From ARDS to AODS: Future of Analytics for Earth Observations
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Analytics Optimized Data Store↗
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NASA EOSDIS Evolution in the BigData Era
NASA's EOSDIS system faces several challenges in the Big Data Era. Although volumes are large (but not unmanageably so), the variety of different data collections is daunting. That variety also brings with it a large and diverse user community. One key evolution EOSDIS is working toward is to enable more science analysis to be performed close to the data.
Information Systems↗
Solar activity and oscillation frequency splittings
Solar p-mode frequency splittings, parameterized by the coefficients through order N = 12 of a Legendre polynomial expansion of the mode frequencies as a function of m/L, were obtained from an analysis of helioseismology data taken at Big Bear Solar Observatory during the 4 years 1986 and 1988-1990 (approximately solar minimum to maximum). Inversion of the even-index splitting coefficients confirms that there is a significant contribution to the frequency splittings originating near the solar poles. The strength of the polar contribution is anti correlated with the overall level or solar activity in the active latitudes, suggesting a relation to polar faculae. From an analysis of the odd-index splitting coefficients we infer an uppor limit to changes in the solar equatorial near-surface rotatinal velocity of less than 1.9 m/s (3 sigma limit) between solar minimum and maximum.
Woodard, M. F.↗
Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture
Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.
Zhang, Hailiang↗
Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh
We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.
SciDB↗
Near 40 Years MERRA-2 Data at NASA GES DISC -Opportunity and Challenge to Support Extremes Study
To the end of 2019, 40 years NASA climate reanalysis data sets from the Modern Era Retrospective-analysis for Research and Applications, Version 2 (MERRA-2) will be available at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 consists atmosphere, land, and ocean data, which may be used for the studies ranging from the short scale weather events to the large scale decadal vulnerabilities. The hourly products, such as precipitation, soil moisture, temperature, and aerosols etc., have been used widely to study extreme events.In supporting users from broad communities, GES DISC have developed various data access services, including subsetter for downloading only data of interest with preferred format; OPeNDAP - for machine-to-machine data access; and Giovanni- for online visualization and analysis, etc. A big challenge for extreme study is to downloading and processing long-term hourly or daily data. The data downloading performance is not very satisfied by many users with current services and the native archived data structure. Late June 2019, many people in Europe had experienced extreme heat waves. The temperatures in several countries exceeded 40°C (104°F). For example, MERRA-2 shows that the near surface daily maximum temperature of June 28 2019 over Marseille, a city in southern France, reached 41.1 °C (106°F), which is the record breaking temperature in the last 40 years. GES DISC is working together with domain science experts to improve the performance of long time series access, making analysis ready data sets in supporting application researches, such as extreme study. In this presentation, using Europe heat wave as an example, we will show prototype of the in developing service for finding extremes from near 40 years MERRA-2 data at a given location. MERRA-2 data can be accessed from NASA GES DISC(https://disc.gsfc.nasa.gov/ ) by search keyword "MERRA-2".
Shen, Suhung↗
An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction
Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.
Liu, Qian↗
Advanced Analytics and Big Earth Data
NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.
Cloud Computing↗
Statistical Analysis of CMC Constituent and Processing Data
Ceramic Matrix Composites (CMCs) are the next "big thing" in high-temperature structural materials. In the case of jet engines, it is widely believed that the metallic superalloys currently being utilized for hot structures (combustors, shrouds, turbine vanes and blades) are nearing their potential limits of improvement. In order to allow for increased turbine temperatures to increase engine efficiency, material scientists have begun looking toward advanced CMCs and SiC/SiC composites in particular. Ceramic composites provide greater strength-to-weight ratios at higher temperatures than metallic alloys, but at the same time require greater challenges in micro-structural optimization that in turn increases the cost of the material as well as increases the risk of variability in the material s thermo-structural behavior. to model various potential CMC engine materials and examines the current variability in these properties due to variability in component processing conditions and constituent materials; then, to see how processing and constituent variations effect key strength, stiffness, and thermal properties of the finished components. Basically, this means trying to model variations in the component s behavior by knowing what went into creating it. inter-phase and manufactured by chemical vapor infiltration (CVI) and melt infiltration (MI) were considered. Examinations of: (1) the percent constituents by volume, (2) the inter-phase thickness, (3) variations in the total porosity, and (4) variations in the chemical composition of the Sic fiber are carried out and modeled using various codes used here at NASA-Glenn (PCGina, NASALife, CEMCAN, etc...). The effects of these variations and the ranking of their respective influences on the various thermo-mechanical material properties are studied and compared to available test data. The properties of the materials as well as minor changes to geometry are then made to the computer model and the detrimental effects observed using statistical analysis software. The ultimate purpose of this study is to determine what variations in material processing can lead to the most critical changes in the materials property. The work I have taken part in this summer explores, in general, the key properties needed In this study SiC/SiC composites of varying architectures, utilizing a boron-nitride (BN)
Fornuff, Jonathan↗
Introduction to Big Earth Data Applications
Climate and weather modeling generate enormous volumes that make iterative analysis challenging, spurring the development of new ways to work with the data. A theme going across applications is the need to identify and highlight "interesting" data for the scientist to focus on. Operational applications often scale up from small, local studies to larger spatial scales with more analysis targets.
