CICMoD - A Climate Index Collection Benchmark (Data and Resources Paper)
Article No.: 77, Pages 1 - 4
Abstract
In the domain of climate science, machine learning (ML) and in particular deep learning (DL) methods are known to be effective for identifying causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills of forecast systems. To attribute climate events in a data-driven way, we need sufficient training data, which is often limited for real world measurements. The data science community provides standard data sets for many applications. As a new data set, we introduce a consistent and comprehensive collection of climate indices typically used to describe Earth System dynamics. Therefore, we use 1000-year control simulations from Earth System Models. The data set is provided as an open-source framework that can be extended and customized to individual needs. It allows users to develop new ML methodologies and to compare results to existing methods and models as benchmark.
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Index Terms
- CICMoD - A Climate Index Collection Benchmark (Data and Resources Paper)
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Published In
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
- General Chairs:
- Maria Luisa Damiani,
- Matthias Renz,
- Program Chairs:
- Ahmed Eldawy,
- Peer Kröger,
- Mario A Nascimento
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Association for Computing Machinery
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Publication History
Published: 22 December 2023
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- Short-paper
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SIGSPATIAL '23
Sponsor:
SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems
November 13 - 16, 2023
Hamburg, Germany
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Overall Acceptance Rate 257 of 1,238 submissions, 21%
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