14 citations · 42 across the 6 of their papers we have counts for
9 papers
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
Rupa Kurinchi-Vendhan, Björn Lütjens, Ritwik Gupta +2
The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from n…
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +3
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is…
The World as a Graph: Improving El Niño Forecasts with Graph Neural Networks
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +5
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Björn Lütjens, Catherine H. Crawford, Mark Veillette +1
Climate models project an uncertainty range of possible warming scenarios from 1.5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble…
Graph Neural Networks for Improved El Niño Forecasting
Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker +4
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-Southern Oscillation (ENSO). However, current dee…
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
Simona Santamaria, David Dao, Björn Lütjens +1
International stakeholders increasingly invest in offsetting carbon emissions, for example, via issuing Payments for Ecosystem Services (PES) to forest conservation projects. Issui…