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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

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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).


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.



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
Cornell University
Funding Program Area(s)