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Sampling uncertainties in ensemble weather forecasting
Stockholm University, Faculty of Science, Department of Meteorology.
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 [en]
Ensemble forecasting, Initial perturbation techniques
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
URN: urn:nbn:se:su:diva-27021ISBN: 978-91-7155-865-7 (print)OAI: oai:DiVA.org:su-27021DiVA: diva2:212419
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
List of papers
1. Initial state perturbations in ensemble forecasting
Open this publication in new window or tab >>Initial state perturbations in ensemble forecasting
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

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:su:diva-15000 (URN)000260557800004 ()
Available from: 2009-01-13 Created: 2009-01-13 Last updated: 2017-12-13Bibliographically approved
2. Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System
Open this publication in new window or tab >>Comparison between Singular Vectors and Breeding Vectors as Initial Perturbations for the ECMWF Ensemble Prediction System
2008 (English)In: Monthly Weather Review, ISSN 0027-0644, E-ISSN 1520-0493, Vol. 136, no 11, 4092-4104 p.Article in journal (Refereed) Published
Abstract [en]

In this paper a study aimed at comparing the perturbation methodologies based on the singular vector ensemble prediction system (SV-EPS) and the breeding vector ensemble prediction system (BV-EPS) in the same model environment is presented. A simple breeding system (simple BV-EPS) as well as one with regional rescaling dependent on an estimate of the analysis error variance (masked BV-EPS) were used. The ECMWF Integrated Forecast System has been used and the three experiments are compared for 46 forecast cases between 1 December 2005 and 15 January 2006. By studying the distribution of the perturbation energy it was possible to see large differences between the experiments initially, but after 48 h the distributions have converged. Using probabilistic scores, these results show that SV-EPS has a somewhat better performance for the Northern Hemisphere compared to BV-EPS. For the Southern Hemisphere masked BV-EPS and SV-EPS yield almost equal results. For the tropics the masked breeding ensemble shows the best performance during the first 6 days. One reason for this is the current setup of the singular vector ensemble at ECMWF yielding in general very low initial perturbation amplitudes in the tropics.

National Category
Meteorology and Atmospheric Sciences
Identifiers
urn:nbn:se:su:diva-14998 (URN)10.1175/2008MWR2498.1 (DOI)000260861900005 ()
Available from: 2009-01-13 Created: 2009-01-13 Last updated: 2017-12-13Bibliographically approved
3. 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: 2017-12-13Bibliographically approved
4. Flow-dependent versus flow-independent initial perturbations for ensemble forecasting
Open this publication in new window or tab >>Flow-dependent versus flow-independent initial perturbations for ensemble forecasting
2009 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 61A, no 2, 194-209 p.Article in journal (Refereed) Published
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:su:diva-27014 (URN)10.1111/j.1600-0870.2008.00385.x (DOI)000263258300002 ()
Available from: 2009-04-22 Created: 2009-04-22 Last updated: 2017-12-13Bibliographically approved
5. The effect of model formulation on Ensemble Transform perturbations
Open this publication in new window or tab >>The effect of model formulation on Ensemble Transform perturbations
(English)Manuscript (Other academic)
National Category
Meteorology and Atmospheric Sciences
Research subject
Meteorology
Identifiers
urn:nbn:se:su:diva-27016 (URN)
Available from: 2009-04-22 Created: 2009-04-22 Last updated: 2010-01-14Bibliographically approved

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