1 citations · 1 across the 4 of their papers we have counts for
4 papers · 1 filter
Do AI Forecast Ensembles Sample the Correct Conditional Distribution?
Lucas J. Howard, Elizabeth A. Barnes
Ensemble forecasting aims to sample the conditional distribution of outcomes; whether AI forecast ensembles do this correctly in a joint sense remains largely untested. We train a…
AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability
Jacob B. Landsberg, Matthew Newman, Elizabeth A. Barnes
Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We exp…
Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network
M. A. Fernandez, Elizabeth A. Barnes, Randal J. Barnes +4
A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves a…
Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework
M. A. Fernandez, Elizabeth A. Barnes
Multi-year-to-decadal climate predictions are a key tool in understanding the range of potential regional climate futures. Here, we present a framework that combines machine learni…