were exposed. A RadLab Working Group (RLWG) has been formed, modeled on the GeneLab Analysis Working Groups and comprised of data contributors and users. RLWG tasks include identifying data sources, normalizing data from diverse detectors, expanding the analysis toolkit and, perhaps most importantly, sharing ideas for research exploiting capabilities of RadLab. We will provide an overview of RadLab data and capabilities and discuss examples of its potential as a resource for open science.

radiation↗

Modern Era Retrospective Restrospective-Analysis for Research and Applications (MERRA) Data and Services at the GES DISC

The Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset is a NASA satellite era, 30 year (1979 - present), reanalysis using the Goddard Earth Observing System Data Assimilation System, Version 5 (GEOS-5). The project, run out of NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center, provides the science and application communities with a state-of-the-art global analysis with emphasis on improved estimates of the hydrological cycle over a broad range of weather and climate time scales. MERRA products are generated as a long-term synthesis that places the NASA EOS suite of observations in a climate context. The MERRA analysis is performed at a horizontal resolution of 2/3 longitude x 1/2 latitude (540x361 global gridpoints) with observational analyses every 6 hours. The MERRA output data will include 3 dimensional state fields for every 6 hourly analysis cycle on 42 pressure levels (or 72 terrain following model coordinate levels) from the surface through the stratosphere. Several data products are specifically designed to support chemistry and stratosphere transport modeling. The 2 dimensional surface and atmospheric diagnostics (numbering 259) are being stored on the native grid at 1 hourly intervals. These include radiation and vertical integrals of the atmosphere for water and energy budget studies and also surface diagnostics where the diurnal cycle is important. The one hourly surface and near surface data product will also facilitate research on the integrated analysis of Earth system observations in the land, ocean and cryosphere. The MERRA products are archived and distributed by the Goddard Earth Sciences Data and Information Services Center (GES DISC) through its Modeling DISC Web (MDISC) portal. Multiple data access methods and services are available for MERRA data through MDISC: (1) Mirador offers a quick, comprehensive search of MERRA and all GES DISC archived data holdings, allowing searches on keywords, location names or latitude/longitude box, and date/time, with responses within a few seconds. (2) Giovanni is a GES DISC developed Web application that provides data visualization and analysis online. Giovanni features popular visualizations such as latitude-longitude maps, animations, cross sections, profiles, time series, etc. and some basic statistical analysis functions such as scatter plots and correlation coefficient maps. Users are able to download results in several different formats, including Google Earth. (3) On-the-fly parameter subsetting of data within a spatial/temporal window is provided through a simple select and click Web page. (4) MERRA data are also available via OPeNDAP, GrADS Data Server (GDS) and can be converted to netCDF on the fly.

Berrick, Stephen W.↗