MN-GM-METFD
| Study course: | M.Sc. Physics of Earth and Atmosphere | |||
| Module: | MN-GM-METFD | |||
| Title | Remote Sensing and Data Assimilation (Fernerkundung und Datenassimilation) | |||
| Module Number | Workload | CP | Duration | Semester |
| MN-GM-METFD | 180 h | 6 | 1 Semester | WT |
| Person in Charge | S. Crewell & H. Elbern |
| Offering Department | Institute of Geophysics and Meteorology, Cologne University |
| Applicability | Course of Study | Category | Semester | ||
| M.Sc. Physics of Earth and Atmosphere | Elective | 1 | |||
| Aims | • Understanding remote sensing principles and the determination of geophysical parameters from radiation measurements in different spectral regions • Knowledge of remote sensing instruments and the operational meteorological observation system • Knowledge of the spatial and spatio-temporal data assimilation methods | |||
| Skills | • Ability to interpret and to quantitatively analyse remote sensing observations • Assessment of statistical assumptions, numerical complexities and practical limits of retrieval and assimilation techniques • Formulation of inverse models and skills to develop adjoint codes | |||
| Content | • Remote sensing principles, meteorological satellites and orbits • Passive remote sensing of the atmosphere at visible, infrared and microwave wavelengths for temperature, humidity, clouds & aerosol • Active remote sensing of the atmosphere with cloud and precipitation radar, lidar, wind profiler, sodar and GPS • Remote sensing of the ocean (temperature, color, wind, waves) • Remote sensing of Earth Surface and vegetation (SAR, NDVI) • Basics of objective analysis and inverse modelling • Spatial data assimilation (DA) methods: optimal interpolation, 3D-var, minimization methods for data assimilation and preconditioning, multivariate data assimilation • Spatio-temporal DA methods: Kalman filter and complexity reduced variants of 4D-var, adjoint and tangent-linear modelling • Observation operators; a priori-control o fobservation; a posteriori validation techniques in data assimilation • Initialization problem (physical balance) | |||
| Prerequisites | Basics of mathematics, physics, experience in programming (mandatory) | |||
| Lectures | Form, Theme | Max. of Participants | h/week | workload | CP |
Lecture Exercise | 20 | 3 2 | 90 90 | 3 3 |
| Examinations | Form of testing and examination | Graded or not |
| Oral examination | Graded | |
| Requirements | Successful participation in the exercises | Not graded |
| Miscellaneous | Recommended literature: Kidder, S.Q. and von der Haar, T.H.; 1995: Satellite Meteorology: An Introduction, Academic Press, 466 pp. Rodgers, C.D.; 2000: Inverse methods for atmospheric sounding: Theory and practice. World Scientific, 238 pp. Bennett, A.F.; 2002: Inverse Modelling for the Ocean and Atmosphere, Cambridge University Press, 234 pp. Kalnay, E.; 2003: Atmospheric Modeling, data assimilation and predictability, Cambridge Univ. Press, 34 pp. Evensen, G.; 2010: Data Assimilation—The Ensemble Kalman Filter, Springer, Volume 42, Issue 8, 1001-1002 pp. Lahoz, W., Khattatov, B., Menard, R. (eds); 2010: Data Assimilation: Making sense of observations, Springer, 491 pp. |