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http://conference.scipy.org/pr...
Article
License: CC BY
Data sources: UnpayWall
https://doi.org/10.25080/major...
Article . 2016 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2023
Data sources: DBLP
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The Climate Modelling Toolkit

Authors: Joy Merwin Monteiro; Rodrigo Caballero;

The Climate Modelling Toolkit

Abstract

The Climate Modelling Toolkit (CliMT) is a Python-based software component toolkit providing a flexible problem-solving environment for climate science problems. It aims to simplify the development of models of complexity ’appropriate’ to the scientific question at hand. This aim is achieved by providing Python-level access to components commonly used in climate models (such as radiative transfer models and dynamical cores) and using the expressive data structures available in Python to access and combine these components. This paper describes the motivation behind developing CliMT, and serves as an introduction to interested users and developers.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
hybrid