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dc.contributor.authorDritschel, D. G.
dc.contributor.authorBöing, S. J.
dc.contributor.authorParker, D. J.
dc.contributor.authorBlyth, A. M.
dc.date.accessioned2018-10-19T08:30:07Z
dc.date.available2018-10-19T08:30:07Z
dc.date.issued2018-10-04
dc.identifier.citationDritschel , D G , Böing , S J , Parker , D J & Blyth , A M 2018 , ' The moist parcel-in-cell method for modelling moist convection ' , Quarterly Journal of the Royal Meteorological Society , vol. 144 , no. 715 , pp. 1695-1718 . https://doi.org/10.1002/qj.3319en
dc.identifier.issn0035-9009
dc.identifier.otherPURE: 252962614
dc.identifier.otherPURE UUID: 04311333-8bd4-44c6-8405-c67fb11c5657
dc.identifier.otherScopus: 85054542794
dc.identifier.otherWOS: 000448651000002
dc.identifier.otherORCID: /0000-0001-6489-3395/work/64697821
dc.identifier.urihttps://hdl.handle.net/10023/16286
dc.descriptionThe authors gratefully acknowledge support for this research from the EPSRC Maths Foresees Network. The numerical model development was carried out under the grant “A prototype vortex-in-cell algorithm for modelling moist convection” from March to October 2016. Steven Böing is partially funded through the NERC/Met Office Joint Programme on Understanding and Representing Atmospheric Convection across Scales (GENESIS, grant number NE/N013840/1). Doug Parker is supported by a Royal Society Wolfson Research Merit Award.en
dc.description.abstractWe describe a promising alternative approach to modelling moist convection and cloud development in the atmosphere. Rather than using a conventional grid‐based approach, we use Lagrangian “parcels” to represent key dynamical and thermodynamical variables. In the prototype model considered, parcels carry vorticity, mass, specific humidity, and liquid‐water potential temperature. In this first study, we ignore precipitation, and many of these parcel “attributes” remain unchanged (i.e. are materially conserved). While the vorticity does change following the parcel motion, the vorticity tendency is readily computed and, crucially, unwanted numerical diffusion can be avoided. The model, called “Moist Parcel‐In‐Cell” (MPIC), is a hybrid approach which uses both parcels and a fixed underlying grid for efficiency: advection (here moving parcels) is Lagrangian whereas inversion (determining the velocity field) is Eulerian. The parcel‐based representation of key variables has several advantages: (a) it allows an explicit subgrid representation; (b) it provides a velocity field which is undamped by numerical diffusion all the way down to the grid scale; (c) it does away with the need for eddy viscosity parametrizations and, in their place, it provides for a natural subgrid parcel mixing; (d) it is exactly conservative (i.e. there can be no net loss or gain of any theoretically conserved attribute); and (e) it dispenses with the need to have separate equations for each conserved parcel attribute; attributes are simply labels carried by each parcel. Moreover, the latter advantage increases as more attributes are added, such as the distributions of microphysical properties, chemical composition and aerosol loading.    
dc.language.isoeng
dc.relation.ispartofQuarterly Journal of the Royal Meteorological Societyen
dc.rightsCopyright © 2018 The Authors. Quarterly Journal of the Royal Meteorological Society published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectCloudsen
dc.subjectConvectionen
dc.subjectNumerical methoden
dc.subjectGE Environmental Sciencesen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subject.lccGEen
dc.subject.lccQA75en
dc.titleThe moist parcel-in-cell method for modelling moist convectionen
dc.typeJournal articleen
dc.contributor.sponsorEPSRCen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.identifier.doihttps://doi.org/10.1002/qj.3319
dc.description.statusPeer revieweden
dc.date.embargoedUntil2018-10-04
dc.identifier.grantnumberRG.MATH.103301en


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