Environmental Sensing and Modeling (Lecture/Exercise and Project)

Lecturer:

Prof. Jia Chen,
Dr. Andreas Luther

Tutor:

M. Sc. Adrian Wenzel

TUM Online:

https://campus.tum.de/tumonline/ee/ui/ca2/app/desktop/#/slc.tm.cp/student/courses/950570280?$ctx=design=ca;lang=en

Offered in:

Summer and winter term

Hours:

4 hours per week (Lecture, exercise and project)

Registration:

see TUMonline "Course Criteria & Registration"

Objective (Expected Results of Study and Acquired Competences):

1. Understanding of different sensor concepts
2. Understand basic concept of atmospheric modeling
3. Capable of applying statistical data analyze tools to the scientific data
4. Analyzing the data in the time and frequency domain

Content:

1. Basics: properties of the atmosphere (earth, sun, other planets)
2. Sensing methodologies and instrumentations:    
   1. Solar-tracking/open path Fourier Transform Spectrometer,
   2. Tunable Diode Laser Spectrometry
   3. Grating spectrometer
   4. LiDAR
   5. Ceilometer
   6. Direct/wavelength modulation spectroscopy
   7. Cavity ring down spectroscopy
   8. Laser photoacoustic spectroscopy
   9. Greenhouse gas satellites
3. Data analysis
   1. linear regression methods: OLS, MA, SMA, York linear fit, Principal component analysis, etc.   
   2. statistical assessment: bootstrap, Student's t-test, Chi-squared test, etc.    
   3. Interpolation: e.g. semantic kriging
   4. Data fusion
4. Atmospheric modeling:
   1. box and column model
   2. Markov chains
   3. Eulerian and Lagrangian model
   4. Inverse modeling
5. Machine learning for environmental applications

Language:

English

Previous knowledge expexted:

Basic knowledge in electrical engineering, physics and optics is desirable
Basic knowledge in a programming language like Matlab, R, C++, Python, etc.