Using Machine Learning to Improve the NSSL's Warn-On-Forecast System's Prediction of Thunderstorm Location

Wiley, Chad

Using Machine Learning to Improve the NSSL's Warn-On-Forecast System's Prediction of Thunderstorm Location

dc.contributor.advisor	Potvin, Corey
dc.contributor.advisor	McGovern, Amy
dc.contributor.author	Wiley, Chad
dc.contributor.committeeMember	Flora, Montgomery
dc.contributor.committeeMember	Homeyer, Cameron
dc.date.accessioned	2023-08-01T14:07:59Z
dc.date.available	2023-08-01T14:07:59Z
dc.date.issued	2023-08-04
dc.date.manuscript	2023-07-27
dc.description.abstract	Deep learning (DL) models have become immensely popular in recent years, with many models creating accurate and high-skill predictions for a wide range of atmospheric phenomena. Using DL models for predicting convection and associated hazards has experienced some of the most substantial gains in skill. The National Severe Storms Laboratory (NSSL) has created the experimental Warn-On-Forecast System (WoFS) to increase warning lead times through probabilistic short-term forecasts of individual thunderstorms. Currently, the WoFS has a shortcoming of missing storms due largely to poorly initialized environments. To help mitigate this issue, we developed a U-Net deep learning model to predict locations of thunderstorms trained on WoFS model data consisting of environmental data, such as CAPE and CIN, and intra-storm variables, such as WoFS ensemble average, mean, and max composite reflectivity and updraft and downdraft velocities. To address the issue of poorly initialized environments and lagging data assimilation, the model also has access to Multi-Radar/Multi-Sensor System (MRMS) data valid at the WoFS initialization is also used as an input. To evaluate the skill of the DL-based guidance, different baseline methods were tested to ensure a substantial performance increase. Comparing the performances of the WoFS baseline and DL model on an independent testing dataset, we were able to increase the maximum critical success index from 0.17 to 0.27, along with increasing the reliability and discrimination of the predictions. Using MRMS composite reflectivity proved to be vital for the DL model's performance when predicting values >= 40 dBZ. Through this work, we demonstrate DL models are an effective and efficient solution to improving the skill of the WoFS forecast of convection with a 30-minute lead time.	en_US
dc.identifier.uri	https://shareok.org/handle/11244/338757
dc.language	en_US	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.subject	Machine Learning	en_US
dc.subject	Severe Weather	en_US
dc.subject	Nowcasting	en_US
dc.subject	Warn-on-Forecast System	en_US
dc.subject	Deep Learning	en_US
dc.subject	U-Nets	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Using Machine Learning to Improve the NSSL's Warn-On-Forecast System's Prediction of Thunderstorm Location	en_US
ou.group	College of Atmospheric and Geographic Sciences::School of Meteorology	en_US