Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 17
Articles | Volume 18, issue 17
https://doi.org/10.5194/gmd-18-5873-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-5873-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 17
Development and technical paper
 | 
10 Sep 2025
Development and technical paper |  | 10 Sep 2025

Statistical summaries for streamed data from climate simulations: one-pass algorithms

Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ivan Alsina-Ferrer, Llorenç Lledó, Ehsan Sharifi, and Francisco Doblas-Reyes

Viewed

Total article views: 593 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
426 138 29 593 23 35
  • HTML: 426
  • PDF: 138
  • XML: 29
  • Total: 593
  • BibTeX: 23
  • EndNote: 35
Views and downloads (calculated since 06 Feb 2025)
Cumulative views and downloads (calculated since 06 Feb 2025)

Viewed (geographical distribution)

Total article views: 593 (including HTML, PDF, and XML) Thereof 593 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Sep 2025
Download
Short summary
We present One_Pass (v0.8.0), a Python package enabling computation of statistics from streamed global climate model output using one-pass algorithms. Users often need statistics covering periods longer than the stream duration, requiring algorithms that do not store full time series. One-pass methods address this need while avoiding full data archiving, offering memory-efficient, accurate results for high-performance computing (HPC) workflows and downstream applications like bias adjustment.
Read more
Share