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Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty.
STint: Self-supervised Temporal Interpolation for Geospatial Data
Nevertheless, most prevailing temporal interpolation techniques hinge on optical flow, which encodes the motion of pixels between video frames.
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
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