Skip to main content
U.S. flag

An official website of the United States government

Publication Date
6 December 2017

Modeling the Contributions of Global Air Temperature, Synoptic-Scale Phenomena and Soil Moisture to Near-Surface Static Energy Variability Using Artificial Neural Networks

Print / PDF
Powerpoint Slide
Science

Built a hierarchy of machine-learning models of varying complexity to examine the relative importance of global air temperatures, daily indices of synoptic meteorology & time-averaged soil moisture (SM) to correct characterization of the variability of equivalent potential temperature (θE).

Impact

We show: Deep machine learning (non-linear) models are necessary to correctly model high intensity, high impact heat events. Excluding SM precludes our ability to model high intensity, high impact heat events. This research re-emphasizes the importance of atmosphere-surface exchange to extreme heat events in the eastern USA.

 

Summary

Our research has evolved mechanistic understanding of θE variability and illustrated the need for high-resolution modeling to characterizing extreme temperature events.

 

Point of Contact
S.C. Pryor
Institution(s)
Cornell University
Funding Program Area(s)
Publication