8 citations · 9 across the 2 of their papers we have counts for
2 papers
physics.geo-ph2020★ 8 cited
Improving seasonal forecast using probabilistic deep learning
Baoxiang Pan, Gemma J. Anderson, AndrE Goncalves +3
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting sy…
physics.ao-ph2020★ 1 cited
Surrogate sea ice model enables efficient tuning
Kelly Kochanski, Ivana Cvijanovic, Donald Lucas
Predicting changes in sea ice cover is critical for shipping, ecosystem monitoring, and climate modeling. Current sea ice models, however, predict more ice than is observed in the…