09 November 2018

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

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.

 

Contact
S.C. Pryor
Cornell University
Publications
Pryor, S, R Sullivan, and J Shepherd.  2018.  "Modeling the Contributions of Global Air Temperature, Synoptic-Scale Phenomena and Soil Moisture to Near-Surface Static Energy Variability Using Artificial Neural Networks."  Atmospheric Chemistry and Physics 17(23): 14457-14471, doi:10.5194/acp-17-14457-2017.