parallel processing (computers)↗
Introduction to Big Earth Data Applications
Climate and weather modeling generate enormous volumes that make iterative analysis challenging, spurring the development of new ways to work with the data. At the same time in the Earth Observation area, technology advances are enabling new sensors and satellites that will increase data volume, velocity and application variety. Scaling up can also be seen when operational applications expand from small, local studies to larger spatial scales with more analysis targets.
Christopher Lynnes↗
IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX
We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.
visualization↗
Analyzing a 35-Year Hourly Data Record: Why So Difficult?
At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.
computational performance↗
VEDA Visualization Exploration & Data Analysis
Why? - Interdisciplinary science depends on large amount of Earth science data and computational resources - Working with these datasets is non-trivial - Big data science requires advanced distributed computing knowledge What? VEDA is an open platform that brings key Earth science datasets next to open source tools for data processing, analysis, visualization, and exploration in a managed and more accessible computing environment.
Manil Maskey↗
Climate Analytics as a Service
Climate science is a big data domain that is experiencing unprecedented growth. In our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). CAaaS combines high-performance computing and data-proximal analytics with scalable data management, cloud computing virtualization, the notion of adaptive analytics, and a domain-harmonized API to improve the accessibility and usability of large collections of climate data. MERRA Analytic Services (MERRA/AS) provides an example of CAaaS. MERRA/AS enables MapReduce analytics over NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of key climate variables. The effectiveness of MERRA/AS has been demonstrated in several applications. In our experience, CAaaS is providing the agility required to meet our customers' increasing and changing data management and data analysis needs.
big data↗
The Global Oscillation Network Group site survey, 2: Results
The Global Oscillation Network Group (GONG) Project will place a network of instruments around the world to observe solar oscillations as continuously as possible for three years. The Project has now chosen the six network sites based on analysis of survey data from fifteen sites around the world. The chosen sites are: Big Bear Solar Observatory, California; Mauna Loa Solar Observatory, Hawaii; Learmonth Solar Observatory, Australia; Udaipur Solar Observatory, India; Observatorio del Teide, Tenerife; and Cerro Tololo Interamerican Observatory, Chile. Total solar intensity at each site yields information on local cloud cover, extinction coefficient, and transparency fluctuations. In addition, the performance of 192 reasonable networks assembled from the individual site records is compared using a statistical principal components analysis. An accompanying paper descibes the analysis methods in detail; here we present the results of both the network and individual site analyses. The selected network has a duty cycle of 93.3%, in good agreement with numerical simulations. The power spectrum of the network observing window shows a first diurnal sidelobe height of 3 x 10(exp -4) with respect to the central component, an improvement of a factor of 1300 over a single site. The background level of the network spectrum is lower by a factor of 50 compared to a single-site spectrum.
Hill, Frank↗
Using Big Data Technologies with Earth Science Data in HDF5: HDF5 Scalable Solutions
HDF5 (Hierarchical Data Format 5) is open-source, high-performance software that consists of an abstract data model, library, and fileformat used for storing and managing extremely large and/or complex data collections. NASA Earth Observing System (EOS) Data and Information Systems use HDF5 as an archival format to store remote sensing data from EOS satellites. HDF5 is also used to store other types of Geoscience and Strophysical data, e.g., seismic data and data from Low-Frequency Array (LOFAR) radio telescopes. Data stored in HDF5 has reached tens of petabytes and is growing at an accelerated rate.With the growing amout of HDF5 Earth Science data to analyze and process, scientists need to adopt big data technologies including new storage paradigms such as cloud and object storage. To run models and perform data analysis they also need to utilizied efficient and diverse ways to access data, from high-performance computing's (HPC) Message Passing Interface (MPI) I/O and deep memory hierarchies (DMH) to non-HPC frameworks such as Apache Hadoop, Spark, and Drill. The HDF Group continually works to enable usage of big data technologies in HDF software.
Knox, Larry↗