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RISCausal Discovery for Climate Research Using Graphical Models
Publisher: American Meteorological Society
Abstract: ausal discovery seeks to recover cause?effect relationships from statistical data using graphical models. One goal of this paper is to provide an accessible introduction to causal discovery methods for climate scientists, ...
Evaluation, Tuning and Interpretation of Neural Networks for Working with Images in Meteorological Applications
Publisher: American Meteorological Society
Abstract: This article discusses strategies for the development of neural networks (aka deep learning) for meteorological applications. Topics include evaluation, tuning and interpretation of neural networks for working with ...
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience
Publisher: American Meteorological Society
Superresolution of GOES-16 ABI Bands to a Common High Resolution with a Convolutional Neural Network
Publisher: American Meteorological Society
Carefully Choose the Baseline: Lessons Learned from Applying XAI Attribution Methods for Regression Tasks in Geoscience
Publisher: American Meteorological Society
Exploring the Use of Machine Learning to Improve Vertical Profiles of Temperature and Moisture
Publisher: American Meteorological Society
Development and Interpretation of a Neural-Network-Based Synthetic Radar Reflectivity Estimator Using GOES-R Satellite Observations
Publisher: American Meteorological Society
Using Deep Learning to Nowcast the Spatial Coverage of Convection from Himawari-8 Satellite Data
Publisher: American Meteorological Society
Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications
Publisher: American Meteorological Society
Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity
Publisher: American Meteorological Society