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On the Convective-Scale Predictability of the Atmosphere
Stockholm University, Faculty of Science, Department of Meteorology .
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

A well-represented description of convection in weather and climate models is essential since convective clouds strongly influence the climate system. Convective processes interact with radiation, redistribute sensible and latent heat and momentum, and impact hydrological processes through precipitation. Depending on the models’ horizontal resolution, the representation of convection may look very different. However, the convective scales not resolved by the model are traditionally parameterized by an ensemble of non-interacting convective plumes within some area of uniform forcing, representing the “large scale”. A bulk representation of the mass-flux associated with the individual plumes in the defined area provide the statistical effect of moist convection on the atmosphere. Studying the characteristics of the ECMWF ensemble prediction system it is found that the control forecast of the ensemble system is not variable enough in order to yield a sufficient spread using an initial perturbation technique alone. Such insufficient variability may be addressed in the parameterizations of, for instance, cumulus convection where the sub-grid variability in space and time is traditionally neglected. Furthermore, horizontal transport due to gravity waves can act to organize deep convection into larger scale structures which can contribute to an upscale energy cascade. However, horizontal advection and numerical diffusion are the only ways through which adjacent model grid-boxes interact in the models. The impact of flow dependent horizontal diffusion on resolved deep convection is studied, and the organization of convective clusters is found very sensitive to the method of imposing horizontal diffusion. However, using numerical diffusion in order to represent lateral effects is undesirable. To address the above issues, a scheme using cellular automata in order to introduce lateral communication, memory and a stochastic representation of the statistical effects of cumulus convection is implemented in two numerical weather models. The behaviour of the scheme is studied in cases of organized convective squall-lines, and initial model runs show promising improvements.

Place, publisher, year, edition, pages
Stockholm: Department of Meteorology, Stockholm University , 2012. , 45 p.
Keyword [en]
Cumulus convection, cellular automata, model uncertainty, sub-grid scale processes, numerical weather prediction
National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
URN: urn:nbn:se:su:diva-75195ISBN: 978-91-7447-494-7 (print)OAI: oai:DiVA.org:su-75195DiVA: diva2:515803
Public defence
2012-05-25, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted. 

Available from: 2012-05-03 Created: 2012-04-11 Last updated: 2013-04-10Bibliographically approved
List of papers
1. Independent Estimations of the Asymptotic Variability in an Ensemble Forecast System
Open this publication in new window or tab >>Independent Estimations of the Asymptotic Variability in an Ensemble Forecast System
2008 (English)In: Monthly Weather Review, ISSN 0027-0644, E-ISSN 1520-0493, Vol. 136, no 11, 4105-4112 p.Article in journal (Refereed) Published
Abstract [en]

One desirable property within an ensemble forecast system is to have a one-to-one ratio between the root-mean-square error (rmse) of the ensemble mean and the standard deviation of the ensemble (spread). The ensemble spread and forecast error within the ECMWF ensemble prediction system has been extrapolated beyond 10 forecast days using a simple model for error growth. The behavior of the ensemble spread and the rmse at the time of the deterministic predictability are compared with derived relations of rmse at the infinite forecast length and the characteristic variability of the atmosphere in the limit of deterministic predictability. Utilizing this methodology suggests that the forecast model and the atmosphere do not have the same variability, which raises the question of how to obtain a perfect ensemble.

Keyword
Ensembles, Forecasting techniques
National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
urn:nbn:se:su:diva-14999 (URN)10.1175/2008MWR2526.1 (DOI)000260861900006 ()
Available from: 2009-01-13 Created: 2009-01-13 Last updated: 2012-04-23Bibliographically approved
2. Impact of flow-dependent horizontal diffusion on resolved convectionin AROME.
Open this publication in new window or tab >>Impact of flow-dependent horizontal diffusion on resolved convectionin AROME.
2012 (English)In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 51, no 1, 54-67 p.Article in journal (Refereed) Published
Abstract [en]

Horizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permittingmodels, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve themodel skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamicalmodel fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flowdependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but amore physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
urn:nbn:se:su:diva-75188 (URN)10.1175/JAMC-D-11-032.1 (DOI)000299395100005 ()
Available from: 2012-04-11 Created: 2012-04-11 Last updated: 2017-12-07Bibliographically approved
3. Large-Scale Dynamical Response to Subgrid-Scale Organization Provided by Cellular Automata
Open this publication in new window or tab >>Large-Scale Dynamical Response to Subgrid-Scale Organization Provided by Cellular Automata
2011 (English)In: Journal of Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469, Vol. 68, no 12, 3132-3144 p.Article in journal (Refereed) Published
Abstract [en]

Due to the limited resolution of numerical weather prediction (NWP) models, sub-grid scale physical processes are parameterized, and represented by grid-box means. However, some physical processes are better represented by a mean and its variance, a typical example being deep convection, with scales varying from individual updraughts to organized meso-scale systems. In this study, we investigate, in an idealized setting, whether a cellular automaton (CA) can be used in order to enhance sub-grid scale organization by forming clusters representative of the convective scales, and yield a stochastic representation of sub-grid scale variability. We study the transfer of energy from the convective to the larger atmospheric scales through nonlinear wave interactions. This is done using a shallow water (SW) model initialized with equatorial wave modes. By letting a CA act on a finer resolution than that of the SW model, it can be expected to mimic the effect of, for instance, gravity wave propagation on convective organization. Employing the CA-scheme allows to reproduce the observed behaviour of slowing down equatorial Kelvin modes in convectively active regions, while random perturbations fail to feed back on the large-scale flow. The analysis of kinetic energy spectra demonstrates that the CA sub-grid scheme introduces energy back-scatter from the smallest model scales to medium scales. However, the amount of energy back-scattered depends almost solely on the memory time scale introduced to the sub-grid scheme, whereas any variation in spatial scales generated does not influence the energy spectra markedly.

Keyword
Convective parameterization, Model errors, Parameterization, Stochastic models, Subgrid-scale processes
National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
urn:nbn:se:su:diva-62376 (URN)10.1175/JAS-D-10-05028.1 (DOI)000298205400021 ()
Available from: 2011-09-16 Created: 2011-09-16 Last updated: 2017-12-08Bibliographically approved
4. A stochastic parameterization for deep convection using cellularautomata
Open this publication in new window or tab >>A stochastic parameterization for deep convection using cellularautomata
(English)In: Journal of Atmospheric Sciences, ISSN 0022-4928, E-ISSN 1520-0469Article in journal (Refereed) Submitted
National Category
Meteorology and Atmospheric Sciences
Research subject
Atmospheric Sciences and Oceanography
Identifiers
urn:nbn:se:su:diva-75192 (URN)
Available from: 2012-04-11 Created: 2012-04-11 Last updated: 2017-12-07Bibliographically approved

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