CCdownscaling: an open-source Python package for multivariable statistical climate model downscaling V1.0
TL;DR: The CCdownscaling package as mentioned in this paper provides a number of downscaling methods, including self-organizing maps, as well as evaluation metrics for assessing downscale model skill, and it is open-source and freely available for use in generating downscaled projections.
read more
Abstract: . Statistical downscaling methods provide an essential bridge between low resolution global climate models and localized information needed by decision makers. As the demand for localized climate information continues to grow to make projections for a wide variety of applications, the need for software that can provide this sort of downscaled data grows with it. The CCdownscaling package described in the article provides a number of downscaling methods, including Self Organizing Maps, as well as a number of evaluation metrics for assessing downscale model skill. In this article, we describe the features of 5 the CCdownscaling package, and show an example use case for downscaling temperature and precipitation. It is open-source and freely available for use in generating downscaled projections.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
References
Random Forests
Leo Breiman
- 01 Oct 2001
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
•Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
An Overview of CMIP5 and the Experiment Design
TL;DR: The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance the authors' knowledge of climate variability and climate change.
The self-organizing map
Teuvo Kohonen
- 01 Sep 1990
TL;DR: The self-organizing map, an architecture suggested for artificial neural networks, is explained by presenting simulation experiments and practical applications, and an algorithm which order responses spatially is reviewed, focusing on best matching cell selection and adaptation of the weight vectors.
9.9K
The Kolmogorov-Smirnov Test for Goodness of Fit
TL;DR: In this paper, the maximum difference between an empirical and a hypothetical cumulative distribution is calculated, and confidence limits for a cumulative distribution are described, showing that the test is superior to the chi-square test.
5.9K