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NOTICE: This version of the NSF Unidata web site (archive.unidata.ucar.edu) is no longer being updated.
Current content can be found at unidata.ucar.edu.
To learn about what's going on, see About the Archive Site.
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).
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