Premium
A stochastic parametrization for deep convection using cellular automata
Author(s) -
Bengtsson Lisa,
Steinheimer Martin,
Bechtold Peter,
Geleyn JeanFrançois
Publication year - 2013
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2108
Subject(s) - parametrization (atmospheric modeling) , convection , squall line , advection , meteorology , convective available potential energy , deep convection , numerical weather prediction , grid , statistical physics , computer science , geology , physics , geodesy , quantum mechanics , thermodynamics , radiative transfer
A cellular automaton (CA) is introduced to the deep convection parametrization of the high‐resolution limited‐area model Aire Limitée Adaptation/Application de la Recherche à l'Opérationnel (ALARO). The self‐organizational characteristics of the CA allow for lateral communication between adjacent numerical weather prediction (NWP) model grid boxes and add additional memory to the deep convection scheme. The CA acts in two horizontal dimensions, with finer grid spacing than the NWP model. It is randomly seeded in regions where convective available potential energy (CAPE) exceeds a threshold value. Both deterministic and probabilistic rules, coupled to the large‐scale wind, are explored to evolve the CA in time. Case studies indicate that the scheme has the potential to organize cells along convective squall lines and enhance advective effects. An ensemble of forecasts using the present CA scheme demonstrated an ensemble spread in the resolved wind field in regions where deep convection is large. Such a spread represents the uncertainty due to subgrid variability of deep convection and could be an interesting addition to an ensemble prediction system.