Segmenting and Tracking Extreme Climate Events using Neural Networks

By Mayur Mudigonda, Sookyung Kim, A. Mahesh, Samira Ebrahimi Kahou, Karthik Kashinath, Dean N. Williams, Vincent Michalski, T. O’Brien, M. Prabhat. 2017

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Gauge Equivariant Convolutional Networks and the Icosahedral CNN (2019, positive): “We evaluate our method on the climate pattern segmentation task proposed by Mudigonda et al. (2017).”
Spherical CNNs on Unstructured Grids (2019, methodology): “Following Mudigonda et al. (2017), we analyze outputs from a 20-year run of the Community Atmospheric Model v5 (CAM5) (Neale et al., 2010).”
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems (2020, background): “, high-impact weather predictions [194], segmentation of climate models [202], and tracking phenomena from climate model data [97, 237]).”
Integrating Physics-Based Modeling with Machine Learning: A Survey (2020, background): “, high-impact weather predictions [175], segmentation of climate models [181], and tracking phenomena from climate model data [85, 219]).”
DeepSphere: a graph-based spherical CNN (2020, methodology): “We evaluate our method on a task proposed by (Mudigonda et al., 2017): the segmentation of extreme climate events, Tropical Cyclones (TC) and Atmospheric Rivers (AR), in global climate simulations (figure 1c).”