1. komponenta

Lecture type	Total
Lectures	30
Exercises	30

* Load is given in academic hour (1 academic hour = 45 minutes)

Sources of climatological data. Climatological bulletins and atlases. Climatological data on Internet. Nature of climatological series: random and non-random part. Annual cycle, ways of computation and properties. Trend and long-term oscillations. Calculation of climatological normals for real data. Stationary stochastic processes, ergodicity, estimation of the autocorrelation function. Pseudo-random numbers. White noise, general linear process, AR(1), AR(2) processes, higher-order models. Fitting the model to measured data. Simulations of climatological time series.
Exercises comprise the processing and analysis of real time series. This includes writing programs (in Matlab) for analysis and simulation of time series, as well as interpreting the results.


LEARNING OUTCOMES:
To be able to:
list and describe the sources of climate data
to explain the nature of climatological time series and identify various time scales
calculate annual cycle and trend
define and explain the notion of stochastic process and stationarity
define white noise and general linear process
define models of autoregression and moving average and interpret their properties in climatological context
fit theoretical stochastic model to real time series and interpret the obtained results.

Conditions for Obtaining a Signature:
Regular class attendance and submission of all homework assignments, revised as necessary.

Method of Knowledge Assessment and Examination:
Project task and oral exam.
In the project task, the student must conduct a comprehensive analysis of a given climatological time series (or multiple series). This includes calculating and removing the annual cycle, assessing the stationarity of the residual series, determining the optimal order and fitting an autoregressive model, evaluating the residuals, conducting Monte Carlo simulations, and similar tasks. The student must, on it's own, write a computer program (in Python) and systematically describe obtained results in a report. After satisfactorily completing the project task, the student may take the oral exam, which consists of discussing the project task and answering two to three questions covering the entire syllabus.
The final grade is based on the completion of the project task and performance in the oral exam.

1. Time Series Analysis: Forecasting and Control, Box G.E.P., G.M. Jenkins, G.C. Reinsel, Wiley, New Jersey, 2008.
2. Applied Climatology, R.D. Thompson, A. Perry, Routledge, London,, 1997.
3. Python Data Science Handbook, J. VanderPlas, O'Reilly, Boston, 2017.

Enrollment :
Passed : Climatology I
Attended : Climatology II

Mandatory course - Regular study - Meteorology and Physical Oceanography