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Maskey, Manil

Publications and source records attributed to Maskey, Manil.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

VISAGE Visualization for Integrated Satellite, Airborne and Ground-Based Data Exploration

The primary goal of the VISAGE project is to facilitate more efficient Earth Science investigations via a tool that can provide visualization and analytic capabilities for diverse coincident datasets. This proof-of-concept project will be centered around the GPM Ground Validation program, which provides a valuable source of intensive, coincident observations of atmospheric phenomena. The data are from a wide variety of ground-based, airborne and satellite instruments, with a wide diversity in spatial and temporal scales, variables, and formats, which makes these data difficult to use together. VISAGE will focus on "golden cases" where most ground instruments were in operation and multiple research aircraft sampled a significant weather event, ideally while the GPM Core Observatory passed overhead. The resulting tools will support physical process studies as well as satellite and model validation.

Data fusion↗
NASA NTRS2017

Building Scalable Knowledge Graphs for Earth Science

Knowledge Graphs link key entities in a specific domain with other entities via relationships. From these relationships, researchers can query knowledge graphs for probabilistic recommendations to infer new knowledge. Scientific papers are an untapped resource which knowledge graphs could leverage to accelerate research discovery. Goal: Develop an end-to-end (semi) automated methodology for constructing Knowledge Graphs for Earth Science.

Ramachandran, Rahul↗
NASA NTRS2017

Detection of Hail Storms in Radar Imagery Using Deep Learning

In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.

natural hazard↗
NASA NTRS2017

Exploiting Dark Information Resources to Create New Value Added Services to Study Earth Science Phenomena

This paper presents two research applications exploiting unused metadata resources in novel ways to aid data discovery and exploration capabilities. The results based on the experiments are encouraging and each application has the potential to serve as a useful standalone component or service in a data system. There were also some interesting lessons learned while designing the two applications and these are presented next.

Earth Science Informatics↗
NASA NTRS2016

Making Connections: Where STEM Learning and Earth Science Data Services Meet

STEM (Science, Technology, Engineering, Mathematics) learning is most effective when students are encouraged to see the connections between science, technology and real world problems. Helping to make these connections has become an increasingly important aspect of Earth Science data research. The Global Hydrology Resource Center (GHRC), one of NASA's 12 EOSDIS (Earth Observing System Data Information System) data centers, has developed a new type of documentation called the micro article to facilitate making connections between data and Earth science research problems.

Micro articles↗
NASA NTRS2016

Virtual Collections: An Earth Science Data Curation Service

The role of Earth science data centers has traditionally been to maintain central archives that serve openly available Earth observation data. However, in order to ensure data are as useful as possible to a diverse user community, Earth science data centers must move beyond simply serving as an archive to offering innovative data services to user communities. A virtual collection, the end product of a curation activity that searches, selects, and synthesizes diffuse data and information resources around a specific topic or event, is a data curation service that improves the discoverability, accessibility, and usability of Earth science data and also supports the needs of unanticipated users. Virtual collections minimize the amount of the time and effort needed to begin research by maximizing certainty of reward and by providing a trustworthy source of data for unanticipated users. This presentation will define a virtual collection in the context of an Earth science data center and will highlight a virtual collection case study created at the Global Hydrology Resource Center data center.

Data↗