Date/Time

Date(s) - 04/07/2021
4:30 pm - 5:30 pm

Please use this zoom link for the event.

Please join us for a UW Data Science Seminar event on Wednesday, April 7th from 4:30 to 5:30 p.m. The seminar is the second of three seminars that feature projects from this year’s Winter Incubator program.

2021 Winter Incubator projects

The goal of our Winter Incubator program is to enable new science by bringing together data scientists and domain scientists to work on focused, intensive, collaborative projects. Our team of data scientists provides expertise in state-of-the-art technology and methods in statistics and machine learning, data manipulation and analytics at all scales, cloud and cluster computing, software design and engineering, visualization, and other topics. Read more about the 2021 projects here.

“Dry thunderstorm forecast using machine learning techniques” – Wei-Yi Cheng, Department of Atmospheric Sciences

Abstract: Dry thunderstorms (DT) are convective storms that generate lightning flashes without significant rainfall at the ground. The frequent occurrence of DTs has long been an important safety concern in the western United States due to its connection to wildfire events. An accurate forecast for the dry thunderstorms is therefore critically important, but remains a difficult task because the corresponding physical mechanisms are not yet fully understood. In addition, traditional lightning parameterizations methods are often based on simplified physical intuition and are limited by a small number of free parameters. Machine learning (ML) techniques enable a more ambitious approach to develop parameterizations using data-driven approach and are therefore more flexible than traditional lightning parameterizations. By utilizing the recently developed lightning observations dataset from World-Wide Lightning Location Network (WWLLN) and various atmospheric observations/reanalysis products from ERA5 and TRMM, this study aims to improve the dry thunderstorms forecast skill in the western United States. Several ML methods are tested, including the random forest model and neural network model, where the models are applied to predictions of both binary classification of the occurrence of thunderstorms and the total lightning stroke. By using 10 years of data, our results show that, even with the same input variables, the ML-based lightning parameterization methods are able to outperform an empirical lightning parameterization method in terms of capturing the spatial/temporal variability of both normal and dry lightning.

“Climate Adaptation for Future Maize – Novel Plant Traits and New Management” – Jennifer Hsiao, Department of Biology

Abstract:

Over the next three decades rising population and changing dietary preferences are expected to increase food demand by 25–75%. At the same time climate is also changing — with potentially drastic impacts on food production. Changes in crop characteristics and management practices have the potential to partially mitigate yield loss due to a changing climate. However, a substantial knowledge gap remains for which of these adaptation techniques are likely to be most effective at any point in time, the mechanisms through which they can mitigate yield loss, and the relative effectiveness of different approaches.

In this project, we use a process-based crop simulation model to explore how different crop traits and agricultural management options affect maize growth and yield, with the hope to identify ideal trait and management combinations that maximize yield and minimize risk for different agro-climate regions in the US. We hope this work will shed light on region-specific adaptation strategies for US maize facing a changing climate.

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.

All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.