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

Data sets

nextGEMS cycle3 datasets: statistical summaries for streamed data from climate simulations Katherine Grayson https://doi.org/10.5281/zenodo.12533197

nextGEMS: output of the model development cycle 3 simulations for ICON and IFS Nikolay Koldunov et al. https://doi.org/10.26050/WDCC/nextGEMS_cyc3

Model code and software

kat-grayson/one_pass_algorithms_paper: v0.5.0 (v0.5) Ivan Alsina-Ferrer and kat-grayson https://doi.org/10.5281/zenodo.15439803

DestinE-Climate-DT/one_pass: v0.8.0 (v0.8.0) Ivan Alsina-Ferrer and kat-grayson https://doi.org/10.5281/zenodo.15438184

Interactive computing environment

kat-grayson/one_pass_algorithms_paper K. Grayson https://doi.org/10.5281/zenodo.15439803

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