1 citations · 3 across the 4 of their papers we have counts for
5 papers
CESAR: A Convolutional Echo State AutoencodeR for High-Resolution Wind Forecasting
Matthew Bonas, Paolo Giani, Paola Crippa +1
An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high re…
A Physics-Informed, Deep Double Reservoir Network for Forecasting Boundary Layer Velocity
Matthew Bonas, David H. Richter, Stefano Castruccio
When a fluid flows over a solid surface, it creates a thin boundary layer where the flow velocity is influenced by the surface through viscosity, and can transition from laminar to…
Calibrated Forecasts of Quasi-Periodic Climate Processes with Deep Echo State Networks and Penalized Quantile Regression
Matthew Bonas, Christopher K. Wikle, Stefano Castruccio
Among the most relevant processes in the Earth system for human habitability are quasi-periodic, ocean-driven multi-year events whose dynamics are currently incompletely characteri…
High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure
Jiachen Zhang, Matthew Bonas, Diogo Bolster +2
Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a nonstation…
Calibration of Spatio-Temporal Forecasts from Citizen Science Urban Air Pollution Data with Sparse Recurrent Neural Networks
Matthew Bonas, Stefano Castruccio
With their continued increase in coverage and quality, data collected from personal air quality monitors has become an increasingly valuable tool to complement existing public heal…