EnglishIntroduction to data assimilation: Kalman filters and ensembles
Multi angle
Auteurs : Mallet, Vivien (Auteur de la Conférence)
CIRM (Editeur )
62M20 - Prediction; filtering (statistics)
62P12 - Applications of statistics to environnemental and related topics
86A05 - Hydrology, hydrography, oceanography
86A32 - Geostatistics
93E10 - Estimation and detection in stochastic control
Ressources complémentaires :
http://smai.emath.fr/cemracs/cemracs16/images/cemracs-mallet.pdf
http://mucm.aston.ac.uk/MUCM/MUCMToolkit/
https://cran.r-project.org/web/packages/DiceKriging/index.html
https://cran.r-project.org/web/packages/COBRA/index.html
CIRM (Editeur )
Loading the player...
|
Résumé :
Codes MSC : 62M20 - Prediction; filtering (statistics)
62P12 - Applications of statistics to environnemental and related topics
86A05 - Hydrology, hydrography, oceanography
86A32 - Geostatistics
93E10 - Estimation and detection in stochastic control
Ressources complémentaires :
http://smai.emath.fr/cemracs/cemracs16/images/cemracs-mallet.pdf
http://mucm.aston.ac.uk/MUCM/MUCMToolkit/
https://cran.r-project.org/web/packages/DiceKriging/index.html
https://cran.r-project.org/web/packages/COBRA/index.html
|
Informations sur la Vidéo
Langue : AnglaisDate de publication : 03/08/2016 Date de captation : 22/07/16 Sous collection : Research School arXiv category : Numerical Analysis ; Probability Domaine : Numerical Analysis & Scientific Computing ; Mathematics in Science & Technology ; Probability & Statistics Format : MP4 (.mp4) - HD Durée : 03:00:50 Audience : Researchers ; Graduate Students Download : https://videos.cirm-math.fr/2016-07-22_Mallet.mp4 
|
Informations sur la Rencontre
Nom de la rencontre : CEMRACS : Numerical challenges in parallel scientific computing - Summer school / CEMRACS : Défis numériques en calcul scientifique parallèle - Ecole d'étéOrganisateurs de la rencontre : Grigori, Laura ; Japhet, Caroline ; Moireau, Philippe ; Parnaudeau, Philippe Dates : 18/07/2016 - 26/08/2016 Année de la rencontre : 2016 URL Congrès : http://conferences.cirm-math.fr/1430.html
Données de citation
DOI : 10.24350/CIRM.V.19024503Citer cette vidéo: Mallet, Vivien (2016). Introduction to data assimilation: Kalman filters and ensembles. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19024503 URI : http://dx.doi.org/10.24350/CIRM.V.19024503 |
Bibliographie
- Anderson, J. L. (1997). The impact of dynamical constraints on the selection of initial conditions for ensemble predictions: low-order perfect model results. Monthly Weather Review, 125(11), 2969–2983 - http://dx.doi.org/10.1175/1520-0493(1997)125%3C2969:tiodco%3E2.0.co;2
- Anderson, J. L. (1996). A Method for Producing and Evaluating Probabilistic Forecasts from Ensemble Model Integrations. Journal of Climate, 9(7), 1518–1530 - http://dx.doi.org/10.1175/1520-0442(1996)009%3C1518:amfpae%3E2.0.co;2
- Bishop, C., Etherton, B., & Majumdar, S. (2001). Adaptive sampling with the ensemble transform Kalman filter, part I: theoretical aspects. Monthly Weather Review, 129(3), 420–436 - http://dx.doi.org/10.1175/1520-0493(2001)129%3C0420:aswtet%3E2.0.co;2
- Bocquet M. (2014). Introduction aux principes et méthodes de l'assimilation de données en géophysique. ParisTech/ENSTA lecture notes - http://cerea.enpc.fr/HomePages/bocquet/teaching/assim-mb.pdf
- Bouttier, F., & Courtier, P. (1999). Data assimilation concepts and methods. ECMWF Training Course Notes - http://www.ecmwf.int/sites/default/files/Data%20assimilation%20concepts%20and%20methods.pdf
- Brier, G.W. (1950). Verification of Forecasts Expressed in Terms of Probability. Monthly Weather Review, 78(1), 1–3 - http://dx.doi.org/10.1175/1520-0493(1950)078%3C0001:vofeit%3E2.0.co;2
- Bureau of Meteorology, Forecast verification - http://www.cawcr.gov.au/projects/verification/
- Chapelle, D., Fragu, M., Mallet, V., & Moireau, P. (2013). Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart. Medical & Biological Engineering & Computing, 51(11), 1221–1233 - http://dx.doi.org/10.1007/s11517-012-0969-6
- Cohn, S.E. (1997). An introduction to estimation theory. Journal of the Meteorological Society of Japan. Ser. II, 75(1B), 257–288 - https://www.jstage.jst.go.jp/article/jmsj1965/75/1B/75_1B_257/_article
- Cohn, S.E., & Todling, R. (1996). Approximate data assimilation schemes for stable and unstable dynamics. Journal of the Meteorological Society of Japan. Ser. II, 74(1), 63–75 - https://www.jstage.jst.go.jp/article/jmsj1965/74/1/74_1_63/_article
- Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research, 99(C5), 10143–10162 - http://onlinelibrary.wiley.com/doi/10.1029/94JC00572/abstract
- Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343–367 - http://dx.doi.org/10.1007/s10236-003-0036-9
- Gaspari, G., & Cohn, S.E. (1999). Construction of correlation functions in two and three dimensions. Quarterly Journal of the Royal Meteorological Society, 125(554), 723–757 - http://dx.doi.org/10.1256/smsqj.55416
- Hamill, T.M., & Colucci, S.J. (1997). Verification of Eta/RSM Short-Range Ensemble Forecasts. Monthly Weather Review, 125(6), 1312–1327 - http://dx.doi.org/10.1175/1520-0493(1997)125%3C1312:voersr%3E2.0.co;2
- Heemink, A.W., Verlaan, M., & Segers, A.J. (2001). Variance reduced ensemble Kalman filtering. Monthly Weather Review 129, 1718–1728 - http://dx.doi.org/10.1175/1520-0493(2001)129%3C1718:vrekf%3E2.0.co;2
- Hunt, B.R., Kostelich, E.J., Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter. Physica D, 230(1-2), 112–126 - http://dx.doi.org/10.1016/j.physd.2006.11.008
- Iooss, B. (2011). Revue sur l'analyse de sensibilité globale de modèles numériques. Journal de la Société Française de Statistique, 152(1), 3–25 - http://journal-sfds.fr/ojs/index.php/J-SFdS/article/view/53
- Jolliffe, I.T., & Stephenson, D.B. (Eds.) (2011). Forecast verification: A practitioner's guide in atmospheric science, 2nd ed. Chichester: Wiley-Blackwell - http://onlinelibrary.wiley.com/book/10.1002/9781119960003
- Joseph, V.R., & Kang, L. (2011). Regression-based inverse distance weighting with applications to computer experiments. Technometrics, 53(3), 254–265 - http://dx.doi.org/10.1198/tech.2011.09154
- Julier, S.J., & Uhlmann, J.K. (1997). A new extension of the Kalman filter to nonlinear systems. Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, 182-193 - http://dx.doi.org/10.1117/12.280797
- Mallet, V., Nakonechny, A., & Zhuk, S. (2013). Minimax filtering for sequential aggregation: Application to ensemble forecast of ozone analyses. Journal of Geophysical Research, 118(19), 11294–11303 - http://dx.doi.org/10.1002/jgrd.50751
- Mallet, V., (2010). Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation. Journal of Geophysical Research, 115(D24) - http://dx.doi.org/10.1029/2010jd014259
- Mallet, V., Stoltz, G., & Mauricette, B. (2009). Ozone ensemble forecast with machine learning algorithms. Journal of Geophysical Research, 114(D5) - http://dx.doi.org/10.1029/2008jd009978
- Moireau, P., & Chapelle, D. (2010). Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. ESAIM: Control, Optimisation and Calculus of Variations, 17(2), 380-405 - http://dx.doi.org/10.1051/cocv/2010006
- Pham, D.T. (2001). Stochastic methods for sequential data assimilation in strongly nonlinear systems. Monthly Weather Review, 129(5), 1194-1207 - http://dx.doi.org/10.1175/1520-0493(2001)129%3C1194:smfsda%3E2.0.co;2
- Pham, D.T., Verron, J., & Roubaud, M. C. (1998). A singular evolutive extended Kalman filter for data assimilation in oceanography. Journal of Marine Systems, 16, 323–340 - http://dx.doi.org/10.1016/S0924-7963(97)00109-7
- Rasmussen, C.E., & Williams, C.K.I. (2006). Gaussian processes for machine learning. Cambridge, Mass.: MIT Press - http://www.gaussianprocess.org/gpml/chapters/
- Sacks, J., Welch, W.J., Mitchell, T.J., & Wynn, H.P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), 409–423 - http://dx.doi.org/10.1214/ss/1177012413
- Talagrand, O., Vautard, R., & Strauss, B. (1997). Evaluation of Probabilistic Prediction System. In: Proceedings of a workshop held at ECMWF on predictability, 20–22 October 1997. European Centre for Medium-Range Weather Forecasts, pp 1–25 - http://www.ecmwf.int/sites/default/files/elibrary/1997/12555-evaluation-probabilistic-prediction-systems.pdf
- Thiébaux, H.J., Mitchell, H.L., & Shantz, D.W. (1986). Horizontal Structure of Hemispheric Forecast Error Correlations for Geopotential and Temperature. Monthly Weather Review, 114(6), 1048–1066 - http://dx.doi.org/10.1175/1520-0493(1986)114%3C1048:hsohfe%3E2.0.co;2
- Verlaan, M., & Heemink, A. (1995). Reduced rank square root filters for large scale data assimilation problems. In Second International Symposium on Assimilation of Observations in Meteorology and Oceanography, 247–252. World Meteorological Organization, WMO -
- Wilks, D.S. (2011). Statistical Methods in the Atmospheric Sciences, 3rd ed. Amsterdam: Elsevier and Academic Press - http://store.elsevier.com/Statistical-Methods-in-the-Atmospheric-Sciences/Daniel-Wilks/isbn-9780123850225/
