Predicting Cloud-To-Ground Lightning in the Western United States From the Large-Scale Environment Using Explainable Neural Networks.

Dmitri A Kalashnikov, Frances V Davenport, Zachary M Labe, Paul C Loikith, John T Abatzoglou, Deepti Singh

Abstract read

In one paragraph

Article in Journal of geophysical research. Atmospheres : JGR, 2024. 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.

Predicting Cloud-To-Ground Lightning in the Western United States From the Large-Scale Environment Using Explainable Neural Networks.Journal of geophysical research. Atmospheres : JGR · 2024
Article

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.

Dmitri A KalashnikovSchool of the Environment Washington State University Vancouver WA USA.ORCID https://orcid.org/0000-0002-2777-4909

Frances V DavenportCivil and Environmental Engineering Colorado State University Fort Collins CO USA.ORCID https://orcid.org/0000-0002-3061-2062

Zachary M LabeAtmospheric and Oceanic Sciences Program Princeton University Princeton NJ USA.ORCID https://orcid.org/0000-0002-6394-7651

Paul C LoikithDepartment of Geography Portland State University Portland OR USA.ORCID https://orcid.org/0000-0002-2352-0565

John T AbatzoglouManagement of Complex Systems Department University of California, Merced Merced CA USA.ORCID https://orcid.org/0000-0001-7599-9750

Deepti SinghSchool of the Environment Washington State University Vancouver WA USA.ORCID https://orcid.org/0000-0001-6568-435X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud-to-ground (CG) lightning across the WUS during June-September from the spatial patterns of seven large-scale meteorological variables from reanalysis (1995-2022). Individually trained CNN models at each 1° × 1° grid cell (

Indexed as

lightningmachine learningneural networkpredictionwildfires

Identifiers

PMID39582578

DOI10.1029/2024JD042147

PMCPMC11583119

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.