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DTSTART:19700308T020000
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DTSTAMP:20181221T160727Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181111T154000
DTEND;TZID=America/Chicago:20181111T155600
UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd104@linklings.com
SUMMARY:A Statistical Analysis of Compressed Climate Model Data
DESCRIPTION:Workshop\nData Management, Hot Topics, Scientific Computing, W
 orkshop Reg Pass\n\nA Statistical Analysis of Compressed Climate Model Dat
 a\n\nPoppick, Nardi, Feldman, Baker, Hammerling\n\nThe data storage burden
  resulting from large climate model simulations continues to grow. While l
 ossy data compression methods can alleviate this burden, they introduce th
 e possibility that key climate variables could be altered to the point of 
 affecting scientific conclusions. Therefore, developing a detailed underst
 anding of how compressed model output differs from the original is importa
 nt. Here, we evaluate the effects of two leading compression algorithms, S
 Z and ZFP, on daily surface temperature and precipitation rate data from a
  popular climate model. While both algorithms show promising fidelity with
  the original output, detectable artifacts are introduced even at relative
 ly low error tolerances. This study highlights the need for evaluation met
 hods that are sensitive to errors at different spatiotemporal scales and s
 pecific to the particular climate variable of interest, with the ultimate 
 goal to improve lossy compression collaboratively with the algorithm devel
 opment teams.
URL:https://sc18.supercomputing.org/presentation/?id=ws_drbsd104&sess=sess
 143
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