Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model
Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmosphe...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haslehner, Mylène [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Rechteinformationen: |
Nutzungsrecht: © 2016 Royal Meteorological Society |
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| Schlagwörter: |
convective scale data assimilation |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: Quarterly journal of the Royal Meteorological Society - Reading : Soc., 1873, 142(2016), 696, Seite 1439-1452 |
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| Übergeordnetes Werk: |
volume:142 ; year:2016 ; number:696 ; pages:1439-1452 |
| Links: |
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| DOI / URN: |
10.1002/qj.2745 |
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OLC1974608530 |
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| 520 | |a Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. | ||
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10.1002/qj.2745 doi PQ20160610 (DE-627)OLC1974608530 (DE-599)GBVOLC1974608530 (PRQ)p1175-6f0cf01217abd83b486f52d71ede26d186cf5b1725698151959579e041f2a4133 (KEY)0013343420160000142069601439testingparticlefiltersonsimpleconvectivescalemodel DE-627 ger DE-627 rakwb eng 550 DNB UA 7650 AVZ rvk Haslehner, Mylène verfasserin aut Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. Nutzungsrecht: © 2016 Royal Meteorological Society convective scale data assimilation non Gaussian data assimilation particle filter nonlinear Data assimilation Simulation Atoms & subatomic particles Janjić, Tijana oth Craig, George C oth Enthalten in Quarterly journal of the Royal Meteorological Society Reading : Soc., 1873 142(2016), 696, Seite 1439-1452 (DE-627)129079324 (DE-600)3142-2 (DE-576)014411946 0035-9009 nnns volume:142 year:2016 number:696 pages:1439-1452 http://dx.doi.org/10.1002/qj.2745 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qj.2745/abstract http://search.proquest.com/docview/1786832514 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_4012 GBV_ILN_4311 UA 7650 AR 142 2016 696 1439-1452 |
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10.1002/qj.2745 doi PQ20160610 (DE-627)OLC1974608530 (DE-599)GBVOLC1974608530 (PRQ)p1175-6f0cf01217abd83b486f52d71ede26d186cf5b1725698151959579e041f2a4133 (KEY)0013343420160000142069601439testingparticlefiltersonsimpleconvectivescalemodel DE-627 ger DE-627 rakwb eng 550 DNB UA 7650 AVZ rvk Haslehner, Mylène verfasserin aut Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. Nutzungsrecht: © 2016 Royal Meteorological Society convective scale data assimilation non Gaussian data assimilation particle filter nonlinear Data assimilation Simulation Atoms & subatomic particles Janjić, Tijana oth Craig, George C oth Enthalten in Quarterly journal of the Royal Meteorological Society Reading : Soc., 1873 142(2016), 696, Seite 1439-1452 (DE-627)129079324 (DE-600)3142-2 (DE-576)014411946 0035-9009 nnns volume:142 year:2016 number:696 pages:1439-1452 http://dx.doi.org/10.1002/qj.2745 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qj.2745/abstract http://search.proquest.com/docview/1786832514 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_4012 GBV_ILN_4311 UA 7650 AR 142 2016 696 1439-1452 |
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10.1002/qj.2745 doi PQ20160610 (DE-627)OLC1974608530 (DE-599)GBVOLC1974608530 (PRQ)p1175-6f0cf01217abd83b486f52d71ede26d186cf5b1725698151959579e041f2a4133 (KEY)0013343420160000142069601439testingparticlefiltersonsimpleconvectivescalemodel DE-627 ger DE-627 rakwb eng 550 DNB UA 7650 AVZ rvk Haslehner, Mylène verfasserin aut Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. Nutzungsrecht: © 2016 Royal Meteorological Society convective scale data assimilation non Gaussian data assimilation particle filter nonlinear Data assimilation Simulation Atoms & subatomic particles Janjić, Tijana oth Craig, George C oth Enthalten in Quarterly journal of the Royal Meteorological Society Reading : Soc., 1873 142(2016), 696, Seite 1439-1452 (DE-627)129079324 (DE-600)3142-2 (DE-576)014411946 0035-9009 nnns volume:142 year:2016 number:696 pages:1439-1452 http://dx.doi.org/10.1002/qj.2745 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qj.2745/abstract http://search.proquest.com/docview/1786832514 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_4012 GBV_ILN_4311 UA 7650 AR 142 2016 696 1439-1452 |
