activity
20192025
most citedStructural Forecasting for Short-term Tropical Cyclone Intensity Guidance

8 citations · 14 across the 7 of their papers we have counts for

collaborators

7 papers

eess.IV2025

Multi-Resolution Analysis of the Convective Structure of Tropical Cyclones for Short-Term Intensity Guidance

Elizabeth Cucuzzella, Tria McNeely, Kimberly Wood +1

Accurate tropical cyclone (TC) short-term intensity forecasting with a 24-hour lead time is essential for disaster mitigation in the Atlantic TC basin. Since most TCs evolve far fr…

stat.AP2022★ 8 cited

Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance

Trey McNeely, Pavel Khokhlov, Niccolo Dalmasso +2

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to sho…

stat.AP2022★ 2 cited

Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery

Trey McNeely, Galen Vincent, Kimberly M. Wood +2

Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this…

stat.ML2021

Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones

Trey McNeely, Galen Vincent, Rafael Izbicki +2

Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weat…

stat.AP2020

Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

Addison J. Hu, Mikael Kuusela, Ann B. Lee +2

Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface…

cs.LG2020★ 2 cited

Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning

Trey McNeely, Niccolò Dalmasso, Kimberly M. Wood +1

Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestibl…

stat.AP2019★ 2 cited

Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

Trey McNeely, Ann B. Lee, Kimberly M. Wood +1

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intens…