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Initial state perturbations in ensemble forecasting
Stockholm University, Faculty of Science, Department of Meteorology.
Stockholm University, Faculty of Science, Department of Meteorology.
Stockholm University, Faculty of Science, Department of Meteorology.
2008 (English)In: Nonlinear processes in geophysics, ISSN 1023-5809, E-ISSN 1607-7946, Vol. 15, no 5, 751-759 p.Article in journal (Refereed) Published
Abstract [en]

Due to the chaotic nature of atmospheric dynamics, numerical weather prediction systems are sensitive to errors in the initial conditions. To estimate the forecast uncertainty, forecast centres produce ensemble forecasts based on perturbed initial conditions. How to optimally perturb the initial conditions remains an open question and different methods are in use. One is the singular vector (SV) method, adapted by ECMWF, and another is the breeding vector (BV) method (previously used by NCEP). In this study we compare the two methods with a modified version of breeding vectors in a low-order dynamical system (Lorenz-63). We calculate the Empirical Orthogonal Functions (EOF) of the subspace spanned by the breeding vectors to obtain an orthogonal set of initial perturbations for the model. We will also use Normal Mode perturbations. Evaluating the results, we focus on the fastest growth of a perturbation. The results show a large improvement for the BV-EOF perturbations compared to the non-orthogonalised BV. The BV-EOF technique also shows a larger perturbation growth than the SVs of this system, except for short time-scales. The highest growth rate is found for the second BV-EOF for the long-time scale. The differences between orthogonal and non-orthogonal breeding vectors are also investigated using the ECMWF IFS-model. These results confirm the results from the Loernz-63 model regarding the dependency on orthogonalisation

Place, publisher, year, edition, pages
2008. Vol. 15, no 5, 751-759 p.
National Category
Meteorology and Atmospheric Sciences
Identifiers
URN: urn:nbn:se:su:diva-15000ISI: 000260557800004OAI: oai:DiVA.org:su-15000DiVA: diva2:181520
Available from: 2009-01-13 Created: 2009-01-13 Last updated: 2017-12-13Bibliographically approved
In thesis
1. Sampling uncertainties in ensemble weather forecasting
Open this publication in new window or tab >>Sampling uncertainties in ensemble weather forecasting
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of ensemble weather forecasting is to provide probability forecasts for the occurrence of meteorological events. The ensembles are constructed by assembling several forecast realisations, each member of the ensemble being constructed to sample the uncertainties in the forecast. These originate from uncertainties in the initial conditions (the analysis) and imperfections of the numerical model. 

 In order to sample the initial uncertainties several techniques have been proposed. The singular-vector technique yields perturbations optimised to maximize the perturbation growth over a finite time interval, whereas the breeding method recycles the perturbations from the previous ensemble in order to sample growing modes.  The ensemble-transform method represents a further development of the breeding method. Here, to create initial perturbations independent of the current flow situation of the atmosphere, random perturbations are introduced by using the difference between two randomly chosen atmospheric states (i.e. analyses). The method produces dynamically balanced perturbations denoted Random Field perturbations (RF). 

 Our results show that the RF perturbations initially have the same dynamical properties as the variability of the atmosphere. After integration over a day the perturbations from all three methods (RF, singular vectors and ensemble transform perturbations) converge. The skill scores indicate a statistically significant advantage for the RF method during the first 2-3 days for most of the evaluated parameters. Over the medium range (3-8 days) the differences are very small. We also discuss the influence of the asymptotic variability of the forecasting model on the ensemble properties.

 

Place, publisher, year, edition, pages
Stockholm: Department of Meteorology, Stockholm University, 2009. 52 p.
Keyword
Ensemble forecasting, Initial perturbation techniques
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:su:diva-27021 (URN)978-91-7155-865-7 (ISBN)
Public defence
2009-06-05, William-Olssonsalen, Geovetenskapens hus, Svante Arrhenius väg 8 A, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2009-05-14 Created: 2009-04-22 Last updated: 2009-04-22Bibliographically approved

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