Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series "Clouds and Climate")

Author:

Beucler Tom12ORCID,Ebert-Uphoff Imme34,Rasp Stephan56,Pritchard Michael7,Gentine Pierre8

Affiliation:

1. University of Lausanne

2. University of California, Irvine

3. Cooperative Institute for Research in the Atmosphere

4. Colorado State University

5. ClimateAi

6. Inc

7. University of California

8. Columbia University

Funder

National Science Foundation

U.S. Department of Energy

European Research Council

Publisher

Wiley

Reference197 articles.

1. Adebayo J. Gilmer J. Muelly M. Goodfellow I. Hardt M. & Kim B. (2018). Sanity checks for saliency maps. In Advances in neural information processing systems (pp. 9505-9515).

2. Baño-Medina J. L. García Manzanas R. Gutiérrez Llorente J. M. etal (2020). Configuration and intercomparison of deep learning neural models for statistical downscaling.

3. Bansal N. Agarwal C. & Nguyen A. (2020). Sam: The sensitivity of attribution methods to hyperparameters. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (pp. 8673-8683).

4. Viewing forced climate patterns through an ai lens;Barnes E. A.;Geophysical Research Letters,2019

5. August). Tropospheric and stratospheric causal pathways between the MJO and NAO;Barnes E. A.;J. Geophys. Res. D: Atmos.,2019

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