Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 10
Articles | Volume 19, issue 10
https://doi.org/10.5194/amt-19-3511-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-3511-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 10
Research article
 | 
27 May 2026
Research article |  | 27 May 2026

A guide to optimised spatiotemporal data co-location by mutual information maximisation

Andrew Steven Martin, Heather Guy, Michael Ray Gallagher, and Ryan Reynolds Neely III

Data sets

Mutual information maximisation for spatiotemporal co-location: ICESat-2 ATL09 and Cloudnet categorize Andrew Martin https://doi.org/10.5281/zenodo.17817304

Custom collection of categorize data from Hyytiälä, Jülich, Munich, and NyÅlesund between 1 Oct 2018 and 1 Jan 2025 K. Ebell et al. https://doi.org/10.60656/726097978E364D06

ATLAS/icesat-2 L3A calibrated backscatter profiles and atmospheric layer characteristics S. Palm et al. https://doi.org/10.5067/ATLAS/ATL09.006

DAndrewA/a-guide-to-optimisedspatiotemporal- data-co-location-by-mutual-informationmaximisation: v1.0.1 Andrew Martin https://doi.org/10.5281/zenodo.17830442

Interactive computing environment

DAndrewA/a-guide-to-optimised-spatiotemporal-data-co-location-by-mutual-information-maximisation: v1.0.1 Andrew Martin https://doi.org/10.5281/zenodo.17830442

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Short summary
Matching geospatial data between datasets recorded on different coordinate systems requires choosing parameters that impact the subset of data in downstream analyses. We developed a framework to optimise the choice of parameters by maximising the mutual information between the data being compared. The optimised parameters vary spatially, and using the optimised parameters results in better comparisons between data than using fixed choices of parameters.
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