Machine learning based algorithms for uncertainty quantification in numerical weather prediction models

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dc.contributor.authorMoosavi, Azamen
dc.contributor.authorRao, Vishwasen
dc.contributor.authorSandu, Adrianen
dc.date.accessioned2022-02-27T04:16:17Zen
dc.date.available2022-02-27T04:16:17Zen
dc.date.issued2021-03-01en
dc.date.updated2022-02-27T04:16:13Zen
dc.description.abstractComplex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the corresponding physical parameters during model configuration can significantly impact the accuracy of model forecasts. There is no combination of physical schemes that works best for all times, at all locations, and under all conditions. It is therefore of considerable interest to understand the interplay between the choice of physics and the accuracy of the resulting forecasts under different conditions. This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. The first problem addressed herein is the estimation of systematic model errors in output quantities of interest at future times, and the use of this information to improve the model forecasts. The second problem considered is the identification of those specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. In order to address these questions we employ two machine learning approaches, random forests and artificial neural networks. The discrepancies between model results and observations at past times are used to learn the relationships between the choice of physical processes and the resulting forecast errors. Numerical experiments are carried out with the Weather Research and Forecasting (WRF) model. The output quantity of interest is the model precipitation, a variable that is both extremely important and very challenging to forecast. The physical processes under consideration include various micro-physics schemes, cumulus parameterizations, short wave, and long wave radiation schemes. The experiments demonstrate the strong potential of machine learning approaches to aid the study of model errors.en
dc.description.versionPublished versionen
dc.format.extent11 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 101295 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.jocs.2020.101295en
dc.identifier.eissn1877-7511en
dc.identifier.issn1877-7503en
dc.identifier.orcidSandu, Adrian [0000-0002-5380-0103]en
dc.identifier.urihttp://hdl.handle.net/10919/108901en
dc.identifier.volume50en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000697573900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectComputer Science, Interdisciplinary Applicationsen
dc.subjectComputer Science, Theory & Methodsen
dc.subjectComputer Scienceen
dc.subjectNumerical weather prediction modelen
dc.subjectPrecipitation predictionen
dc.subjectPhysical processesen
dc.subjectMachine learningen
dc.subjectCONVECTIVE PARAMETERIZATIONen
dc.subjectBULK PARAMETERIZATIONen
dc.subjectDATA ASSIMILATIONen
dc.subjectERROR ESTIMATIONen
dc.subjectPRECIPITATIONen
dc.subjectMICROPHYSICSen
dc.subjectIMPACTen
dc.subject0802 Computation Theory and Mathematicsen
dc.subject0806 Information Systemsen
dc.titleMachine learning based algorithms for uncertainty quantification in numerical weather prediction modelsen
dc.title.serialJournal of Computational Scienceen
dc.typeArticleen
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Computer Scienceen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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