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DTSTART:19700308T020000
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DTSTAMP:20260522T150117Z
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:Andrew Poppick, Joseph Nardi, and Noah Feldman (Carleton Colle
 ge) and Allison Baker and Dorit Hammerling (National Center for Atmospheri
 c Research)\n\nThe data storage burden resulting from large climate model 
 simulations continues to grow. While lossy data compression methods can al
 leviate this burden, they introduce the possibility that key climate varia
 bles could be altered to the point of affecting scientific conclusions. Th
 erefore, developing a detailed understanding of how compressed model outpu
 t differs from the original is important. Here, we evaluate the effects of
  two leading compression algorithms, SZ and ZFP, on daily surface temperat
 ure and precipitation rate data from a popular climate model. While both a
 lgorithms show promising fidelity with the original output, detectable art
 ifacts are introduced even at relatively low error tolerances. This study 
 highlights the need for evaluation methods that are sensitive to errors at
  different spatiotemporal scales and specific to the particular climate va
 riable of interest, with the ultimate goal to improve lossy compression co
 llaboratively with the algorithm development teams.\n\nTag: Data Managemen
 t, Hot Topics, Scientific Computing\n\nRegistration Category: Workshop Reg
  Pass\n\n
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