105 citations · 133 across the 16 of their papers we have counts for
3 papers · 1 filter
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification
Daniel Getter, Julie Bessac, Johann Rudi +1
Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over re…
Joint modeling of wind speed and wind direction through a conditional approach
Eva Murphy, Whitney Huang, Julie Bessac +2
Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to cl…
Station-wise statistical joint assessment of wind speed and direction under future climates across the United States
Qiuyi Wu, Julie Bessac, Whitney Huang +1
This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the representativ…