Ryan Lagerquist

Publications

Corresponding Articles: 3

Ryan Lagerquist authored and/or contributed to the following articles/publications.

Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model

This paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative Transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather predic...

Institution National Oceanic and Atmospheric Administration - NOAA

Using deep learning to nowcast the spatial coverage of convection from Himawari-8 satellite data

Predicting the timing and location of thunderstorms (“convection”) allows for preventive actions that can save both lives and property. We have applied U-nets, a deep-learning-based type of neural network, to forecast convection on a grid at lead times up to 120 minutes. The goal is to make skillful forecasts with only present and past satellite...

Institution National Oceanic and Atmospheric Administration - NOAA

Estimating full longwave and shortwave radiative transfer with neural networks of varying complexity.

Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous wor...

Institution National Oceanic and Atmospheric Administration - NOAA