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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
The aim of this course is to get a knowledge of the objective analysis of meteorological data and data assimilation into numerical weather prediction models. |
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Last update: doc. Mgr. Jiří Mikšovský, Ph.D. (19.10.2017)
Student se musí orientovat v použití metod objektivní analýzy a asimilace dat. Nezbytnou podmínkou je umět metody konkrétně aplikovat na jednoduchých datech. Znalosti se ověřují pomocí testů. |
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
(1) Daley R., 1993: Atmospheric data analysis. Cambridge University Press. 457 str.
(2) Kalnay E., 2002: Atmospheric modeling, data assimilation and predictability. Cambridge University Press. 341 str.
(3) Lorenc A., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc. 112, 1177-1194.
(4) Lorenc A., 1988: Optimal nonlinear objective analysis. Quart. J. Roy. Meteor. Soc. 114, 205-240.
(5) Lewis J.M., Lakshmivarahan S., Dhall S.K., 2006: Dynamic data assimilation: a least squares approach. Cambridge University Press. 654 str.
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
Besides mathematical descriptions of the methods the course is focussed on their practical applications. The attributes of particular methods are illustrated by examples. The texts are available in Microsoft Office PowerPoint.
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
Students should be able to apply methods of an objective analysis and data assimilation to the solution of real problems. They should pass tests, which the methods are applied to simple data. |
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
This course deals with the objective analysis of meteorological data and with data assimilation into numerical weather prediction models. The overview of the methods is aimed at currently used approaches. Besides mathematical description of the methods the course is focussed on their practical application. The attributes of particular methods are illustrated by examples. As a part of the objective analysis the data check procedures are also discussed and basic principles are demonstrated. The methods of data assimilation are described and their relationship to the methods of objective analysis is shown. The aim of the course is to provide an overview of current state-of-art of objective analysis and data assimilation. The texts are available in Microsoft Office PowerPoint.
1. Basic concepts Interpolation and extrapolation, objective analysis of meteorological data, data assimilation into numerical weather prediction models, quality control of observations.
2. Methods of objective analysis Basic methods: polynomial interpolation (extrapolation), correction methods (general correction method, Cressman's method, Barnes's method), optimal (statistical) interpolation, variational method 3D VAR.
3. Optimal interpolation and 3D VAR Derivation of the methods and their numerical applications. The multivariate analysis of geopotential height and horizontal wind components.
4. Data assimilation Overview of the methods (optimal interpolation, nudging, Kalman filter, 3D-Var and 4D-Var) and their attributes. Relationships among the methods of objective analysis and data assimilation.
5. Special methods focused on the interpolation of meteorological data Methods for the interpolation of measured rainfall sums. Methods for wind interpolation in the boundary layer.
6. Quality control of observations Methods and algorithms applied to quality control of observed data. |
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Last update: SOKOL/MFF.CUNI.CZ (29.04.2008)
Basic knowledge of mathematical analysis, numerical methods, knowledge gained from lecture "Synoptic Meteorology I and II" and "Analysis of weather charts I" is supposed. |