Call for abstracts for an EGU session on data compression:
---------- Forwarded message ---------
From: Charlie Zender <zender@xxxxxxx>
Date: Tue, Nov 2, 2021 at 1:00 PM
Subject: Abstract solicitation for EGU22 lossy/lossless compression session
Dear Colleague,
Based on your previous work in this field, we encourage you to submit an
abstract to the session on lossy and lossless compression that we are
organizing at EGU22:
ESSI2.7: Lossy and Lossless Compression for Greener Geoscientific
Computing and Data Storage
Conveners: Charlie Zender (UC Irvine), Edward Hartnett (NOAA), Bryan N.
Lawrence (NCAS, U. Reading), V. Balaji (Princeton)
https://meetingorganizer.copernicus.org/EGU22/session/42046
[Full session description is appended below]
Confirmed Invited Speaker:
Dr. Milan Klöwer, University of Oxford
EGU22 is in Vienna April 3-8, 2022 and our session will be in fully
hybrid vPICO format to facilitate remote attendance. The submission
deadline is Jan. 12, 2022. Feel free to forward this to your colleagues
who may be interested in this session, and to contact any of the
conveners with questions.
Sincerely,
Charlie, Ed, Bryan, and Balaji
--
Charlie Zender, Earth System Sci. & Computer Sci.
University of California, Irvine 949-891-2429 )'(
ESSI2.7: Lossy and Lossless Compression for Greener Geoscientific
Computing and Data Storage
The ongoing explosion in size of geoscientific model and measurement
datasets has generally been accommodated by a combination of increased
storage space (i.e., brute force) and older lossless data compression
techniques. Newer, more efficient, and faster lossy and lossless
compression techniques can significantly mitigate storage growth and
accelerate workflows without sacrificing data of scientific value.
Reduced storage requirements lower data center power consumption and its
attendant consequences for greenhouse gas emissions and environmental
sustainability. Thus modern data compression techniques allow
researchers to analyze and/or generate more data with a greener climate
footprint.
This session invites presentations on all aspects of how geosciences can
shift towards greener computing by adopting modern data compression
techniques including, though not limited to: algorithmic advances,
assessments of geoscientific computing and data storage sustainability,
compression efficiency and speed in software and/or hardware,
implementation in MIPs (e.g., CMIP7), interoperability issues, metadata
standards (e.g., CF), remote sensing applications, and support in widely
used languages (e.g., C/C++, Fortran, Java, Julia, Python), data storage
formats (e.g., HDF, netCDF, Zarr), and Open Source workflows (e.g., CDO,
NCO, R, Xarray).
