Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Applications of Multivariate Statistical Methods in Meteorology and Climatology - NMET512

Title:	Využití vícerozměrných statistických metod v meteorol. a klimat.
Guaranteed by:	Department of Atmospheric Physics (32-KFA)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2016
Semester:	summer
E-Credits:	3
Hours per week, examination:	summer s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	prof. RNDr. Radan Huth, DrSc.
Class:	DS, meteorologie a klimatologie
Classification:	Physics > Meteorology and Climatology

Opinion survey results Examination dates SS schedule Noticeboard

Annotation -

Last update: T_KMOP (29.04.2004)

Introduction to multivariate statistical methods currently used in meteorology and climatology, with emphasis on their practical applications

Course completion requirements - Czech

Last update: doc. Mgr. Jiří Mikšovský, Ph.D. (19.10.2017)

Ústní zkouška v rozsahu témat daných sylabem.

Literature - Czech

Last update: doc. Mgr. Jiří Mikšovský, Ph.D. (13.05.2020)

Dle doporučení vyučujícího.

Requirements to the exam - Czech

Last update: doc. Mgr. Jiří Mikšovský, Ph.D. (19.10.2017)

Ústní zkouška v rozsahu témat daných sylabem.

Syllabus -

Last update: T_KMOP (29.04.2004)

1. Introduction

need to analyze huge datasets

examples of tasks leading to the use of multivariate methods: classification, regionalization, reduction of datasets, removal of linear dependency, ...

2. Multiple linear regression

basic notions and definitions

methods of selection of predictors

3. Principal component analysis

definitions

derivation, basic properties

selection of the input data matrix (modes of decomposition)

selection of similarity matrix

concept of simple structure, rotation, selection of the number of components

interpretation of results

Buell's map sequences

4. Cluster analysis

basic definitions

(dis)similarity measures

algorithms, their properties

fuzzy methods

principal component analysis as a cluster analysis method

5. Canonical correlation analysis

basic derivation

interpretation of results

relations among multiple regression, principal component analysis, and canonical correlation analysis

6. Examples of treating specific tasks

definition of climate areas (regionalization)

teleconnections (modes of variability) in geopotential height fields - incl. comparison with correlation method

classification of circulation patterns

classification of air masses (weather types)

relationships between circulation and surface climate elements (temperature, precipitation)

statistical downscaling

identification and removal of ?non-meteorological" signal

Model Output Statistics