scholarly journals NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology

2020 ◽  
Vol 12 (17) ◽  
pp. 2821
Author(s):  
Zhong Liu ◽  
Chung-Lin Shie ◽  
Angela Li ◽  
David Meyer
Keyword(s):  
Earth Sciences ◽  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Data Access ◽  
Tropical Meteorology ◽  
Distribution Analysis ◽  
Model Data ◽  
Science Data ◽  
Data Products

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications.

scholarly journals Giovanni: The Bridge Between Data and Science

Eos ◽  
2017 ◽  
Author(s):  
Zhong Liu ◽  
James Acker
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Science Data ◽  
Web Based ◽  
Sensing Data ◽  
Daunting Task ◽  
Earth Science Data

Using satellite remote sensing data sets can be a daunting task. Giovanni, a Web-based tool, facilitates access, visualization, and exploration for many of NASA’s Earth science data sets.

scholarly journals Smart Environmental Data Infrastructures: Bridging the Gap between Earth Sciences and Citizens

2020 ◽  
Vol 10 (3) ◽  
pp. 856 ◽  
Author(s):  
José R. R. Viqueira ◽  
Sebastián Villarroya ◽  
David Mera ◽  
José A. Taboada
Keyword(s):  
Scientific Community ◽  
Earth Science ◽  
Scientific Information ◽  
Open Data ◽  
Data Access ◽  
Data Representation ◽  
Environmental Data ◽  
Science Data ◽  
Data Infrastructures ◽  
Earth Science Data

The monitoring and forecasting of environmental conditions is a task to which much effort and resources are devoted by the scientific community and relevant authorities. Representative examples arise in meteorology, oceanography, and environmental engineering. As a consequence, high volumes of data are generated, which include data generated by earth observation systems and different kinds of models. Specific data models, formats, vocabularies and data access infrastructures have been developed and are currently being used by the scientific community. Due to this, discovering, accessing and analyzing environmental datasets requires very specific skills, which is an important barrier for their reuse in many other application domains. This paper reviews earth science data representation and access standards and technologies, and identifies the main challenges to overcome in order to enable their integration in semantic open data infrastructures. This would allow non-scientific information technology practitioners to devise new end-user solutions for citizen problems in new application domains.

scholarly journals Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

2020 ◽  
Vol 12 (22) ◽  
pp. 3781
Author(s):  
George Riggs ◽  
Dorothy Hall
Keyword(s):  
Snow Cover ◽  
Infrared Imaging ◽  
Earth Science ◽  
Satellite System ◽  
Science Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Data Products ◽  
Moderate Resolution ◽  
Earth Science Data

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

Distributed Generation of NASA Earth Science Data Products

2003 ◽  
Vol 1 (2) ◽  
pp. 101-116 ◽  
Author(s):  
Bruce R. Barkstrom ◽  
Thomas H. Hinke ◽  
Shradha Gavali ◽  
Warren Smith ◽  
William J. Seufzer ◽  
...  
Keyword(s):  
Distributed Generation ◽  
Earth Science ◽  
Science Data ◽  
Data Products ◽  
Earth Science Data

scholarly journals How to publish your data with the EPOS Multi-scale Laboratories

2021 ◽  
Author(s):  
Geertje ter Maat ◽  
Otto Lange ◽  
Martyn Drury ◽  
Keyword(s):  
Earth Sciences ◽  
Solid Earth ◽  
Earth Science ◽  
Laboratory Data ◽  
Supplementary Information ◽  
Science Data ◽  
Scientific Publications ◽  
Data Repositories ◽  
Multi Scale ◽  
Metadata Standards

<p>EPOS (the European Plate Observing System) is a pan-European e-infrastructure framework with the goal of improving and facilitating the access, use, and re-use of Solid Earth science data. The EPOS Thematic Core Service Multi-scale Laboratories (TCS MSL) represent a community of European Solid Earth sciences laboratories including high-temperature and high-pressure experimental facilities, electron microscopy, micro-beam analysis, analogue tectonic and geodynamic modelling, paleomagnetism, and analytical laboratories. </p><p>Participants and collaborating laboratories from Belgium, Bulgaria, France, Germany, Italy, Norway, Portugal, Spain, Switzerland, The Netherlands, and the UK are already represented within the TCS MSL. Unaffiliated European Solid Earth sciences laboratories are welcome and encouraged to join the growing TCS MSL community.</p><p>Laboratory facilities are an integral part of Earth science research. The diversity of methods employed in such infrastructures reflects the multi-scale nature of the Earth system and is essential for the understanding of its evolution, for the assessment of geo-hazards, and the sustainable exploitation of geo-resources.</p><p>Although experimental data from these laboratories often provide the backbone for scientific publications, they are often only available as images, graphs or tables in the text or as supplementary information to research articles. As a result, much of the collected data remains unpublished, not searchable or even inaccessible, and often only preserved in the short term.</p><p>The TCS MSL is committed to making Earth science laboratory data Findable, Accessible, Interoperable, and Reusable (FAIR). For this purpose, the TCS MSL encourages the community to share their data via DOI-referenced, citable data publications. To facilitate this and ensure the provision of rich metadata, we offer user-friendly tools, plus the necessary data management expertise, to support all aspects of data publishing for the benefit of individual lab researchers via partner repositories. Data published via TCS MSL are described with the use of sustainable metadata standards enriched with controlled vocabularies used in geosciences. The resulting data publications are also exposed through a designated TCS MSL online portal that brings together DOI-referenced data publications from partner research data repositories (https://epos-msl.uu.nl/). As such, efforts have already been made to interconnect new data (metadata exchange) with previous databases such as MagIC (paleomagnetic data in Earthref.org), and in the future, we expect to enlarge and improve this practice with other repositories. </p>

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