Lunch and Learn with Jebb Stewart

May 16

12:00pm - 1:00pm

Engineering & Computer Science, Room 510, Event Space

New Approaches to Dealing with Increasing Volumes of Data for Numerical Weather Modeling

Big data is a big challenge for Numerical Weather Prediction (NWP). As Machine Learning and in particular Deep Learning applications continue to grow across a variety of fields, our team at the National Oceanic Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) is researching the application of these techniques for processing of satellite observation data. With the recent launch of new satellites, the data volume has increased by orders of magnitude and can be difficult to process in a timely manner using traditional methods. Deep Learning has shown promising advancements to significantly improve both processing speed and scientific accuracy of results.

This presentation will provide an overview of our research efforts at ESRL into the application of deep learning to improve satellite data assimilation, object detection and classification, along with the challenges we face, and where we see these applications heading into the future.
Biography:


Jebb Q. Stewart is the lead of the Informatics, Visualization, and Outreach Section with the NOAA Earth System Research Laboratory through the Cooperative Institute for Research in the Atmosphere (CIRA). With a unique background in both Meteorology and Computer Science, he has over 20 years of experience in software development for visualizing, processing, distributing, and interacting with geophysical data.