Please use this zoom link for the event.
Please join us for a UW Data Science Seminar event on Tuesday, October 26th from 4:30 to 5:30 p.m. The seminar will feature Brian Henn, Machine Learning Scientist with the AI2 Climate Modeling team.
“Correcting coarse-resolution weather and climate models by machine learning from global storm-resolving simulations”
Abstract: Global atmospheric `storm-resolving’ models with horizontal grid spacing of less than ~5km resolve deep cumulus convection and flow in complex terrain. While computationally expensive, they can be run for yearlong scales and can serve as reference models for improving more economical coarse-grid global weather and climate models. Machine learning (ML) offers an avenue for translating the patterns seen in storm-resolving models onto the coarser grid, with the ultimate goal of reducing uncertainties in regional precipitation and temperature trends in global climate models. AI2’s Climate Modeling group is undertaking an effort to apply this approach in an operational weather and climate model, among other efforts to leverage ML in climate modeling. Here we describe an experiment to machine learn the corrections between a coarse and storm-resolving model (“nudging”) as functions of coarse model state, in order to improve upon the physical parameterizations of temperature, humidity, and winds, in a real-geography coarse-grid model (FV3GFS with a 200 km grid). We evaluate whether the ML correction improves the coarse model’s weather forecast metrics and spatial distributions of temperature and precipitation. The best configuration of ML uses learned nudging for temperature and humidity but not winds, with neural nets slightly outperforming random forests. Forecasts of 850 hPa temperature gain 18 hours of skill at 3-7 day leads and time-mean precipitation patterns are improved 30% by applying the ML correction.
Biography: Brian Henn is a machine learning scientist with the AI2 Climate Modeling team. He has worked on weather, flood, and water resources forecasting and modeling for more than a decade, with a particular focus on the climate of the western United States. Previously he was a postdoc at Scripps Institution of Oceanography and a data scientist at a Seattle startup. He has a PhD in civil and environmental engineering from the University of Washington, and enjoys getting outside with his family on the weekends.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
The 2021-2022 seminars will be hybrid virtual and in-person events, and are free and open to the public.