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

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

doi:10.5194/gmd-18-5873-2025

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

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Month	HTML	PDF	XML	Total
Feb 2025	192	32	12	236
Mar 2025	142	58	10	210
Apr 2025	48	28	8	84
May 2025	78	40	4	122
Jun 2025	84	16	18	118
Jul 2025	58	28	4	90
Aug 2025	250	100	0	350
Sep 2025	1,502	32	19	1,553
Oct 2025	186	61	15	262
Nov 2025	163	58	17	238
Dec 2025	178	156	23	357
Jan 2026	199	92	46	337
Feb 2026	193	105	16	314
Mar 2026	142	129	20	291
Apr 2026	193	73	8	274
May 2026	160	64	8	232
Jun 2026	10	4	1	15

Cumulative views and downloads (calculated since 06 Feb 2025)

Month	HTML	PDF	XML	Total
Feb 2025	192	32	12	236
Mar 2025	142	58	10	210
Apr 2025	48	28	8	84
May 2025	78	40	4	122
Jun 2025	84	16	18	118
Jul 2025	58	28	4	90
Aug 2025	250	100	0	350
Sep 2025	1,502	32	19	1,553
Oct 2025	186	61	15	262
Nov 2025	163	58	17	238
Dec 2025	178	156	23	357
Jan 2026	199	92	46	337
Feb 2026	193	105	16	314
Mar 2026	142	129	20	291
Apr 2026	193	73	8	274
May 2026	160	64	8	232
Jun 2026	10	4	1	15

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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.