TY - GEN
T1 - In Situ Climate Modeling for Analyzing Extreme Weather Events
AU - Dutta, Soumya
AU - Klein, Natalie
AU - Tang, Li
AU - Wolfe, Jonathan David
AU - Roekel, Luke Van
AU - Benedict, James Joseph
AU - Biswas, Ayan
AU - Lawrence, Earl
AU - Urban, Nathan
PY - 2021/11/15
Y1 - 2021/11/15
N2 - The study of many extreme weather events requires simulations with high spatiotemporal data that can grow in size quickly. Storing all the raw data from such a large-scale simulation for traditional post hoc analyses is soon going to be prohibitive as the data generation speed is outpacing the data storage capability in supercomputers. In situ analysis has emerged as a solution to this problem; data is analyzed when it is being produced, bypassing the slower disk input/output (I/O). In this work, we develop a new in situ analysis pathway for Energy Exascale Earth System Model (E3SM) and propose an algorithm for analyzing the impacts of sudden stratospheric warmings (SSWs), which can cause extreme cold temperature outbreaks at the surface, resulting in hazardous weather and disrupting many socioeconomic sectors. We detect SSWs and model the surface temperature data distributions in situ and show that post hoc analysis using the distribution models can predict the impact of SSWs in the continental United States.
AB - The study of many extreme weather events requires simulations with high spatiotemporal data that can grow in size quickly. Storing all the raw data from such a large-scale simulation for traditional post hoc analyses is soon going to be prohibitive as the data generation speed is outpacing the data storage capability in supercomputers. In situ analysis has emerged as a solution to this problem; data is analyzed when it is being produced, bypassing the slower disk input/output (I/O). In this work, we develop a new in situ analysis pathway for Energy Exascale Earth System Model (E3SM) and propose an algorithm for analyzing the impacts of sudden stratospheric warmings (SSWs), which can cause extreme cold temperature outbreaks at the surface, resulting in hazardous weather and disrupting many socioeconomic sectors. We detect SSWs and model the surface temperature data distributions in situ and show that post hoc analysis using the distribution models can predict the impact of SSWs in the continental United States.
UR - http://www.scopus.com/inward/record.url?scp=85119952595&partnerID=8YFLogxK
U2 - 10.1145/3490138.3490142
DO - 10.1145/3490138.3490142
M3 - Conference contribution
SN - 9781450387156
T3 - ACM International Conference Proceeding Series
SP - 18
EP - 23
BT - Proceedings of ISAV 2021
PB - Unknown Publisher
T2 - In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, ISAV 2021 - Held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2021
Y2 - 15 November 2021
ER -