Evidence map›Paper›PMID 42236821›Full record

ArticleScientific reports2026

Intercomparison case study of data-driven reconstructions of a cloud-obscured Saharan dust plume in Europe.

Franz Kanngießer, Stephanie Fiedler

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Franz KanngießerInstitute of Geophysics and Meteorology, Universitsy of Cologne, Pohligstr. 3, 50969, Cologne, Germany. fkanngiesser@geomar.de.
Stephanie FiedlerInstitute of Geophysics and Meteorology, Universitsy of Cologne, Pohligstr. 3, 50969, Cologne, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On 15 March 2022 an intense, but partially cloud-obscured, Saharan dust plume was transported towards Europe by an atmospheric river. Ten-year statistics of dust plumes co-occurring with cloud cover over Europe highlights that regionally up to 100% of dust plumes are obscured by clouds, which poses challenges for reconstructing dust plumes from satellite images. The European dust case on 15 March 2022 is used to investigate whether data-driven machine-learning techniques for restoring the spatial extent of dust plumes in SEVIRI satellite images can be alleviated by exploiting the rich ground-based data sets in Europe. Satellite images were paired with different combinations of ground-based observation data, stemming from ground-based remote sensing, weather reports and measurements of particulate matter, using a k-nearest neighbours approach. Combining ceilometer and photometer data with satellite images added the most value for restoring the dust plume extent and are recommended for future reconstructions of cloud-obscured dust plumes.

Indexed as

Machine learningMineral dust aerosolObservational dataReconstructing information

Identifiers

PMID42236821
PMCPMC13234271

What OpenQuestion holds

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Registered trials

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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.