Evidence map›Paper›PMID 41429773›Full record

ArticleNature communications2025

Trends in vertical wind velocity variability reveal cloud microphysical feedback.

Donifan Barahona, Katherine H Breen, Derek Ngo, Flor Vanessa Maciel, Ryan Patnaude, Minghui Diao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Donifan BarahonaGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA. donifan.o.barahona@nasa.gov.ORCID http://orcid.org/0000-0001-5786-1344
Katherine H BreenGlobal Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
Derek NgoDepartment of Meteorology and Climate Science, San Jose State University, San Jose, CA, USA.
Flor Vanessa MacielDepartment of Meteorology and Climate Science, San Jose State University, San Jose, CA, USA.
Ryan PatnaudeDepartment of Meteorology and Climate Science, San Jose State University, San Jose, CA, USA.
Minghui DiaoDepartment of Meteorology and Climate Science, San Jose State University, San Jose, CA, USA.ORCID http://orcid.org/0000-0003-0324-0897

Funding

NASA | Goddard Space Flight Center (NASA's Goddard Space Flight Center) 281945.02.31.04.39NASA | Goddard Space Flight Center (NASA's Goddard Space Flight Center) NNH20ZDA001N-MAPNational Aeronautics and Space Administration (NASA) 80NSSC21K1457National Aeronautics and Space Administration (NASA) 80NSSC24K1616National Science Foundation (NSF) AGS 1642291National Science Foundation (NSF) OPP 1744965
6 · The paper itself

Abstract

By controlling supersaturation vertical air motion influences how aerosols activate into cloud droplets and ice crystals. This effect is difficult to represent accurately in atmospheric models as they cannot typically resolve the sub-kilometer scale component of wind motion, however it can be addressed by machine learning. Here we apply a generative technique combining storm-resolving simulations, observational and climate reanalysis data, to predict the spatial standard deviation in vertical wind velocity, σ

Identifiers

PMID41429773
PMCPMC12749717

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.