Data integration with the Climate Science Modelling Language

Woolf, Andrew; Lawrence, Bryan; Lowry, Roy; Dam, Kerstin Kleese van; Cramer, Ray; Gutierrez, Marta; Kondapalli, S.; Latham, Susan; Lowe, Dominic; O'Neill, Kevin; Stephens, Ag

doi:10.5194/adgeo-8-83-2006

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…Basic concepts for these areas have been developed and tested for a range of projects [43][44][45]. However, these were never applied collectively, nor integrated to capture domain knowledge in an easily usable form.…”

Section: Co-analysis Across Different Investigative Methodsmentioning

confidence: 99%

Challenges in Data Intensive Analysis at Scientific Experimental User Facilities

Dam

Miller

et al. 2011

Handbook of Data Intensive Computing

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Section: Co-analysis Across Different Investigative Methodsmentioning

confidence: 99%

Challenges in Data Intensive Analysis at Scientific Experimental User Facilities

Dam

Miller

et al. 2011

Handbook of Data Intensive Computing

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“…This will lead to unprecedented data exchange possibilities and facilitate the development of widely reusable software tools. An early attempt at doing this is the Climate Science Modelling Language 9 (CSML, Woolf et al 2006) which has defined formalised feature-type models for a range of climate-science data types (point timeseries, grids, profiles, trajectories, etc.). A prototype feature catalogue for CSML has also been shown to provide benefits for re-use (Millard et al 2007).…”

Section: Reference Systemsmentioning

confidence: 99%

Powered by standards – new data tools for the climate sciences

Woolf

2010

International Journal of Digital Earth

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The ultimate goal of much current research in earth science informatics is to enable more efficient discovery and use of environmental data. Large-scale efforts are underway at regional and global levels. For instance the European INSPIRE Directive (2007/2/EC) and international GEOSS initiative will both provide unprecedented catalogues of earth observation and environmental data, with links to online services providing direct access to digital data repositories. While the motivation for these emerging infrastructures is clear (e.g. understanding global change), it is less obvious how they might be implemented. Standards will play a major role and considerable effort is currently being devoted to their development by bodies like the International Organisation for Standardisation and the Open Geospatial Consortium. Internet search engines are amongst the most popular websites visited today. Using the metaphor of a web search portal, we review the potential of new geospatial standards to provide an advanced, userfriendly approach to discovery and use of climate-science data.

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“…Another GML based implementation is NcML-GML which provides for storing the metadata of netCDF files in GML (Nativi et al, 2005). Others include CSML (Woolf et al, 2006), GeoSciML (Sen and Duffy, 2005), etc.…”

Section: Terrains@citygmlmentioning

confidence: 99%

A Citygml Extension for Handling Very Large Tins

Kumar

Ledoux

Stoter

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In addition to buildings, the terrain forms an important part of a 3D city model. Although in GIS terrains are usually represented with 2D grids, TINs are also increasingly being used in practice. One example is 3DTOP10NL, the 3D city model covering the whole of the Netherlands, which stores the relief with a constrained TIN containing more than 1 billion triangles. Due to the massive size of such datasets, the main problem that arises is: how to efficiently store and maintain them? While CityGML supports the storage of TINs, we argue in this paper that the current solution is not adequate. For instance, the 1 billion+ triangles of 3DTOP10NL require 686 GB of storage space with CityGML. Furthermore, the current solution does not store the topological relationships of the triangles, and also there are no clear mechanisms to handle several LODs. We propose in this paper a CityGML extension for the compact representation of terrains. We describe our abstract and implementation specifications (modelled in UML), and our prototype implementation to convert TINs to our CityGML structure. It increases the topological relationships that are explicitly represented, and allows us to compress up to a factor of ∼25 in our experiments with massive real-world terrains (more than 1 billion triangles).

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Data integration with the Climate Science Modelling Language

Cited by 14 publications

References 3 publications

Challenges in Data Intensive Analysis at Scientific Experimental User Facilities

Challenges in Data Intensive Analysis at Scientific Experimental User Facilities

Powered by standards – new data tools for the climate sciences

A Citygml Extension for Handling Very Large Tins

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