Toby Ault, Marty Sullivan – Cornell
Numerical models of climate science. Most of fluid dynamics in models for weather and climate are physical equations and solvers are “Dynamical Cores” that tells you about the flows of fluid in 3d space.
Continuum of scale needs to be accommodated – done through parametrization. Want to be able to sample as many parameterization schemes as possible.
Interested in intermediate time scales (weeks to months) that have been difficult to model. There’s uncertainty arising from different models so having multiple models with multiple parameterizations that get averaged together with machine learning can have huge payouts in accuracy.
Are the most useful variables for stakeholders the most predictive?
Weather simulation in the cloud:
Infrastructure as code makes things much easier. Able to containerize the models (which include lots of old code), so people don’t need to know all the nuts and bolts. Using lots of serverless – makes running web interfaces extremely easy.
Democratization of science – offered through a web interface. People can configure and run models for their locations.
Lots of orchestration behind the scenes: Deploying with CloudFormation, using ECS, etc.