A deep-learning model to predict thunderstorms within 400 km2 South Texas domains

dc.contributor.authorkamangir, hamid
dc.contributor.authorCollins, Waylon
dc.contributor.authorTissot, Philippe
dc.contributor.authorKing, Scott
dc.creator.orcidhttps://orcid.org/ 0000-0002-2954-2378en_US
dc.creator.orcidhttps://orcid.org/ 0000-0001-9718-7518en_US
dc.creator.orcidhttps://orcid.org/ 0000-0002-4022-0388en_US
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/0000-0001-9718-7518
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/0000-0001-9718-7518
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388https://orcid.org/ 0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/ 0000-0001-9718-7518
dc.creator.orcidhttps://orcid.org/ 0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/0000-0001-9718-7518
dc.creator.orcidhttps://orcid.org/0000-0002-4022-0388
dc.creator.orcidhttps://orcid.org/ 0000-0002-2954-2378
dc.creator.orcidhttps://orcid.org/ 0000-0001-9718-7518
dc.creator.orcidhttps://orcid.org/ 0000-0002-4022-0388
dc.date.accessioned2022-02-22T20:56:25Z
dc.date.available2022-02-22T20:56:25Z
dc.date.issued2020-04-13
dc.description.abstractA deep-learning neural network (DLNN) model was developed to predict thunderstorm occurrence within 400 km2 South Texas domains for up to 15 hr (±2 hr accuracy) in advance. The input features were chosen primarily from numerical weather prediction model output parameters/variables; cloud-to-ground lightning served as the target. The deep-learning technique used was the stacked denoising autoencoder (SDAE) in order to create a higher order representation of the features. Logistic regression was then applied to the SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models, a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same data set.en_US
dc.description.abstractA deep-learning neural network (DLNN) model was developed to predict thunderstorm occurrence within 400 km2 South Texas domains for up to 15 hr (±2 hr accuracy) in advance. The input features were chosen primarily from numerical weather prediction model output parameters/variables; cloud-to-ground lightning served as the target. The deep-learning technique used was the stacked denoising autoencoder (SDAE) in order to create a higher order representation of the features. Logistic regression was then applied to the SDAE output to train the predictive model. An iterative technique was used to determine the optimal SDAE architecture. The performance of the optimized DLNN classifiers exceeded that of the corresponding shallow neural network models, a classifier via a combination of principal component analysis and logistic regression, and operational weather forecasters, based on the same data set.
dc.identifier.citationKamangir, H., Collins, W., Tissot, P. and King, S.A., 2020. A deep‐learning model to predict thunderstorms within 400 km2 South Texas domains. Meteorological Applications, 27(2), p.e1905.en_US
dc.identifier.citationKamangir, H., Collins, W., Tissot, P. and King, S.A., 2020. A deep‐learning model to predict thunderstorms within 400 km2 South Texas domains. Meteorological Applications, 27(2), p.e1905.
dc.identifier.doihttps://doi.org/10.1002/met.1905
dc.identifier.urihttps://hdl.handle.net/1969.6/90184
dc.language.isoen_USen_US
dc.language.isoen_US
dc.publisherRoyal Meteorological Societyen_US
dc.publisherRoyal Meteorological Society
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectdeep-learningen_US
dc.subjectdeep-learningen_US
dc.subjectthunderstormsen_US
dc.subjectpredictionen_US
dc.subjectsouth texasen_US
dc.subjectnumerical weather predictionen_US
dc.subjectstacked denoising autoencoderen_US
dc.subjectthunderstorm predictionen_US
dc.subjectdeep-learning
dc.subjectdeep-learning
dc.subjectthunderstorms
dc.subjectprediction
dc.subjectsouth texas
dc.subjectnumerical weather prediction
dc.subjectstacked denoising autoencoder
dc.subjectthunderstorm prediction
dc.titleA deep-learning model to predict thunderstorms within 400 km2 South Texas domainsen_US
dc.titleA deep-learning model to predict thunderstorms within 400 km2 South Texas domains
dc.typeArticleen_US
dc.typeArticle

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