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10.1002/qj.2745 doi PQ20160610 (DE-627)OLC1974608530 (DE-599)GBVOLC1974608530 (PRQ)p1175-6f0cf01217abd83b486f52d71ede26d186cf5b1725698151959579e041f2a4133 (KEY)0013343420160000142069601439testingparticlefiltersonsimpleconvectivescalemodel DE-627 ger DE-627 rakwb eng 550 DNB UA 7650 AVZ rvk Haslehner, Mylène verfasserin aut Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. Nutzungsrecht: © 2016 Royal Meteorological Society convective scale data assimilation non Gaussian data assimilation particle filter nonlinear Data assimilation Simulation Atoms & subatomic particles Janjić, Tijana oth Craig, George C oth Enthalten in Quarterly journal of the Royal Meteorological Society Reading : Soc., 1873 142(2016), 696, Seite 1439-1452 (DE-627)129079324 (DE-600)3142-2 (DE-576)014411946 0035-9009 nnns volume:142 year:2016 number:696 pages:1439-1452 http://dx.doi.org/10.1002/qj.2745 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qj.2745/abstract http://search.proquest.com/docview/1786832514 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_4012 GBV_ILN_4311 UA 7650 AR 142 2016 696 1439-1452 |
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10.1002/qj.2745 doi PQ20160610 (DE-627)OLC1974608530 (DE-599)GBVOLC1974608530 (PRQ)p1175-6f0cf01217abd83b486f52d71ede26d186cf5b1725698151959579e041f2a4133 (KEY)0013343420160000142069601439testingparticlefiltersonsimpleconvectivescalemodel DE-627 ger DE-627 rakwb eng 550 DNB UA 7650 AVZ rvk Haslehner, Mylène verfasserin aut Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. Nutzungsrecht: © 2016 Royal Meteorological Society convective scale data assimilation non Gaussian data assimilation particle filter nonlinear Data assimilation Simulation Atoms & subatomic particles Janjić, Tijana oth Craig, George C oth Enthalten in Quarterly journal of the Royal Meteorological Society Reading : Soc., 1873 142(2016), 696, Seite 1439-1452 (DE-627)129079324 (DE-600)3142-2 (DE-576)014411946 0035-9009 nnns volume:142 year:2016 number:696 pages:1439-1452 http://dx.doi.org/10.1002/qj.2745 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qj.2745/abstract http://search.proquest.com/docview/1786832514 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_62 GBV_ILN_154 GBV_ILN_601 GBV_ILN_4012 GBV_ILN_4311 UA 7650 AR 142 2016 696 1439-1452 |
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Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model |
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Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. |
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Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. |
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Convective‐scale applications require data assimilation methods that can cope with nonlinear dynamics and the stochastic nature of convection. For this application, the particle filter is a promising data assimilation method because it estimates the probability density function (PDF) of the atmospheric state and not only its first two moments. However, in order to represent PDFs with a small number of particles, the particle filter is usually combined with another data assimilation technique. In this article we investigate a hybrid algorithm, the nudging proposal particle filter, which combines the sequential importance resampling particle filter with nudging. Analytic and experimental results on an idealized, nonlinear, one‐dimensional model are used to show that there exists a combination of the two methods such that the nudging proposal particle filter outperforms both of its components. In this article, a stochastic cloud model, represented through a birth–death process, serves as a first test model for the filter. The transition probability density can be calculated exactly for this model, thus providing insight into its contribution to the selection of particles during resampling. The functioning mechanism of the nudging proposal particle filter in its simplest form is investigated and the impact of the model parameters on the filter's behaviour highlighted. |
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Testing particle filters on simple convective‐scale models Part l: A stochastic cloud model |
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