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Found 7 papers, 3 papers with code
Validating Climate Models with Spherical Convolutional Wasserstein Distance
The validation of global climate models is crucial to ensure the accuracy and efficacy of model output.
Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study
As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential.
Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data
Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction.
Multi-model Ensemble Analysis with Neural Network Gaussian Processes
Multi-model ensemble analysis integrates information from multiple climate models into a unified projection.
Scalable Multiple Changepoint Detection for Functional Data Sequences
We show that our method outperforms a recent multiple functional changepoint detector and several univariate changepoint detectors applied to our proposed projections.
Denoising
Time Series Analysis
Methodology
Evaluating proxy influence in assimilated paleoclimate reconstructions -- Testing the exchangeability of two ensembles of spatial processes
Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output.
Methodology Applications
Elastic depths for detecting shape anomalies in functional data
We propose a new family of depth measures called the elastic depths that can be used to greatly improve shape anomaly detection in functional data.
Methodology
